Application of high performance liquid chromatography and...

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Application of High Performance Liquid Chromatography

and Mass Spectrometry to the Analysis of the Structure of

Protein Kinase ErbB2 and Therapeutic Applications

A dissertation presented

by

Yue Zhang

To

The Department of Chemistry and Chemical Biology

In partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the field of

Chemistry

Northeastern University

Boston, Massachusetts

August 15, 2013

1

Application of High Performance Liquid Chromatography and

Mass Spectrometry to the Analysis of the Structure of Protein

Kinase ErbB2 and Therapeutic Applications

by

Yue Zhang

ABSTRACT OF DISSERTATION

Submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy in Chemistry

in the College of Science of

Northeastern University

August 15, 2013

2

ABSTRACT

ErbB2/Her2 encodes human epidermal growth factor receptor 2, which is a member of the

epidermal growth factor receptor family. The amplification of ErbB2 has been observed in

approximately 25% of all breast cancer patients and associated with poor diagnosis and

malignant metastatic disease forms. Significant efforts have been made in the genomic and

proteomic characterization of ErbB2-positive breast cancer. Approximately 20 genes located

around ErbB2 on chromosome 17, which is referred to as the ErbB2 amplicon, have been

observed to be co-overexpressed with ErbB2 in breast cancer.

Monoclonal antibodies (mAbs) have gained great interest in the treatment of cancer during the

past decade. More than 10 mAb drugs have been approved by the U.S. Food and Drug

Administration for cancer therapy since the first marketing approval for Herceptin in 1998.

Currently, there are three approved mAb-based drugs for the treatment of ErbB2-positive breast

cancer: Trastuzumab (Herceptin), pertuzumab (Perjeta), and Trastuzumab emtansine (Kadcyla).

In chapter 1, several types of ErbB2-positive breast cancer are overviewed in the first place. The

common techniques for the genomic and proteomic study of breast cancer are discussed,

including RNA-Sequencing, chromatography, and mass spectrometry. Moreover, currently

developed biopharmaceuticals for the treatment of ErbB2-positive breast cancer are described.

Mass spectrometry-based proteomics approach for the characterization and pharmacokinetics

(PK) of biopharmaceuticals is further discussed in detail.

In chapter 2, we selected three breast cancer cell lines (SKBR3, SUM149, and SUM190) with

different oncogene expression levels involved in ERBB2 and EGFR signaling pathways as a

model system for the evaluation of the selective integration of subsets of transcriptomic and

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proteomic data. We used RNA-Sequencing data to identify those oncogenes with significant

transcript levels in these cell lines and interrogated the corresponding proteomics data sets for

proteins with significant interaction values with these oncogenes. We focused on four main

oncogenes for pathway analysis, that is, ERBB2, EGFR, MYC, and GRB2. We used several

bioinformatics sites to identify pathways that contained the four main oncogenes and had good

coverage in the transcriptomic and proteomic data sets as well as a significant number of

oncogene interactors. The four pathways identified were ERBB signaling, EGFR1 signaling,

integrin outside-in signaling, and validated targets of c-Myc transcriptional activation. The

greater dynamic range of the RNA-Sequencing values allowed the use of transcript ratios to

correlate observed protein values with the relative levels of the ERBB2 and EGFR transcripts in

each of the four pathways. This provided us with potential proteomic signatures for the SUM149

and 190 cell lines.

In chapter 3, we investigated the ErbB2 isoforms in SKBR3 cell line. Two ErbB2 isoforms were

identified in SKBR3 cell lysate by the combination of immunoprecipitation and liquid

chromatography–tandem mass spectrometry (LC-MS/MS). The two ErbB2 isoforms have 1240

and 1255 amino acid residues, respectively. The proteomics results show agreement with RNA-

Sequencing results.

In chapter 4, two bi-specific monoclonal antibodies (Zybody) were characterized by LC-MS-

based approaches. Full-sequence coverage was achieved for both Zybody candidates using multi-

enzyme digestion strategies. The stability of bi-specific binding sites in two molecules were

accessed and compared, and a better candidate was selected for further pharmacokinetics (PK)

study. Besides, common modifications were studied using different LC-MS platforms.

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In chapter 5, analytical platforms using LC-MS were developed to quantitate Zybodies in mouse

serum. Two different enrichment techniques were used: a specific approach for Zybody (anti-

Zybody immunoprecipitation) and a general method for any conventional mAb (protein A

enrichment). In general, the results from two different enrichment methods correlated with each

other well and produced good agreement with the enzyme-linked immunosorbent assay (ELISA)

approach. This can confirm the desired functionality of the anti-Zybody provided by our

collaborator. Two LC-MS platforms were applied for quantitation: either using the intensity of

precursor ions for quantitation in nanoflow LC or using the MRM in industry-standard LC-MS

platform. In both methods, the half-life of Zybody in mouse serum was determined as

approximately 48 hours. Besides, most tryptic peptides as well as their major modified forms can

be quantified using our platform. We have demonstrated that LC-MS is an accurate and high-

throughput method for PK and metabolism study of mAbs.

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ACKNOWLEDGEMENTS

The completion of this thesis work could not be fulfilled without the support and help of the

following people. I can never express my respect and appreciation to everyone in word.

Above all, I would like to express my deepest gratitude to my advisor, Prof. William S. Hancock.

I am grateful to have the opportunity to work in his group. Prof. Hancock is the best supervisor

as well as a great mentor whom any graduate student would be lucky to have: he is wise, cheery,

inspirational, and always supportive. Those periods when the chromosome team worked together

would always be my best memories in graduate school.

I also gratefully acknowledge my supervisor, Dr. Shiaw-Lin (Billy) Wu. I thank Dr. Wu for

leading me into the world of bioanalysis, to which I will devote my scientific career. I have

benefited and will always benefit from Dr. Wu’s profound knowledge of science and mass

spectrometry.

My special acknowledgment goes to Prof. Barry L. Karger. I appreciate Prof. Karger for

providing the cutting-edge mass spectrometers and superb research environment in the Barnett

Institute of Chemical and Biological Analysis.

I am grateful to my committee members: Prof. Penny Beuning, Prof. George O'Doherty, and Prof.

Michael Pollastri. I really appreciate the precious time they dedicated and the wise suggestions

they gave me.

I could not submit this work without thanking the members of the Barnett Institute. It has been a

great pleasure to study and work with all my friends and colleagues in the institute. I would like

to thank Dr. Shujia (Daniel), Dr. Jim Glick, Dr. Marina Hincapie, Dai, Dr. Jing (Susan) Fang, Dr.

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Haitao Jiang, Shan Jiang, Dr. Janet Law, Dr. Chen Li, Siyang (Peter) Li, Siyuan (Serah) Liu, Dr.

Suli Liu, Dr. Zhenke (Jack) Liu, Dr. Qiaozhen (Cheryl) Lu, Dr. Wenqin Ni, Dr. Dongdong Wang,

Xianzhe Wang, and Dr. Yi Wang. Thank you for all the support, encouragement, and kind help.

I am thankful to the staff in the Barnett Institute and the Department of Chemistry and Chemical

Biology. I appreciate all of their help in the past five years.

I dedicate this work to my wonderful family and friends. Without their support, the completion

of this degree would not have been possible. I am always extremely grateful for the love and

encouragement they gave me.

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TABLE OF CONTENTS

ABSTRACT OF DISSERTATION ...............................................................................................1

ACKNOWLEDGEMENTS ..........................................................................................................5

TABLE OF CONTENTS ...............................................................................................................7

LIST OF FIGURES .....................................................................................................................14

LIST OF TABLES ........................................................................................................................17

LIST OF ABBREVIATIONS ......................................................................................................19

Chapter 1 Overview of the Application of HPLC and MS to the Analysis of the Structure of

Protein Kinase ERBB2 and Therapeutic Applications .............................................................23

1.1 Abstract ...........................................................................................................................24

1.2 ErbB2 positive breast cancer and inflammatory breast cancer (IBC) .............................24

1.2.1 Types of breast cancer ............................................................................................. 24

1.2.2 Inflammatory breast cancer ..................................................................................... 25

1.3 C-HPP initiative and genomic analysis of ErbB2 positive breast cancer ........................26

1.3.1 Introduction to c-HPP initiative .............................................................................. 26

1.3.2 Chromosome 17 parts list ....................................................................................... 28

1.3.2.1 Chromosome 17 and its association with breast cancer ...................................... 28

1.3.2.2 Oncogenes located on chromosome 17 ............................................................... 29

1.3.3 Genomic characteristics of ERBB2/ErbB2 positive breast cancer ......................... 29

1.3.3.1 ErbB2 amplicon ................................................................................................... 30

1.3.3.2 Technologies for transcriptome profiling ............................................................ 31

1.3.3.3 Protein isoforms .................................................................................................. 31

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1.3.4 Pharmaceuticals for the treatment of ErbB2 positive breast caner ......................... 33

1.3.4.1 Marketed monoclonal antibody drugs ................................................................. 33

1.3.4.2 Non-canonical therapeutic antibodies currently in clinical trials ........................ 35

1.3.4.3 Small molecules drugs ........................................................................................ 36

1.4 Proteomics .......................................................................................................................37

1.4.1 Protein enrichment methods ................................................................................... 37

1.4.1.1 Immunoprecipitation ........................................................................................... 37

1.4.1.2 Affinity chromatography ..................................................................................... 38

1.4.2 Protein and peptide separation techniques .............................................................. 39

1.4.2.1 Gel electrophoresis .............................................................................................. 39

1.4.2.2 Reversed phase liquid chromatography .............................................................. 40

1.4.2.3 Multidimensional liquid chromatography ........................................................... 41

1.4.2.4 Capillary electrophoresis ..................................................................................... 42

1.4.3 Mass-spectrometry based proteomic study ............................................................. 43

1.4.4 Mass spectrometer .................................................................................................. 45

1.4.4.1 Ionization methods .............................................................................................. 45

1.4.4.2 Mass analyzer ...................................................................................................... 46

1.4.4.3 Tandem mass spectrometry ................................................................................. 50

1.4.4.3.1 Fragmentation methods in tandem mass spectrometry ............................................50

1.4.4.3.2 Multiple reaction monitoring (MRM) ......................................................................53

1.4.4.4 Quantitative proteomic analysis by mass spectrometry ...................................... 54

1.5 Proteomics analysis of biopharmaceuticals .....................................................................58

1.5.1 Overview of post-translational modifications ......................................................... 59

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1.5.2 Common chemical modifications ........................................................................... 60

1.5.3 Di-sulfide bond linkages ......................................................................................... 63

1.5.4 Glycosylation .......................................................................................................... 64

1.5.5 Pharmacokinetics and pharmacodynamics (PK/PD) study of therapeutic proteins 65

1.6 Conclusions .....................................................................................................................67

1.7 References .......................................................................................................................67

Chapter 2 Genome Wide Proteomics of ERBB2 and EGFR and Other Oncogenic Pathways

in Inflammatory Breast Cancer ..................................................................................................82

2.1 Abstract ...........................................................................................................................83

2.2 Introduction .....................................................................................................................84

2.3 Materials and methods ....................................................................................................85

2.3.1 Cell lines, cell lysis, and in-gel digestion ............................................................... 85

2.3.2 LTQ-FT MS ............................................................................................................ 87

2.3.3 Protein identification ............................................................................................... 87

2.3.4 RNA-Seq measurement .......................................................................................... 88

2.4 Results and discussion .....................................................................................................89

2.4.1 Analysis of cell lines SKBR3, SUM149, and SUM190 ......................................... 89

2.4.2 Characterization of EGFR and ERBB2 .................................................................. 90

2.4.3 Protein observations with RNA-Seq data and expressed in a genome wide format

(chromosomes) ....................................................................................................................... 91

2.4.4 Use of RNA-Seq data to explore ERBB2 signaling pathways ............................... 92

2.4.5 Proteomic analysis of SKBR3, SUM149, and 190 cell lines .................................. 98

2.4.6 Comparison of proteomic observations between cell lines ................................... 101

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2.4.7 Mapping of oncogene interactions with proteomic observations ......................... 101

2.4.8 Identification of pathways that contain ERBB2, EGFR, GRB2 and MYC

interactors ............................................................................................................................. 102

2.5 Conclusion ..................................................................................................................... 111

2.6 Acknowledgement ......................................................................................................... 112

2.7 Supplementary information ........................................................................................... 112

2.8 References ..................................................................................................................... 119

Chapter 3 Identification of ErbB2 Isoforms from SKBR3 Cell Lysate by

Immunoprecipitation and Liquid Chromatography - Tandem Mass Spectrometry (LC-

MS/MS) .......................................................................................................................................124

3.1 Abstract .........................................................................................................................125

3.2 Introduction ...................................................................................................................125

3.3 Experiments ...................................................................................................................127

3.3.1 Material ................................................................................................................. 127

3.3.2 ErbB2 immunoprecipitation (IP) with anti-ErbB2 antibodies .............................. 128

3.3.3 SDS-PAGE ............................................................................................................ 129

3.3.4 In gel tryptic digestion .......................................................................................... 129

3.3.5 LC-MS analysis .................................................................................................... 130

3.3.6 Data analysis ......................................................................................................... 130

3.3.7 RNA-Seq Measurement ........................................................................................ 131

3.4 Results ...........................................................................................................................131

3.4.1 SDS-PAGE gel image ........................................................................................... 132

3.4.2 Efficiency of ErbB2 immunoprecipitation ............................................................ 133

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3.4.3 Overall ErbB2 coverage ........................................................................................ 136

3.4.4 Identification of different ErbB2 isoforms ........................................................... 136

3.5 Conclusion .....................................................................................................................141

3.6 References .....................................................................................................................141

Chapter 4 Structural Characterization of Two Zybody Candidates by Liquid

Chromatography Coupled with Online Tandem Mass Spectrometry (LC-MS) Analysis ...143

4.1 Abstract .........................................................................................................................144

4.2 Introduction ...................................................................................................................145

4.3 Experiments ...................................................................................................................147

4.3.1 Materials ............................................................................................................... 147

4.3.2 In solution enzyme digestion ................................................................................ 147

4.3.3 SDS-PAGE and in gel digestion ........................................................................... 147

4.3.4 LC-MS analysis by LTQ-Orbitrap ........................................................................ 148

4.3.5 LC-MS analysis by Q-TOF ................................................................................... 149

4.4 Results and discussion ...................................................................................................149

4.4.1 Primary structure identification ............................................................................ 149

4.4.2 C-terminal truncation ............................................................................................ 152

4.4.3 Disulfide bond linkages ........................................................................................ 155

4.4.4 Chemical modifications ........................................................................................ 159

4.4.4.1 Pyroglutamic acid (PyroE) at the N-terminus of heavy chain .......................... 159

4.4.4.2 Oxidation ........................................................................................................... 162

4.4.4.3 Deamidation ...................................................................................................... 164

4.4.4.4 Isomerization of aspartic acid ........................................................................... 167

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4.4.5 Identification of glycopeptides ............................................................................. 169

4.5 Conclusion .....................................................................................................................171

4.6 References .....................................................................................................................172

Chapter 5 Pharmacokinetics and Metabolism Study of Zybodies by Liquid

Chromatography Coupled with Mass Spectrometry (LC-MS) .............................................175

5.1 Abstract .........................................................................................................................176

5.2 Introduction ...................................................................................................................177

5.3 Experiments ...................................................................................................................179

5.3.1 Materials ............................................................................................................... 179

5.3.2 Preparation of spike-in samples ............................................................................ 179

5.3.3 Antibody (anti-Zybody) enrichment ..................................................................... 180

5.3.4 Protein A enrichment............................................................................................. 180

5.3.5 SDS-PAGE and in gel digestion ........................................................................... 181

5.3.6 LC-MS analysis .................................................................................................... 181

5.3.7 QQQ ...................................................................................................................... 182

5.4 Results and discussion ...................................................................................................182

5.4.1 Protein A enrichment............................................................................................. 183

5.4.2 Enrichment by antibody immunoprecipitation ..................................................... 185

5.4.3 Quantitation by data dependent mode................................................................... 186

5.4.4 Optimization of LC condition in Agilent 1200 series ........................................... 188

5.4.5 Optimization of collision energy in MRM ............................................................ 189

5.4.6 Candidate 2 absolution quantitation by MRM ...................................................... 192

5.4.7 Comparison of two enrichment methods .............................................................. 194

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5.5 Conclusion .....................................................................................................................194

5.6 Supplementary Table S5-1 ............................................................................................195

5.7 References .....................................................................................................................196

Chapter 6 Conclusions and Future Work ................................................................................197

Copyright Clearance ..................................................................................................................199

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LIST OF FIGURES

Figure 1-1: Genomic, transcriptomic and protein information for the set of genes present in

selected regions of chromosomes 13 and 17......................................................................... 27

Figure 1-2: Transcriptomic and proteomic expression of ErbB2 amplicon in two ErbB2 positive

breast cancer cell lines .......................................................................................................... 30

Figure 1-3: RNA processing increases protein variation through basic transcription or alternative

splicing. ................................................................................................................................. 32

Figure 1-4: Schematic diagram of a Zybody molecule. ................................................................ 36

Figure 1-5: Three-dimensional schematic of the Orbitrap cell. .................................................... 49

Figure 1-6: Construction details of the Q Exactive. ..................................................................... 50

Figure 1-7: Bond cleavages in MS/MS fragmentation. ................................................................ 51

Figure 1-8: MRM analysis in a QQQ mass spectrometer. ............................................................ 53

Figure 1-9: Absolute quantification of proteins and phosphoproteins using the AQUA strategy. 59

Figure 2-1: Annotation of KEGG ERBB2 signaling pathways with transcriptomic data. ........... 96

Figure 2-2: A composite of SUM149 (A) and SUM190 (B) transcriptomic, proteomic, and

interaction data for significant oncogenes observed in SUM149 and SUM190. .................. 97

Figure 2-3: Ratio of number of protein observations per number of genes for each chromosome

............................................................................................................................................. 100

Figure 3-1: SDS-PAGE gel images of eluents and flow-through from ErbB2

immunoprecipitation using different antibodies ................................................................. 133

Figure 3-2: Protein coverage of the two ErbB2 isoforms identified. .......................................... 138

Figure 3-3: Comparison of the primary sequences of the two ErbB2 isoforms identified. ........ 139

Figure 3-4: Identification of the unique peptide of ENSP00000446466 .................................... 140

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Figure 4-1: Structure of Zybody molecule used in this study ..................................................... 146

Figure 4-2: Identification of intact C-terminal peptide (T42H) in two Zybodies ....................... 151

Figure 4-3: Examples of identification of C-terminal truncated peptides in two Zybody molecules.

............................................................................................................................................. 153

Figure 4-4: Identification of one disulfide bond (T2H-T11H) by mass spectrometry ................ 158

Figure 4-5: Identification of N-terminal peptide on heavy chain and formation of pyro-glutamic

acid on N-terminus of heavy chain of Candidate 1 ............................................................. 160

Figure 4-6: Relative quantification of extent of pyro-E formation at N-terminus of heavy chain

............................................................................................................................................. 161

Figure 4-7: Identification of methionine oxidation of T21H of Candidate 1 ............................. 163

Figure 4-8: Identification of succinimide intermediate formed during asparagine deamidation for

two Zybody molecules ........................................................................................................ 166

Figure 4-9: Identification of succinimide intermediate formed during aspartic acid isomerization

for two Zybody molecules .................................................................................................. 168

Figure 4-10: LC-MS analysis of glycopeptides of Candidate 1 ................................................. 170

Figure 4-11: Glycopeptides distribution comparison between two Zybody molecules. ............. 171

Figure 5-1: Workflow of IgG enrichment and LC-MS analysis ................................................. 183

Figure 5-2: Gel image of Zybody enrichment by protein A beads. ............................................ 184

Figure 5-3: Gel image of Zybody enrichment by antibody immunoprecipitation. ..................... 186

Figure 5-4: Quantifications of a representative peptide on the heavy chain (T1H) of Candidate 2

by data dependent mode on LTQ-Orbitrap. ........................................................................ 187

Figure 5-5: Optimization of collision energy in CID for T1H. 30.0 eV was selected based on the

fragmentation of precursor ion and the intensity of product ions. ...................................... 190

16

Figure 5-6: Representative of quantitation results of Zybodies. ................................................. 193

17

LIST OF TABLES

Table 2-1: List of oncogenes associated with breast cancer with associated proteomic and

transcriptomic data ................................................................................................................ 93

Table 2-2: ErbB receptor signaling network with RNA-Seq ratios (SUM149 vs. SUM190) ...... 98

Table 2-3: EGFR1 signaling from NCI ....................................................................................... 104

Table 2-4: Integrin outside-in signaling ..................................................................................... 107

Table 2-5: Validated targets of C-MYC transcriptional activation (a sub-pathway of c-MYC

pathway) .............................................................................................................................. 108

Table 2-6: p53 pathway (a sub-pathway of Class I PI3K signaling events mediated by Akt) .... 110

Table 3-1: Efficiency of ErbB2 enrichment ................................................................................ 135

Table 3-2: Protein coding splice variants of ErbB2 .................................................................... 137

Table 3-3: ErbB2 variants identified from RNA-Sequencing data of SKBR3 cell lysate .......... 140

Table 4-1: Summary of identified C-terminal truncated peptides in two Zybody molecules with

the corresponding intensity and percentages ...................................................................... 154

Table 4-2: Disulfide bond linkages in two Zybody molecules ................................................... 156

Table 4-3: Comparison of the percentage of pyroglutamic acid formation on N-terminus of heavy

chain for two Zybody molecules ......................................................................................... 161

Table 4-4: Comparison of the percentage of oxidation for two Zybody molecules ................... 164

Table 4-5: Comparison of the percentage of succinimide intermediate formed during asparagine

deamidation for two Zybody molecules ............................................................................. 166

Table 4-6: Comparison of the percentage of succinimide intermediate formed during aspartic

acid isomerization for two Zybody molecules .................................................................... 168

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Table 5-1: Concentration of Candidate 1 in mouse serum at three time points .......................... 188

Table 5-2: Optimization of gradient flow rate and loading flow rate ......................................... 189

Table 5-3: MRM method for monitoring all tryptic peptides on the heavy chain of Candidate 2

............................................................................................................................................. 190

Table 5-4: Comparison of the concentrations of Candidate 2 determined by two enrichment

methods ............................................................................................................................... 194

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LIST OF ABBREVIATIONS

2DE two-dimensional gel electrophoresis

CE capillary electrophoresis

Ab antibody

ACN acetonitrile

ADCC antibody-dependent cell-mediated cytotoxicity

Asn asparagine

Asp aspartic acid

CDC complement-dependent cytotoxicity

CDR complementarity determining region

c-HPP chromosome centric human proteome project

CID collision induced dissociation

CV coefficient of variation

Cys cysteine

DTT dithiothreitol

ECD electron capture dissociation

ELISA enzyme-linked immunosorbent assay

ER estrogen receptor

ErbB2, Her2 Receptor tyrosine-protein kinase erbB-2

ESI electrospray ionization

ETD eletron transfer dissociation

F fucose

Fab fragment antigen-binding

Fc fragment crystallizable

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FDA Food and Drug Administration

FTICR fourier transform ion cyclotron resonance

Gal galactose

GlcNAc N-acetylglucosamine

Gln glutamine

Glu glutamic acid

GPMDB Global Proteome Machine Database

HCD high-energy collision-induced dissociation

HILIC hydrophilic-interaction chromatography

HPLC high performance liquid chromatography

HPP human proteome project

IBC inflammatory breast cancer

IEF isoelectric focusing

IEX ion exchange

IgG immunoglobulin G

IMAC immobilised metal affinity chromatography

IsoAsp isoaspartic acid

ITRAQ isobaric tag for relative and absolute quantitation

kDa kilodalton

LC liquid chromoatography

LC-MS liquid chromatography mass spectrometry

LC-MS/MS liquid chromatography with tandem mass spectrometry

LIT linear ion trap

LTQ Linear Trap Quadrupole

m/z mass to charge ratio

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mAb monoclonal antibody

MALDI matrix assisted laser desorption ionization

Man mannose

MDLC Multi-dimensional liquid chromatography

Met methionine

MRD molecular recognition domain

MRM multiple reaction monitoring

MS mass spectrometry

MS/MS, MS2 tandem mass spectrometry

MudPIT multidimensional protein identification technology

PD pharmacodynamics

PK pharmacokinetics

PR progesterone receptor

PTM posttranslational modification

Pyro-Glu pyroglutamic acid

Q-TOF quadrupole coupled with time-of-flight

RF radio frequency

RNA-Seq RNA sequencing

RP reversed phase

RPKM Reads per kilo base per million

RSD relative standard deviation

SA sialic acid

SDS-PAGE sodium dodecyl sulfate polyacrylamide gel electrophoresis

SEC size exclusion chromatography

SILAC stable isotopic labeling of amino acids in cell culture

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SIM selected ion monitoring

SRM selected reation monitoring

TOF time-of-flight

Trp tryptophan

UPLC ultra performance liquid chromoatography

23

Chapter 1 Overview of the Application of HPLC and MS

to the Analysis of the Structure of Protein Kinase ERBB2

and Therapeutic Applications

24

1.1 Abstract

ErbB2/Her2 encodes for human epidermal growth factor receptor 2, which is a member of the

epidermal growth factor receptor family. Amplification of ErbB2 has been observed in about

25% of all breast cancer patients and associated with poor diagnosis and malignant metastatic

disease forms. Significant efforts have been made in the genomic and proteomic characterization

of ErbB2 positive breast cancer. About twenty genes located around ErbB2 on chromosome 17,

which is referred to as the ErbB2 amplicon, have been observed to be co-overexpressed with

ErbB2 in breast cancer.

In this chapter, several types of breast cancer are dicussed, followed by the introduction of a

special type of breast cancer: inflammatory breast cancer. Common techniques for genomic and

proteomic study of breast cancer are discussed, including RNA-Sequencing, chromatography,

and mass spectrometry. Moreover, currently developed biopharmaceuticals for the treatment of

ErbB2 positive breast cancer are described. Mass spectrometry-based proteomics approaches for

the characterization of biopharmaceuticals are further discussed in details.

1.2 ErbB2 positive breast cancer and inflammatory breast cancer (IBC)

1.2.1 Types of breast cancer

Breast cancer is one of the most common cancers around the world, and it is the most prevalent

cancer only after lung cancer in the US.1 In 2012, there were 290,170 women in total diagnosed

with breast cancer. This number constitutes one third of all new women cancer patients.2

25

Invasive breast cancer contributes to an estimated 226,870 new cases in US in 2012, including

2,190 new cases in male patients 2. In situ breast cancer accounts for 63,300 new breast cancer

cases in 2012 apart from invasive breast cancer, and one of the most common in situ breast

cancer is ductal carcinoma in situ (DCIS).3 It has been reported that estrogen receptor (ER)

positive cancer occurs in more than 70% DCIS patients, and the overexpression of ErbB2

accounts for half of DCIS cases.4 BRCA1 and BRCA2 mutations have also been reported to be

closely associated to DCIS.4-5

1.2.2 Inflammatory breast cancer

Inflammatory breast cancer (IBC) is a very rare form of breast cancer with approximately less

than 5% of the cases of breast cancer being this type of breast cancer.6 It has been usually

associated with poor prognosis and rapid progression.7 It is one of the most aggressive types of

breast cancer with a five-year survival rate of about 10% even after treatments like surgery and

radiation therapy.8 The distinct symptoms of IBC usually involve redness and thickening of skin,

as well as the appearances of ‘orange skin’, which are symptoms similar to inflammation in the

breast.8

The expression of breast cancer markers can be very helpful in better prognosis for IBC patients.

It has been reported that ER and PR (progesterone receptor) are present in fewer IBC patients

compared to other types of breast cancer.9-10 In addition, ERBB2 overexpression and TP53

mutation has been reported to be more frequently occurring in IBC compared to non-

inflammatory tumors.11

26

Although IBC has been considered to be a heterogeneous disease,12 the studies on gene

mechanism of cancer development and progression have shown distinguishing characteristics of

IBC.13 RhoC GTPase has been reported to be a transforming oncogene in human mammary

epithelial cells. Overexpression of RhoC GTPase accounted for higher cell mobility and

invasiveness as well as tumor formations, which are very common in IBC cell lines.14 EGFR,

another gene highly-associated with breast cancer, was reported to be more frequently deleted in

IBC compared to non-IBC in a transcriptomic study.15 In addition, E-cadherin has been shown to

be related to the ‘inflammatory signature’ in IBC; under-expression of E-cadherin will result in

increased invasiveness and high metastatic potential.13 C-Met and PI3K have also been shown to

be overexpressed in IBC in another immunohistochemical study, suggesting that c-Met might be

activated through PI3K signaling.16

1.3 C-HPP initiative and genomic analysis of ErbB2 positive breast cancer

1.3.1 Introduction to c-HPP initiative

The Human Proteome Project (HPP), launched at the 2010 HUPO World Congress of Proteomics,

aims at the characterization of at least one protein for each human genome coded gene. The

chromosome-based HPP (c-HPP), which is one of the wide-ranging projects under HPP, is

currently led by Dr. William S. Hancock and Dr. Young-Ki Paik.17 The c-HPP will integrate

proteomic data into the order of chromosome locations of encoded genes, which will allow us to

improve understanding of proteomics data sets, therefore to explore the relationship between

gene location and expression and observe disease-related amplicons.

27

One important goal of c-HPP is to share proteomic data sets from different samples for a

chromosome centric display as shown in Figure 1-1.18 In this figure, each gene has its following

panel showing its protein evidence from Uniprot (PE), mass spectrometry–based protein

identification quality in GPMDB (Mq), availability of antibody (Ab), and post-translational

modifications such as phosphorylation (Ph), acetylation (Ac), and glycosylation (Gl). There are

also columns for Disease relationship (Di) and transcriptomic information for each gene. This

traffic light map offers a straightforward view of integration of various proteomic data sets into

encoded genes in the order of chromosome locations.

Figure 1-1: Genomic, transcriptomic and protein information for the set of genes present in

selected regions of chromosomes 13 and 17.

Reprinted by permission from Macmillan Publishers Ltd: Nature Biotechnology (Paik, Y.-K., et

al. The Chromosome-Centric Human Proteome Project for cataloging proteins encoded in the

genome. Nature Biotech 2012, 30(3), 221-223), 18 copyright (2012).

Up to now, twenty-four international teams have chosen their chromosomes as part of C-HPP

initiative based on their special interests in one or more particular disease. Dr. Hancock’s

research group has chosen chromosome 17 to improve understanding of breast cancer. We have

been working on the chromosome 17 parts list, as well as a genomic- and proteomic-based breast

28

cancer study.

C-HPP is composed of a large scale of research modules as described in the following. Shotgun

and targeted mass spectrometry-based proteomic analysis, when integrated with PeptideAtlas can

prove protein and peptide identification. Molecular biology and biochemistry techniques offer an

antibody factory where monoclonal or polyclonal antibodies can be selected to enrich the

uncharacterized proteins. Bioinformatics allow us to investigate signaling changes upon the

occurrence of disease. Clinical studies provide various sample banks for disease analysis.

Genomics and transcriptomics techniques offer RNA-Sequencing (RNA-Seq) data, alternative

splicing, and information about coding SNPs (cSNPs). With these integrative technologies, it is

possible to have a better view of the relationship between genomic, transcriptomic and proteomic

measurements and phenotypes.18 To integrate and update all this information from all of the

international c-HPP groups, an open-source data integration and analysis software has been

developed.3 This platform facilitates the search of updated information in assembled

international biological databases and housing proteomic data sets.

1.3.2 Chromosome 17 parts list

1.3.2.1 Chromosome 17 and its association with breast cancer

Chromosome 17 has some unusual properties including second highest gene densities (16.2

genes per Mb) in all human chromosomes and relatively more protein-coding genes19. The

sequencing of chromosome 17 was finished in 1996 as part of the Human Genome Project with a

finished sequence of 78,839,971 bases.19

29

Chromosome 17 has been observed to be closely associated with many types of cancer including

gastric cancer, ovarian cancer, prostate cancer, etc.20-22 Abnormalities in chromosome 17 are

frequently observed in breast cancer, including whole chromosome anomalies, gene-copy-

number anomalies, allelic losses, and structural rearrangements.23 It has been reported that whole

chromosome 17 copy-number changes occur in more than 90% of breast tumors.23

Transcriptome analysis has identified several regions on chromosome 17 (17p13, 17p11, 17q21,

17q23, and 17q25) as regions of increased tumor expression in breast tumor. 24

1.3.2.2 Oncogenes located on chromosome 17

Many oncogenes are located on chromosome 17, including classic breast cancer associated genes

such as TP53 (tumor protein p53), BRCA1 (breast cancer 1, early onset), ERBB2/ErbB2

(epidermal growth factor receptor 2), and TOP2A (topoisomerase DNA II alpha). These genes

are usually deleted (TP53 and BRCA1) or amplified (ERBB2 and TOP2A) in breast cancer. 23

1.3.3 Genomic characteristics of ERBB2/ErbB2 positive breast cancer

ERBB2 is located on chromosome 17 cytoband 17q12. In the past ten years, several genes that

are located around ErbB2 on cytobands 17q12-q21, termed as ErbB2 amplicon, have been

reported to be co-overexpressed with ErbB2 in carcinoma.25-26 A minimum region containing

about 20 genes have been identified as ErbB2 amplicon.

30

Gene RP

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TIAF1 ## ## 0 0 PNMT ## ## 0 0 PSMD3 ## ## 2 9

TRAF4 ## ## 0 0 PGAP3 ## ## 0 0 MED24 ## ## 0 0

PSMB3 ## ## 3 14 ERBB2 ## ## 46 19 NR1D1 ## ## 0 0

LASP1 ## ## 0 0 MIEN1 ## ## 0 0 CASC3 ## ## 0 0

MED1 ## ## 0 0 GRB7 ## ## 2 2 CDC6 ## ## 0 0

CDK12 ## ## 0 0 IKZF3 ## ## 0 0 RARA ## ## 0 0

PPP1R1B ## ## 0 0 ORMDL3 ## ## 0 0 TOP2A ## ## 58 0

STARD3 ## ## 0 0 GSDMB ## ## 0 0

Figure 1-2: Transcriptomic and proteomic expression of ErbB2 amplicon in two ErbB2 positive

breast cancer cell lines

The color coding used the following: green, RNA-Seq RPKM ≥ 15, spectral count ≥ 5; yellow,

RNA-Seq RPKM between 3 and 15, spectral count between 3 and 5; red, RNA-Seq RPKM

between 1 and 3, spectral count equals 1 or 2; black, no information.

1.3.3.1 ErbB2 amplicon

The ErbB2 amplicon refers to the genes located in chromosome 17 cytoband q12-21 in close

proximity to ErbB2.26 This amplicon has been greatly associated with several cancer types such

as breast, gastric, ovarian, cervical, etc.27 These genes include TIAF1, TRAF4, PSMB3, LASP1,

MED1, CDK12, PPP1R1B, STARD3, PNMT, PGAP3, ERBB2, MIEN1, GRB7, IKZF3,

ORMDL3, GSDMB, PSMD3, MED24, NR1D1, CASC3, CDC6, RARA, and TOP2A, ranked by

their locations on chromosome from the centromere to the end of q arm of chromosome 17.28

Many of these genes have been observed to co-amplify with ErbB2. Figure 1-2 gives an example

of the ErbB2 amplicon expression level in ErbB2 positive breast cancer, SKBR3 and SUM190.

31

1.3.3.2 Technologies for transcriptome profiling

“Transcriptome” refers to the whole set of quantitated transcript in a cell at specific

developmental stages including mRNAs, non-coding RNAs and small RNAs. 29 The

transcriptome has been a crucial part of the promising ‘-omics’ profiling in recent proteomics

research. 30 It has been reported that by monitoring the combination of personal ‘-omics’ profiles,

including transcriptomics, proteomic and metabolomics, various medical risks could be revealed

for a single person. 31

In recent years, RNA-Seq has greatly improved the techniques for mapping and quantifying

transcriptomes. Compared to the existing methods such as microarray and sequence-based

approaches, RNA-Seq has advantages of high-throughput, high dynamic range, high resolution

of base-pair level, low background noise, low amount of RNA required, low cost, and

differentiation of various isoforms and allelic expression.29 In spite of its shortcomings such as

requirements of cDNA synthesis and poor reproducibility in low-quantity RNA samples, RNA-

Seq is still a very powerful tool for the transcriptomic characterization and quantitation since its

establishment.32

1.3.3.3 Protein isoforms

The identification of protein isoforms coded from each human gene by mass spectrometry is one

of the most important goals for c-HPP project. “Isoform” is a recommended term by the

International Union of Pure and Applied Chemistry (IUPAC) to refer to protein forms that are

from the same gene family and have high sequence identity.33 Figure 1-3 shows the three sources

32

of different protein isoforms: single nucleotide polymorphisms (SNPs), alternative splicing, and

post-translational modifications (PTMs).

SNP refers to the difference of a single nucleotide in a DNA sequence. Although nucleic acid–

based approaches are able to provide a high throughput analysis of SNPs, proteomics-based

methods allow the identification of amino acid-changing single-nucleotide polymorphisms

(coding SNPs, or cSNPs).

Figure 1-3: RNA processing increases protein variation through basic transcription or alternative

splicing.

This research was originally published in The Journal of Biological Chemistry. Tipton, J. D.;

Tran, J. C.; Catherman, A. D.; Ahlf, D. R., Durbin, K. R.; Kelleher, N. L. Analysis of intact

protein isoforms by mass spectrometry. Journal of Biological Chemistry, 2011, 286(29), 25451-

25458.33 Copyright (2011) the American Society for Biochemistry and Molecular Biology.

33

In alternative splicing, some exons of a gene may be included within, or excluded from, the final

processed messenger RNA (mRNA) produced from that gene. If the exons are translated, the

alternatively spliced mRNAs will encode correlated but different protein isoforms.34 Because

some of the peptides produced from different protein isoforms are identical to all forms, it is

often very difficult to differentiate and identify various isoforms by conventional bottom-up

proteomics strategy. However, top-down mass spectrometry has shown promise in the qualitative

and quantitative analysis of protein isoforms.33, 35-36 For example, Tran et al. used a four-

dimensional separation coupled with intact mass spectrometry to successfully identify many

protein isoforms on a proteomics scale, including 9 of 15 isoforms of histone H2A that have

greater than 95% sequence identity.37

1.3.4 Pharmaceuticals for the treatment of ErbB2 positive breast caner

1.3.4.1 Marketed monoclonal antibody drugs

Trastuzumab

Trastuzumab (trade name: Herceptin) is a therapeutic monoclonal antibody developed by

Genentech and was approved by FDA in 1998 for the treatment of ErbB2-overexpressed invasive

breast cancers. It is a humanized monoclonal antibody derived from murine Mab 4D5 that binds

the extracellular domain of ErbB2.38 The complementarity determining region of Herceptin,

which is derived from murine Mab 4D5, binds to the extracellular domain of ErbB2 near its

membrane portion, which therefore blocks the subsequent reactions of the intracellular tyrosine

kinase of ErbB2 upon activation.39 The mechanism of Herceptin includes several processes,

34

including physical inhibition of ErbB2 homodimerization, antibody-dependent cellular

cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC), and other possible

mechanism.40

Pertuzumab

Pertuzumab (trade name: Perjeta, also known as 2c4 or Omnitarg), a humanized monoclonal

antibody developed by Genentech, was recently approved by FDA in July, 2012 to treat patients

with ErbB2 positive metastatic breast cancer who have not received anti-ErbB2 therapy or

chemotherapy. Pertuzumab was intended to be used in combination with Trastuzumab and

docetaxel. This treatment method has been shown to be effective in increasing the progression-

free survival of patients by 6 months compared to the treatment with placebo plus Trastuzumab

plus docetaxel.41 Pertuzumab binds ErbB2 at the position close to the domain II of ErbB2, and is

therefore able to block the heterodimerization of ErbB2 and Her3,42 which have been discovered

to work as an oncogene unit to control the proliferation of breast tumor cells.43

Trastuzumab emtansine

Trastuzumab emtansine (T-DM1, trade name: Kadcyla) is a therapeutic antibody drug conjucate

developed by Roche/Genentech. It was recently approved by the FDA on February 22, 2013 for

the treatment of patients with ErbB2 positive metastatic breast cancer. T-DM1 is composed of

Trastuzumab and the cytotoxic microtubule inhibitor DM1 linked via a thioether linker.44 The

result of its phase III clinical trial showed that T-DM1 was able to prolong median progression-

35

free survival of ErbB2 positive breast cancer patients.45

1.3.4.2 Non-canonical therapeutic antibodies currently in clinical trials

Besides traditional full-length, single target mAb molecules, the development of non-canonical

mAbs becomes a promising trend in antibody-based therapeutic cancer drugs.46 These mAb-

based molecules, some of which are under clinical trials, include antibody-conjugates, bispecific

antibodies, and antibody fragments or domains.

One of the most promising non-canonical antibody techniques is the bi-specific monoclonal

antibody.47 Bi-specific mAbs are designed to target two antigens simultaneously. Although using

two mAb as cocktail therapy can accomplish a similar job, the advance of bi-specific mAbs is

able to save the cost and time of development and assessments of drug safety and efficacy by

half or even more. An example of a bi-specific ErbB2 targeted mAb is MM-111 (developed by

Merrimack Pharmaceuticals), which is a bispecific single-chain variable fragment consisting of

the antibody moieties of both human anti-ErbB2 and human anti-Her3 via a linker of modified

human serum albumin.48 MM-111 is designed to target the ErbB2/Her3 heterodimer in metastatic

ErbB2 positive cancer and it is currently under evaluation of dose-finding phase I/II study.49

There are different formats for making bispecific antibodies. Zybody is one of the most advanced

technologies for designing bispecific mAbs. Zybodies consist of specific peptides, called

molecule recognition domains, which are fused to C-termini or N-termini of heavy or light chain

of mAb. It has been observed in ELISA that fusion to each of the four positions will retain the

binding functions of the parental mAb and its target.50 The illustration of a Zybody molecule is

36

shown in Figure 1-4. The molecule recognition domains (MRDs) are fused to the N-terminus or

C-terminus of immunoglobulin heavy (H) and light (L) chains.50 Currently Zybodies are able to

bind two (but up to five) different antigens from EGFR, ERBB2, ANG2, IGF1R, and integrin

αvβ3. In addition to their superiority in high efficacy multispecific bindings, Zybodies also retain

the advantage of traditional monoclonal antibodies in long half-time.

Figure 1-4: Schematic diagram of a Zybody molecule.

Reprinted by permission from Landes Bioscience: mAbs (LaFleur, D. W. et al. Monoclonal

antibody therapeutics with up to five specificities: functional enhancement through fusion of

target-specific peptides. mAbs. 2013, 5(2), 208-18.), 50 copyright (2013).

1.3.4.3 Small molecules drugs

Lapatinib is a small molecule drug marketed by GlaxoSmithKline. It was firstly approved by

37

FDA in March, 2007 for use in combination with capecitabine to treat ErbB2 positive metastatic

breast cancer patients. It is a kinase inhibitor that blocks the activity of both EGFR and ErbB2.

1.4 Proteomics

Proteomics is the study of determination of genes and cellular functions at the protein level.51 It

is a collection of techniques including enrichment and separation via various approaches, mass

spectrometry identification, and bioinformatics analysis of proteins and peptides.

1.4.1 Protein enrichment methods

1.4.1.1 Immunoprecipitation

Immunoprecipitation (IP) can largely decrease the complexity of biological or clinical samples

by affinity enrichment of proteins of interest and their interactors. Proteins or immunoaffinity

tags are captured by antibodies and enriched before the subsequent immunoblot or mass

spectrometry analysis.52 Although the outcome of IP experiments is not the entire protein-protein

interaction network, the partners of an interaction network can still be revealed.53 In MS analysis,

protein interactors are allowed to be identified simultaneously without bias;52 however, special

sample handling methods and experiment conditions should be applied due to the sensitivity of

mass spectrometry. For example, proper salt and detergent buffers should be selected in order to

maintain the sample complex and reduce non-specific bindings.53 The major limitation of IP

enrichment is the inadequate availability of proper capture molecules. Besides the most

38

commonly used polyclonal or monoclonal antibodies, antibody fragments and other scaffold-

based binding agents have been developed for IP experiments.54

1.4.1.2 Affinity chromatography

Phosphorylation-enrichment

Enrichment of phosphorylated proteins can facilitate the study of signaling process and decrease

sample complexity at the same time. Phosphorylated peptides can be enriched by anti-

phosphorylation antibodies, immobilised metal affinity chromatography (IMAC), or metal oxide

chromatography prior to mass spectrometry analysis.52 For example, by using anti-

phosphotyrosine antibodies, peptides containing phosphotyrosine were enriched from HeLa cell

lysate in order to study the time-dependent analysis of phosphotyrosine proteome upon

epidermal growth factor stimulation.55 The limitation of antibody enrichment is the availability

of the antibodies to phosphoserine and phosphothreonine.56 In addition to anti-phosphorylation

antibodies, IMAC is another common approach for enrichment and purification of

phosphopeptides. Several transitional metal ions, including Fe3+, Ga3+, Zn2+, etc.,57-59 have been

applied to enrich phosphorylated peptides. Moreover, titanium oxide (TiO2) microcolumns have

shown high selectivity to phosphorylated peptides, and the binding of non-phosphorylated

peptides could be significantly reduced when peptides were loaded with proper acid.60-61

Glycosylation-enrichment

Glycosylation is the most intricate and common post-translational modification, and it occurs in

more than half of human proteins.62 Lectin has been a desirable reagent to capture glycopeptides

39

and glycoproteins for almost half of a century.63 Several high-throughput multi-lectin affinity

chromatography (M-LAC) platforms have been developed to isolate glycoproteins from serum or

plasma to study glycoproteome.64-67 Enrichment of glycoproteins rather than glycopeptides not

only preserves the protein conformational structure, but also benefits from a stronger binding due

to an increased number of glycosylation sites. Compared to single or serial lectin capture, M-

LAC offers a more comprehensive capture of glycoproteins in complex clinical samples.64 In

addition, combination of lectin affinity purification with hydrophilic interaction liquid

chromatography (HILIC) has been reported to be an efficient and powerful strategy to enrich

glycopeptides before MS analysis and characterization of N-glycosylation in complex proteomic

samples.68

1.4.2 Protein and peptide separation techniques

1.4.2.1 Gel electrophoresis

Two-dimensional gel electrophoresis (2DE), which is a well-developed technique since it was

first introduced in 1975, is a process to separate proteins based on their different isoelectric

points (pI) and molecular weights.69 2DE is able to resolve, identify, and quantitate thousands of

proteins at the same time,70 and 2DE coupled with MS has been widely applied in proteomics

study in various biological samples.71-74 However, several shortcomings of 2DE have limited its

application, especially when other advanced separation techniques have been established in

recent decades. One main drawback of 2DE is the limited application in low abundant protein

analysis due to its biased recovery.75 Another major disadvantage of 2DE is the relatively poor

results for protein extraction and solubility. Proteins that are hydrophobic or have extreme pI are

40

very difficult to resolve by isoelectric focusing (IEF).76-77 This problem could be avoided in IEF-

free gel electrophoresis, such as one-dimensional sodium dodecyl sulfate polyacrylamide gel

electrophoresis (1D SDS-PAGE).

1D SDS-PAGE is a very common method used for protein separation before MS analysis.

Proteins are separated from the complex samples in gel by their different molecular weights, and

are stained by either silver or Coomassie Blue. Gels containing proteins are excised into small

bands, and each gel band is treated as an independent fractionation for the subsequent

experiments. Proteins in each gel section are digested by enzymes, and peptides are extracted

from gel bands and subjected to MS analysis. This method has the following advantages: first,

biological samples become compatible with MS after being treated in gel; second, no method

development is required because the SDS-PAGE and in-gel digestion protocols have been fixed;

finally, proteome recovery from SDS-PAGE is the highest among all protein separation

techniques.52, 78 In recent years, another 1D electrophoretic protein separation method called

GELFrEE (gel-eluted liquid fraction entrapment electrophoresis) has been developed. This

method also fractionates proteins based on the molecular weights, but employs a solution phase

to elute proteins from gel tubes.37, 79-80 This method is able to separate proteins in broad mass

range (low µg to mg) and molecular weight range (10 to 250 kDa) within a short time (about 1

hour). The reproducibility and protein recovery have been shown to be higher than those in

conventional SDS-PAGE.79, 81

1.4.2.2 Reversed phase liquid chromatography

Reverse phase liquid chromatography (RPLC) separates proteins or peptides based on their

41

different hydrophobicity. RPLC is the most dominant separation technique because of its

advantages in high efficiency, resolution, reproducibility, and direct-coupling to tandem mass

spectrometers via electrospray ionization.82 Since separation prior to MS is crucial to the overall

dynamic range and sensitivity of proteomic analysis, many efforts have been made to increase

the peak capacity, sensitivity, and analysis speed in RPLC.83-84 Using small size particles to pack

long columns has been shown to be an effective way to enhance peak capacity in RPLC.85

Moreover, ultra performance liquid chromatography (UPLC), which employs very small

particles (less than 2 µm), has been developed to provide a more rapid and higher resolving

power separation for proteomics study.86-87

1.4.2.3 Multidimensional liquid chromatography

Multidimensional liquid chromatography (MDLC) integrates several separation approaches, such

as ion exchange (IEX), size exclusion (SEC), normal phase (NP), and reverse phase (RP), in

order to reduce the sample complexity. It plays an important role in proteomics study especially

in analyzing large-scale proteomic samples with high-complexity. Various separation

mechanisms offered by different LC can increase peak capacity, selectivity, sensitivity, and

resolution.88 For example, a technology called multidimensional protein identification

technology (MudPIT) separates peptides in a biphasic column combined with a strong cation-

exchange (SCX) resin and reversed-phase resin.89 This method improves proteomic analysis with

high reproducibility and dynamic range (> five orders of magnitude),89 and reduces the false

positive rate dramatically to less than 1% in large-scale protein analysis.90 Another MDLC is

using RP in both dimensions. This could increase the peak capacity in the first dimension

42

separation, and its mobile phase is salt-free which is very compatible with MS.91 Different

stationary phases or pH of mobile phase are employed to achieve greater orthogonality.92-93 In

addition, other MDLC platforms, such as hydrophilic interaction chromatography (HILIC) x RP,

size exclusion (CZE) x RP, and affinity-based LC, have also been applied in peptide and protein

separations.91, 94

1.4.2.4 Capillary electrophoresis

Capillary electrophoresis (CE) offers a different separation mechanism compared to liquid

chromatography. CE has the two following advantages: first, small columns used in CE offer

faster separation; second, CE employs small liquid volumes and can avoid the considerable dead

volumes existing in traditional devices.95 A shortcoming of CE separation is limited sample

volume, and as a result mass spectrometers with high sensitivity are required. Isoelectric

focusing (IEF) is able to enrich samples efficiently with a concentration factor of 100 at

minimum,96 therefore prefractionation of biological samples by IEF prior to CE separation has

been reported to be a proficient method to decrease sample complexity and increase numbers of

protein identified in MS.97

Both of the prevalent MS ionization methods, ESI and MALDI, have been used for CE-MS. In

ESI, sheathless methods have been built up in order to increase the detection limit and reduce

background noise.98-100 In these methods, proper electrical connection at the ESI tip needs to be

selected for the purpose of maintaining a constant flow in CE.101

A major challenge in applying CE-MS in proteomics study is the relatively slow data acquisition

43

speed in MS compared to the highly efficient separation in CE.102 To circumvent this problem,

several techniques have been developed. One of these methods is to reduce the separation

voltage in CE during MS data acquisition.89, 103 In addition, peptide mixture is prefractionated in

liquid chromatography before it is injected into CE. Moreover, evolutions in mass spectrometers

such as Orbitrap Velos provide suitable and promising detectors for analyzing peptides and

proteins by CE.104

Alternatively, MALDI has also been an attractive ionization method in CE-MS based proteomics

because MS spectra are not collected within the narrow separation windows in CE as a result of

the off-line deposition of CE fluent.105 One major difficulty is to interface continuous CE with

MALDI target plates. Multiple platforms have been established so as to collect and mix CE

fluent with MALDI matrix prior to tandem MS analysis. For example, a continuous vacuum

deposition system was applied to couple CE with MS wherein samples and matrix were mixed in

an evacuated source chamber.106

CE has also been proven to be a powerful tool in intact proteins analysis.107-110 The highly

efficient separation provided by CE has made itself very suitable for characterizing protein

pharmaceuticals especially for differentiating glycoforms.111-112

1.4.3 Mass-spectrometry based proteomic study

Bottom-up proteomics

The bottom-up strategy is the most ubiquitous method in mass-spectrometry based proteomics,

especially for analyzing complex samples.82 In bottom-up proteomics, proteins extracted from

44

cells, tissues, or bodily fluids are digested into small peptides by proteolysis before being

introduced to mass spectrometers, and the peptide sequences are mapped to identify the

corresponding proteins according to databases.30 Tandem mass spectrometry is usually necessary

in bottom-up proteomics. The peptide mixtures digested from proteins are separated and

identified in tandem mass spectrometers to the maximum extent. This process is often referred to

as ‘Shotgun proteomics’.113 Since numerous peptides are eluted and introduced to a mass

spectrometer at the same time, it is very important to build mass spectrometers with high

resolution, sequencing speed, and sensitivity. Q Exactive instrument that features Higher energy

Collisional Dissociation (HCD) cell offers an exciting high performance in bottom-up

proteomics study.114

Top-down proteomics

Contrary to bottom-up proteomics, in top-down proteomics, intact proteins are directly sent into

high-resolution mass spectrometers to measure molecular weight, and are introduced into gas

phase for further fragmentation.115 Currently, this emerging method is mainly applied for the

investigation of single purified proteins. Despite its limited through-put, top-down proteomics

may become a powerful tool in proteomics study in the future, especially in PTM analysis.116

One of the major limitations of top-down proteomics is slower progress in fractionation of intact

proteins. Gel electrophoresis and liquid chromatography are the most common techniques for

intact protein separation36. Two-dimensional gel electrophoresis provides a good separation for

proteomic samples. However, the compatibility of 2D gel can be satisfied neither by MALDI nor

by ESI.73, 117 Other protein fractionation methods, especially multi-dimensional separation

45

techniques, such as solution isoelectric focusing (sIEF), gel-eluted liquid fraction entrapment

electrophoresis (GELFrEE), followed by ESI-compatible reverse phase liquid chromatography

(RPLC), have been applied in top-down proteomics to discover different isoforms of intact

protein.37 Moreover, development of top-down proteomics can benefit from the innovation in

mass spectrometry instrumentation. Fourier transform ion cyclotron resonance (FTICR) had

dominated the mass-spectrometry based top-down proteomics because of its advantages in high

mass limit and high accuracy and resolution.118 In recent years, cutting-edge mass spectrometers

such as Orbitrap Elite have also played increasingly important roles in top-down proteomics as a

result of their better resolving power in high mass-to-charge ratios compared to FTICR.119-120

Last but not least, databases and algorithms must also be built to identify intact proteins from

mass spectra. In recent years, various software platforms, such as ProSightPC, PIITA, USTag,

and MS-TopDown, have been developed in favor of top-down mass spectrum interpretation.121

There is another method called ‘middle-down’ strategy, in which proteins are limitedly digested

to generate large peptides (>3 kDa).36 This approach benefits from both bottom-up and top-down

strategies.

1.4.4 Mass spectrometer

1.4.4.1 Ionization methods

Soft ionization methods, particularly electrospray ionization (ESI) and matrix-assisted laser

desorption ionization (MALDI), have made mass spectrometry revolutionarily accessible to large,

thermally labile molecules such as polypeptides, proteins, and polymers.

46

ESI

In ESI, samples are sprayed from an aqueous or organic solvent into the inlet of the mass

spectrometer at the presence of a strong electric field with atmospheric pressure.122 Analytes

become multiply charged in ESI and therefore are detectible in the mass analyzer with a

relatively small m/z range.123 ESI is now a primary technique for online LC-MS, and has been

coupled with nano-flow LC in order to increase the overall sensitivity in proteomics study.124

MALDI

In MALDI, samples are cocrystallized with a chemical matrix, which sublimates upon exposure

to pulsed laser radiation and carries the sample [M+H]+ ions into the gas phase.125 The most

commonly used matrices are 2,5-dihydroxybenzoic acid (DHB) and α-cyano-4-hydroxycinnamic

acid (CHCA). DHB is a more reactive matrix and is preferred when samples need to be stable for

milliseconds rather than microseconds.126 Ions generated by MALDI are mainly singly charged

ions. MALDI has been shown to be an effective way to characterize peptides and map

glycosylation.127-129 Major disadvantages of MALDI are the relatively low shot-to-shot

reproducibility and high matrix background in low m/z range.82

1.4.4.2 Mass analyzer

Mass analyzers are where ions are stored and separated based on the mass-to-charge (m/z) ratios.

Common mass analyzers are quadrupole, ion trap, time-of-flight (TOF), Fourier transform ion

47

cyclotron resonance (FTICR), Orbitrap, etc. They have different properties in terms of mass

range, accuracy, sensitivity, scan speed, and dynamic range.

Quadrupole

A quadrupole consists of four metal rods of which a direct current and a radio frequency voltage

are applied to each opposite pair. The electric field changes with time so that ions of a certain

m/z value can have a stable trajectory through the quadrupole to reach the detector.130 The

advantages of quadrupole include low cost, application to triple quadrupole system, and simple

scanning mode. However, the major drawback is its low resolution.

Ion trap

Same as quadrupole, ion trap is one the most prevalent mass analyzers. In ion trap instruments,

ions are trapped in three-dimensional electric fields continuously, and are ejected from the ion

trap volume into a detector one m/z a time to acquire a MS spectra. When performing tandem

MS, a precursor ion is isolated while other ions are all ejected from the trap volume, and

fragmented into its product ions for analysis. The advantages in low cost, compact volume, high-

throughput, and inherent tandem MSn capability make it very suitable for bench top applications

and MSn experiments.

Time-of-flight (TOF)

48

In TOF, ions are accelerated to a specific kinetic energy and transmitted through a flight tube. At

the same kinetic energy, smaller ions have higher velocity and therefore reach detectors faster

than larger ions. Reflectors are used to correct the differences in the initial energies. The

advantage of TOF is high mass range, high resolution, and high sensitivity.

Fourier transform ion cyclotron (FT-MS)

In FTMS, ions are trapped in a cell within a spatially uniform magnetic field and move about the

z direction of the magnetic field in a cyclotronic motion, and their m/z values are determined by

measuring the frequency of motion of the ions82. The development of ion cyclotron resonance

(ICR) technology enables simultaneous determination of the frequencies of all ions.131 The

unsurpassed resolution once made FTMS a dominant mass analyzer in proteomics study;118, 131

however, due to its high cost both in purchase and maintenance, other high resolution

instruments have become much more prevalent.

Orbitrap

Orbitrap consists of a central spindle-like and an outer barrel-like electrode wherein ions are

trapped, as shown in Figure 1-5. Ions oscillate in the electrostatic field along the z direction of

the central electrode, and their m/z values are measured by the oscillation frequencies which are

converted from the current signals using Fourier transform.132 Orbitrap features high mass

accuracy (2-5 ppm), high resolving power (150,000), high m/z range, and high dynamic range

(greater than 10).

49

Orbitrap can couple to LTQ ion trap, and the resulting hybrid LTQ-Orbitrap mass analyzer

combines the advantage of both LTQ and Orbitrap and can be operated in a parallel mode

wherein full MS spectra are acquired in the Orbitrap and fragmentations are performed in LTQ.

This high-resolution mass analyzer can be coupled to various LC and become a very powerful

platform in proteomics study.134-136

Figure 1-5: Three-dimensional schematic of the Orbitrap cell.

Reprint with permission: Pomerantz, A. E.; Mullins, O. C.; Paul, G., Ruzicka, J.; & Sanders, M.

Orbitrap mass spectrometry: a proposal for routine analysis of nonvolatile components of

petroleum. Energy & Fuels. 2011, 25(7), 3077-3082.133 Copyright (2011) American Chemical

Society.

Another Orbitrap-based hybrid mass analyzer, Q Exactive, which consists of a quadrupole

coupled with Orbitrap, provides unique and complementary advantages to LTQ-Orbitrap. The

construction of the Q Exactive is shown in Figure 1-6. The quadrupole mass filter and high-

energy collision-induced dissociation (HCD) peptide fragmentation enables fast selection of

precursor ions for selected ion monitoring (SIM) and tandem MS/MS scan. This MS platform

has shown high performances and duty cycles in proteomics study.114

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1.4.4.3 Tandem mass spectrometry

Tandem mass spectrometry (MS/MS) has become the fundamental method for peptide and

protein identification, especially in high-throughput analysis. In MS/MS, precursor ions are first

selected and isolated, then fragmented by collision with inert gas atoms to generate product ions

in the second stage.

Figure 1-6: Construction details of the Q Exactive.

This research was originally published in Molecular & Cellular Proteomics: Michalski A. et al.

Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop

quadrupole Orbitrap mass spectrometer. Mol Cell Proteomics. 2011, 10(9), M111.011015.114

Copyright 2011 by American Society for Biochemistry and Molecular Biology.

1.4.4.3.1 Fragmentation methods in tandem mass spectrometry

Collision-induced dissociation (CID) and electron transfer dissociation (ETD) are currently most

51

popular fragmentation methods. A novel fragmentation approach, HCD, plays a leading role in

cutting edge MS instruments, such as Q Exactive and Orbitrap Elite. CID generate b- and y-ions

dominantly, and ETD provides c- and z-ions, whereas a-, b-, x-, y- and immonium ions can all be

observed in HCD, as shown in Figure 1-7.

Figure 1-7: Bond cleavages in MS/MS fragmentation.

Reprinted with permission from: Khatun, J.; Ramkissoon, K.; Giddings, M. C. Fragmentation

characteristics of collision-induced dissociation in MALDI TOF/TOF mass spectrometry. Anal.

Chem. 2007, 79(8), 3032-3040.137 Copyright (2007) American Chemical Society.

CID

CID is the most common fragmentation method in MS-based peptide mapping. Peptides from

enzymatic digestion of proteins are collided by inert gas molecules, and the resulted vibrational

excitement will cause dissociation of the peptide backbones and generate b- and y-ions.

ETD

Unlike CID, in ETD, peptide cation radicals are formed in electron transfer reaction, and then

52

dissociate to induce the cleavage of N-Cα bond.138 The fragment ions are termed c- and z-ions.

ETD employs the same mechanism as electron-capture dissociation (ECD), but has been much

more broadly applied because of its low cost.139 It has been shown that charge state of 3+ or

higher is necessary for efficient fragmentation in ETD, therefore ETD is suitable for large

peptides.140 In ETD, labile post-translational modifications (PTMs) are reserved. Combining

ETD and CID of an isolated charge-reduced species has been a powerful tool in mapping PTMs,

especially in disulfide bond linkages.141-145 Overall, ETD works in a complementary fashion with

CID, increases the numbers of identification in peptide mapping, and advances in elucidation of

PTM.

HCD

Higher-energy collision dissociation (or higher-energy C-trap dissociation) is a recently-

established fragmentation method for Orbitrap-based hybrid mass spectrometers. In HCD,

fragmentation of peptide ions occurs in the octopole collision cell at the far end of the C-trap, the

product ions are analyzed in Orbitrap at high resolution and high accuracy.146 The spectrum

generated in HCD are similar to those in CID, however, contain more information in low m/z

range, which have been used for identification of various PTM. For example, HCD is able to

generate distinct Y1 ions (peptide + GlcNAc) in glycopeptides fragmentation, which allows the

assignment of N-glycosylation and O-GlcNAc modification sites.147-148 This collision method not

only preserves the advantage of LTQ in ion isolation and storage, but also benefits from the

generation of low-molecular weight reporter ions and the acquisition of full fragment mass range.

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1.4.4.3.2 Multiple reaction monitoring (MRM)

MRM provides high throughput and high sensitivity in accurately quantitating proteins and

peptides in complex samples such as plasma and serum. It has been used to detect and quantitate

drug and drug metabolism in pharmaceuticals and investigate cellular signaling in proteomics.149-

150 It can also provide low quantitation limit and high dynamic range in absolutely quantitative

proteomics.151

Figure 1-8: MRM analysis in a QQQ mass spectrometer.

Reprinted by permission from Macmillan Publishers Ltd: Nature Methods (Gillette, M. A.; Carr

S. A. Quantitative analysis of peptides and proteins in biomedicine by targeted mass

spectrometry. Nature Methods 2012, 10(1), 28-34.), 152 copyright (2012).

Triple quadrupole (QQQ) has been extensively applied to operate MRM in order to achieve

ultimate selectivity and sensitivity. As shown in Figure 1- 8, a QQQ mass analyzer comprises

three quadrupole mass spectrometers in tandem. In the first quadrupole Q1, MS scan occurs and

precursor ions are selected, only peptides with specific m/z values can pass through Q1 to reach

the second quadrupole, where peptides are subjected into collision and transferred into the third

54

quadrupole (Q3). In Q3, a further selection is processed upon all fragmented ions, and particular

product ions can pass through to be detected. This double selection mechanism enables high

specificity and high throughput analysis.

1.4.4.4 Quantitative proteomic analysis by mass spectrometry

Mass spectrometry has not only been proven to be a powerful platform to characterize proteins in

complex sample, but also has been applied in quantitative proteomic analysis.153 In relative

quantitation, the signals of the same peptide under different conditions are compared, and the

amount of one protein is expressed in relation to another protein. In absolution quantitation,

peptides are often labeled with stable isotope or chemical groups, and the amounts of proteins in

question are determined. Generally MS-based proteomics quantitation methods can be

categorized into two approaches: quantitation based on integrated peak areas or intensities of

particular peptides, and comparison between the signals of stable isotope labeled and unlabeled

peptides.

15N labeling

The metabolic labeling method uses isotopic nuclei 15N as the exclusive nitrogen source in cell

culture to grow cells. These cells are mixed with cells grown under normal 14N media, and

proteins are extracted and digested, and then subjected into LC-MS analysis. The abundance of a

particular protein is compared by its corresponding integrated peak intensities of 14N- and 15N-

peptides. Apart from the high cost of 15N cell media, another disadvantage of this method is the

55

unpredicted mass difference in labeled peptide due to the random labeling efficiency.

Stable isotope labelling with amino acids in cell culture (SILAC)

SILAC is a technique where cell populations are grown in different cell medium that contains

light (natural) and heavy essential amino acids. Proteins are harvested and purified. Unlabeled

and labeled proteins are mixed and digested, followed by LC-MS analysis.154-155 Up to five

different states of cells can be compared when proper heavy amino acid combinations are

chosen.156

SILAC has been successfully applied in mammalian cells, bacteria, yeast, and plants.157 One

major advantage of this method is that SILAC is suitable for analysis of limited staring materials.

Minimum sample manipulations are required because of metabolic labeling. Moreover,

compared to the isotopic stable nuclei labeling, peptides are labeled more sequence-specific in

SILAC, and mass difference can be predicted.154 The major limitation of SILAC is that this

technique can only be used in metabolically active cells. As a result, tissue samples cannot be

quantified by SILAC.

Isotope coded affinity tag (ICAT)

This relative quantitation method was introduced by Gygi’s team in 1999 based on isotope-coded

affinity tags (ICATs) and mass spectrometry.158 The ICAT reagent is made up of three parts: a

biotin tag to isolate ICAT-labeled peptides, a stable-isotope-incorporated linker, and a thiol-

56

specific reactive group. The reagent can be presented in two forms, heavy and light forms,

containing eight deuteria and hydrogens, respectively. The heavy and light reagents are used to

treat two different cell states and covalently bind to cysteine residues in each protein. The protein

mixtures from cells are digested into peptides, and ICAT-labeled peptides are purified by avidin

affinity before HPLC separation and MS identification. The heavy reagent-labeled peptides have

8 Da m/z increase in singly charged ions (4 Da m/z for doubly charged ions) compared to ones

labeled by light reagent. The relative quantification is determined by the ratio of the peak areas

or peak intensities of the peptide pair. To avoid the problem caused by slightly different

chromatographic behaviors between deuterium and hydrogen, cleavable isotope-coded affinity

tag (cICAT), a second-generate ICAT reagent, has been developed.159 In cICAT, nine 13C are

used in heavy reagent instead of eight 2H. The advantage of ICAT is that the amount of starting

material is not limited. Moreover, due to the fact that ICAT is a postisolation stable isotope

labeling method, tissues or cells that cannot be quantitated with metabolic labeling are still

compatible with ICAT.158 However, this method could not quantitate proteins that do not have a

cysteine residue. Considering the fact that cysteine is present in only about 1% of all proteins, 160

ICAT is not a universal technique for quantitative proteomics.

Isobaric tag for relative and absolute quantitation (iTRAQ) and tandem mass tag (TMT)

Isobaric peptide labeling technique plays an important role in relative and absolute quantitative

proteomics. Three major reagents are predominantly applied: 4- and 8- plex iTRAQ, and 6-plex

TMT. ITRAQ employs a multiplexed set of reagents to quantitate proteins in complex samples.

The isobaric tagging compound is composed of a reporter group, a mass balance group, and a

57

reactive group that can form an amine bond at lysine side chain and N-termini of peptides. The

mass of the reporting group range from 114 to 117 in a 4-plex reagent set or 113-119, 121 in an

8-plex set. The peptides from different samples are labeled by iTRAQ, mixed and then analyzed

by LC-MS. The same peptide from different samples are eluted at the same time in LC and

cannot be differentiated in MS because of the isobaric nature of iTRAQ reagents. In tandem

MS/MS, singly charged reporting groups are released from the labeled peptides, and the

intensities of the product ions at corresponding signature m/z are used for relative quantitation of

different samples. TMT employs the same mechanism, but uses a set of reporter ions with six

different mass.161 Absolute quantification can be performed when a known amount of protein is

used as a standard. The advantage of isobaric peptide labeling technique is the parallel

proteomics study of multiple samples, which decreases the analysis time and dismisses the run-

to-run errors. In addition, the protein coverage remains similar with normal bottom-up

proteomics analysis.162 It has been reported that the 4-plex iTRAQ has higher numbers of

proteins identified compared to TMT and 8-plex iTRAQ.163 Though the labeling reagent is

relatively expensive, this is still a recommended approach for relative and absolute quantitation

with high reproducibility and high accuracy, especially in handling multiple proteomics

samples.164-167 Moreover, advances in fragmentation methods in HCD and mass analyzers in

Orbitrap have further improved the quantitation precision for iTRAQ and TMT.168

Absolute quantification (AQUA)

AQUA introduces an isotope-labeled standard peptide to mimic a native pre-selected peptide of a

particular protein in biological fluids. This ‘AQUA’ peptide is chemically synthesized and spiked

58

into the digested whole proteomics samples. Quantifications are performed by MRM in order to

reduce the background noise. The AQUA peptides can also be prepared with modifications such

as phosphorylation and methylation, so that post-translational modifications in certain proteins

can also be investigated, as shown in Figure 1-9169. Because the AQUA peptide was introduced

after protein digestion, the variations in sample preparation prior to MS analysis cannot be

corrected. For example, the application of AQUA has been reported to show limited efficiency in

gel-separated proteins.170 Unlike other isotopic labeling techniques such as ICAT and iTRAQ,

AQUA focuses on the relative quantitation of one of a few proteins rather than the whole

proteome. Therefore this method is preferred in biomarker study in clinical samples or specific

PTMs.171

1.5 Proteomics analysis of biopharmaceuticals

Biopharmaceuticals are unique medicines due to their intricate nature: they are very large,

masses range from several kDa to over 100 kDa; they have numerous post-translational

modifications; they are manufactured through complicated procedures, rather than the synthetic

route for traditional small molecule medicines. For structural characterization and functional

elucidation of therapeutic proteins including monoclonal antibodies (mAbs), many analytical

techniques have been developed, and bottom-up mass spectrometry-based proteomics has

become the most prevalent approach.

59

Figure 1-9: Absolute quantification of proteins and phosphoproteins using the AQUA strategy.

Reprint from Proceedings of the National Academy of Sciences of the United States of America:

Gerber, S. A.; Rush, J.; Stemman, O.; Kirschner, M. W.; Gygi, S. P. Absolute quantification of

proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A. 2003,

100(12), 6940-6945.169 Copyright 2003 Sherpa RoMEO.

1.5.1 Overview of post-translational modifications

Post-translational modifications (PTMs) plays an important role in cellular processes, including

recognition, signaling, targeting, etc. Up to now, over 400 different types of PTM and more than

90,000 individual PTM have been identified.177 PTM also largely accounts for heterogeneity

introduced into protein pharmaceuticals during manufacture process or storage, and may

60

influence immunogenicity and lead to product nonequivalence. Therefore it is essential to

development analytical methods to examine possible PTMs in protein pharmaceuticals. A

proteomics strategy, including enrichment of desired peptides and proteins, mass spectrometry

identification, and bioinformatics investigation, can provide an effective platform in

characterization of therapeutic proteins including monoclonal antibodies, which are the most-

promising classes of biotechnology product at present.

1.5.2 Common chemical modifications

In spite of the large number of various types of PTMs, only a few of them are associated with

currently marketed protein pharmaceuticals, including asparagine deamidation, oxidation,

glycosylation and non-enzymatic glycation, disulfide bonds, and heavy-chain C-terminal

processing and N-terminal cyclization in monoclonal antibodies.178 The other common PTMs,

such as phosphorylation, acetylation, and acylation, are responsible for different intracellular

processes. These PTMs are not often involved in characterization of PTMs in terms of

therapeutic proteins.179

Deamidation

Asparagine (Asn) deamidation and the subsequent formation of aspartic acid (Asp) and

isoaspartic acid (isoAsp) may occur in protein pharmaceuticals and lead to product heterogeneity

and instability. It is a spontaneous and irreversible process and commonly takes place in

therapeutic proteins. In deamidation, Asn loses an amine group in its side chain and cyclizes into

61

succinimide intermediate, which further hydrolyzes into the deamidation final products: a

mixture of Asp and isoAsp at a ratio of 1:3 to 1:4. Besides Asn deamidation, isoAsp can also

form from Asp isomerization. For both Asn deamidation and Asp isomerization, the lability is

highly dependent on primary sequence and protein conformation.180 The major degradation sites

include Asn before Gly or Ser, Asp before Gly, as well as highly flexible regions. The reaction

can be significantly accelerated under alkaline pH and elevated temperature. For example, Asp56

in light chain and Asp99/101 in heavy chain of recombinant antibodies are reported to be

isomerized rapidly under elevated temperatures.181

Identification of deamidated peptides by mass spectrometry is relatively straightforward.

Because the amine group is replaced by a hydroxyl group, a mass increase of 0.984 Da can be

detected in MS spectrum. The site of deamidated Asn can therefore be confirmed in CID MS/MS.

The differentiation of isoAsp from Asp isomerization by mass spectrometry is considerably

challenging, because peptides containing isoAsp and Asp have identical precursor m/z and CID

MS/MS spectrum. Development of fragmentation methods in ECD/ETD brought new

approaches for the identification of isoAsp by MS. Cournoyer et al. reported specific product

ions of isoAsp in ECD in synthetic peptides.182-183 Two distinctive product ions, cn•+58.0054

(C2H2O2) and zl–n–56.9976 (C2HO2), from a diagnostic cleavage were detected in peptides

containing isoAsp (here z is the position of isoAsp and l is the total number of amino acids in the

peptide). The same fragments also present with ETD.141, 184

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Oxidation

Oxidation is one of the chemical degradations that account for primary degradation process.

Oxidation may occur to proteins that contain methionine, cysteine, tryptophan, tyrosine, and

histidine. For protein pharmaceuticals, oxidation could take place in any stage of manufacturing

and could be accelerated under various conditions. For instance, Met oxidation is shown to be

pH independent but mainly determined by solvent accessibility.185-186 Fully solvent-exposed Met

is oxidized much more rapidly than buried Met. Oxidation of Trp residues is very sensitive to

UV light and has been observed in recombinant human interferon-α2a.187

Identification of oxidation by mass spectrometry mainly depends on the mass increase due to the

oxygen introduction, and oxidation sites can be confirmed in tandem MS/MS.

C-terminal lysine processing

The heavy chain C-terminal lysine can be completely or partially clipped in recombinant

monoclonal antibody products as a result of the action of basic carboxypeptidases.188 C-terminal

lysine processing will not significantly affect the structure and functions of antibodies, but will

introduce charge heterogeneity as a result of losing Lys residues.188-189 Removal of C-terminal

Lys can be identified by mass spectrometry easily using the mass decrease of 128.095 Da.

N-terminal cyclization

The glutamine or glutamate residues at N-termini of the heavy chain and the light chain of mAbs

63

can be partially cyclized into pyroglutamic acid (pGlu) through a spontaneous or possible

enzymatic process.190-191 Though it has not been revealed how this modification will affect the

biological functions of mAbs, the formation of N-terminal pGlu needs to be characterized

because it introduces heterogeneity to mAb products.192

Proteins or peptides containing pGlu have different chromatographic behavior with their

uncyclized counterparts. For example, mAbs with pGlu elute later in RPLC compared to those

with Gln. Moreover, mass spectrometry is often the method of choice in identification of pGlu

formation because cyclization of Gln and Glu will result in a mass loss of 17 and 18 Da,

respectively.

1.5.3 Di-sulfide bond linkages

Disulfide bonds help to conserve and stabilize protein tertiary and quaternary structure of

therapeutic proteins including antibodies. Mapping disulfide bonds by mass spectrometry

includes the following procedures: proteins are proteolytically digested under reduced and non-

reduced conditions; peptides are subjected to LC-MS analysis and tandem MS/MS fragmentation

for assignment; specific product ions generated under reduced and non-reduced peptides are

compared, and disulfide bond linkages are manually confirmed.193

The conventional CID fragmentation cleaves peptide backbones and leaves disulfide linkages

intact as a result of higher bond energy of disulfide bonds.194 The development of ETD

fragmentation has provided a novel approach for mapping disulfide bond linkages by MS.145

ETD preferentially breaks disulfide bonds which have a higher ability to capture electrons than

64

peptide backbones. As a result, the half-cystinyl peptide pairs usually have the highest abundance

in ETD-MS/MS spectrum of disulfide bond-containing peptides. Further CID-MS3

fragmentation could also help in disulfide linkage confirmation. This method has been

successfully applied in the identification of the unpaired cysteine status and complete mapping of

disulfides of recombinant tissue plasminogen activator,144 mapping disulfide bonds and their

possible scrambling in mAbs143, and assignment of cysteine knot and nested disulfides of

recombinant human arylsulfatase A.142

1.5.4 Glycosylation

Glycosylation functions variously and notably in therapeutic proteins: It aids in protein folding

and assembly, targeting and trafficking; It facilitates ligand recognition and binding; It stabilizes

proteins; It also fundamentally regulates the half-life of protein drugs in serum.195

Most mAbs have the only N-linked glycosylation site located at an Asn-X-Ser/Thr (here, X could

be any amino acid residue except for proline) consensus sequence in the constant region of heavy

chains.196 Because all currently marketed mAbs are produced in mammalian cell lines such as

Chinese hamster ovary (CHO) or NS0 cells, the N-linked oligosaccharides are often fucosylated

biantennary complex with 0, 1, or 2 terminal galactoses. In addition, oligosaccharides of high

mannose have also been reported as a major glycan type for mAbs.197 In other proteins, N-linked

glycans could consist of hybrid type, high mannose, and complex type.

When analyzing glycosylated peptides with conventional CID MS/MS, cleavage occurs on

glycosidic bond. As a result, limited information could be acquired, and the glycosylation sites

65

cannot be confirmed. To solve this problem, Peptide-N-Glycosidase F (PNGase F) can

specifically remove N-glycans with converting Asn to Asp, which brings a mass increase of 1 Da.

When using 18O labeled water for digestion, the peptide that was once N-glycosylated is

incorporated with the 18O from solvent and thus has another 2 Da mass increase. The resulting 3

Da mass increase in total can be used for identifying the N-glycosylation site in CID MS/MS.198

Unlike N-glycosylation, O-glycosylation is relatively challenging to characterize because of the

lack of a predicted amino acid consensus sequence. In addition, there is no enzyme that can

specifically remove O-linked glycans from Ser or Thr.

ETD offers unique advantage in the characterization of glycosylation. In ETD, cleavage occurs

on peptide backbones instead of glycosidic bonds.199 Thus, the practically complete

fragmentation of peptide backbone can be achieved while the glycan remains intact. By

combining CID and ETD fragmentation, the assignment of amino acid sequence, glycosylation

sites, and glycan structural information can be obtained at the same time.200

1.5.5 Pharmacokinetics and pharmacodynamics (PK/PD) study of therapeutic proteins

Immunoassays such as enzyme linked immune sorbent assays (ELISA) have often been used for

protein quantitation in PK/PD studies.201 This method has the following shortcomings: (1) the

development of a specific antibody is time-consuming, (2) nonspecific binding of endogenous

proteins can affect the selectivity of the assay and contribute to false-positive results, and (3)

immunoassays often cannot distinguish between the active (intact) and the metabolized (partially

degraded) forms of a peptide or protein drug. Therefore, they cannot provide metabolism data for

66

protein and peptide drugs.202-203

In recent years, mass spectrometry has become the method of choice for protein and peptide

quantitation, especially in PK/PD study of protein drugs. When using mass spectrometry for

protein (peptide) quantitation, proteins are often digested into peptides using enzymes, and one

or several signature peptides of the targeted proteins are then selected. The stable isotope labeled

internal standard (SIL) of the signature peptides or the intact protein drug is often spiked into

samples to correct for the variability in sample preparation and LC-MS analysis. Two or more

product ions are often monitored in MRM using triple quadrupole mass spectrometers to get the

absolution concentration of signature peptides.

Alternatively, when high-resolution, accurate-mass (HR/AM) mass spectrometry is applicable,

the intensities or peak areas of the selected precursor ions can be used for quantitation. The

combination of accurate mass, isotope pattern recognition, and elution time offers confident

confirmation of the targeted peptides in complex samples.204

In mass spectrometry-based protein quantitation, several techniques can be used to decrease

sample complexity, such as albumin and immunoglobulin depletion and solid phase extraction.

Besides, antibody capture is still often necessary to enrich protein drugs from biological fluidics.

Although the development of antibody is still time-consuming, mass spectrometry is able to

provide information for the potential structure change of protein drugs, such as chemical

modifications, or loss of payload drugs during the circulation of antibody-drug conjugates.

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1.6 Conclusions

The development of genomic techniques and mass spectrometry-based proteomics is valuable to

gain insights into the biology of ErbB2 in cancer. Various separation approaches and cutting-

edge mass spectrometers with versatile fragmentation methods have not only helped us to

investigate the disease but also discovered novel therapeutic products for the treatment of ErbB2-

positive breast cancer.

In Chapter 2, we integrated genomics and proteomics to study a unique ErbB2-positive breast

cancer cell line and identified several potential protein signatures for ErbB2 signaling and

several other oncogenes. In Chapter 3, isoforms of ErbB2 were identified from SKBR3 cell

lysate by using immunoprecipitation and LC-MS/MS analysis. In Chapters 4 and 5, a novel

bispecific mAb drug candidate for the potential treatment of ErbB2 positive breast cancer was

comprehensively characterized using various LC-MS platforms, and the pharmacokinetics and

metabolism of the drug candidate in mouse serum were studied by QQQ.

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Chapter 2 Genome Wide Proteomics of ERBB2 and

EGFR and Other Oncogenic Pathways in Inflammatory

Breast Cancer

Contribution:

This work was done with a global collaboration as part of the Chromosome-Centric Human

Proteome Project (C-HPP). My contribution includes: proteomics experiment design and

performance, data analysis, and manuscript writing. The other co-authors’ work in this chapter:

Dr. Massimo Cristofanilli, Dr. Fredika Robertson, Dr. James M. Reuben, and Dr. Zhaomei Mu:

supply of samples; Dr. Ronald C. Beavis, Dr. Matan Hofree, Dr. Trey Ideker, Dr. Gilbert S.

Omenn, and Dr. Susan Fanayan: data discussion and manuscript revision; Dr. Hogune Im and Dr.

Michael Snyder: RNA-Sequencing experiments; Dr. Seul-Ki Jeong and Dr. Young-ki Paik: grant

support; Dr. Shiaw-Lin Wu: experimental design and data discussion; Dr. William S. Hancock:

data discussion, manuscript revision, and grant support.

Publication:

Emma Yue Zhang, Massimo Cristofanilli, Fredika Robertson, James M. Reuben, Zhaomei Mu,

Ronald C. Beavis, Hogune Im, Michael Snyder, Matan Hofree, Trey Ideker, Gilbert S. Omenn,

Susan Fanayan, Seul-Ki Jeong, Young-ki Paik, Anna Fan Zhang, Shiaw-Lin Wu, and William S.

Hancock. “Genome Wide Proteomics of ERBB2 and EGFR and Other Oncogenic Pathways in

Inflammatory Breast Cancer”. J. Proteome Res., 2013, 12 (6), 2805-17.

Reprinted (adapted) with permission from Zhang et al. J. Proteome Res., 2013, 12 (6), 2805-17.

Copyright (2013) American Chemical Society.

83

2.1 Abstract

In this study we selected three breast cancer cell lines (SKBR3, SUM149 and SUM190) with

different oncogene expression levels involved in ERBB2 and EGFR signaling pathways as a

model system for the evaluation of selective integration of subsets of transcriptomic and

proteomic data. We assessed the oncogene status with reads per kilobase per million mapped

reads (RPKM)1 values for ERBB2 (14.4, 400, and 300 for SUM149, SUM190, and SKBR3,

respectively) and for EGFR (60.1, not detected, and 1.4 for the same 3 cell lines). We then used

RNA-Seq data to identify those oncogenes with significant transcript levels in these cell lines

(total 31) and interrogated the corresponding proteomics data sets for proteins with significant

interaction values with these oncogenes. The number of observed interactors for each oncogene

showed a significant range, e.g., 4.2% (JAK1) to 27.3% (MYC). The percentage is measured as a

fraction of the total protein interactions in a given data set vs total interactors for that oncogene

in STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, version 9.0) and I2D

(Interologous Interaction Database, version 1.95). This approach allowed us to focus on four

main oncogenes, ERBB2, EGFR, MYC, and GRB2, for pathway analysis. We used

bioinformatics sites GeneGo, PathwayCommons and NCI receptor signaling networks to identify

pathways that contained the four main oncogenes and had good coverage in the transcriptomic

and proteomic data sets as well as a significant number of oncogene interactors. The four

pathways identified were ERBB signaling, EGFR1 signaling, integrin outside-in signaling, and

validated targets of C-MYC transcriptional activation. The greater dynamic range of the RNA-

Seq values allowed the use of transcript ratios to correlate observed protein values with the

relative levels of the ERBB2 and EGFR transcripts in each of the four pathways. This provided

84

us with potential proteomic signatures for the SUM149 and 190 cell lines, growth factor

receptor-bound protein 7 (GRB7), Crk-like protein (CRKL) and Catenin delta-1 (CTNND1) for

ERBB signaling; caveolin 1 (CAV1), plectin (PLEC) for EGFR signaling; filamin A (FLNA) and

actinin alpha1 (ACTN1) (associated with high levels of EGFR transcript) for integrin signalings;

branched chain amino-acid transaminase 1 (BCAT1), carbamoyl-phosphate synthetase (CAD),

nucleolin (NCL) (high levels of EGFR transcript); transferring receptor (TFRC), metadherin

(MTDH) (high levels of ERBB2 transcript) for MYC signaling; S100-A2 protein (S100A2),

caveolin 1 (CAV1), Serpin B5 (SERPINB5), stratifin (SFN), PYD and CARD domain containing

(PYCARD), and EPH receptor A2 (EPHA2) for PI3K signaling, p53 subpathway. Future studies

of inflammatory breast cancer (IBC), from which the cell lines were derived, will be used to

explore the significance of these observations.

2.2 Introduction

Breast cancer is a major health problem with over 40,000 deaths each year in the United States.

We have previously studied proteomics and glycoproteomics in samples collected from breast

cancer patients 2−4 as potential markers for the early detection of breast cancer. As an extension

of these studies, we report in this manuscript on a study of protein expression as measured by

both RNA-Seq1 and proteomics of two cell lines established from primary inflammatory breast

cancer (IBC) tumors,5 namely, SUM149 and SUM190, which are ER (-) and PR (-), as well as

the well-studied cell line SKBR3, which is known to express high levels of ERBB2 and is ER (-)

and PR (-).

EGFR and ERBB2 are members of the epidermal growth factor receptor (EGFR) family, one of

20 subfamilies of human receptor tyrosine kinases (RTK).6 The EGF family is one of the best

85

studied growth factor receptor systems, often overexpressed in human tumors.7−9 Several small

molecule inhibitors and protein drugs have been developed to modulate disorders in the EGFR

family.10,11 Moreover, determination of ERBB2 status by immunohistochemistry (IHC) or

fluorescent in situ hybridization (FISH) has been recommended by the American Society of

Clinical Oncology (ASCO) as a marker for diagnosis and evaluation in primary invasive breast

cancer.12 Initially we will describe the analysis of the RNA-Seq data to determine the presence or

absence of oncogenes typically associated with breast cancer as well as the levels of the target

oncogenes ERBB2 and EGFR. These studies demonstrated the importance of EGFR and ERBB

family members in the cell lines, as well as other oncogenes such as TP53, CRKL, EZR and

MYC. We then explored different approaches to integrate the proteomic information with the

transcriptome data and compared the proteomic levels as measured by spectral count with the

transcript level as well as interaction values of the observed proteins with the panel of oncogenes.

These comparisons highlighted the 4 oncogenes, namely, EGFR, ERBB2, MYC and GRB2, and

allowed the identification of protein-based subpathways of interest for the different cell lines.

2.3 Materials and methods

2.3.1 Cell lines, cell lysis, and in-gel digestion

Cell lines SKBR3, SUM149 and SUM190. The human breast cancer cell line SKBR3 (ER/PR−,

HER2+, metastatic pleural effusion), was obtained from the American Type Culture Collection

(Manassas, VA) and maintained in culture with DMEM/F-12 medium supplemented with 10%

FBS (Tissue Culture Biologicals, Seal Beach, CA) and 1% of Antibiotic-Antimycotic 100X

(Gibco, Carlsbad, CA).

SUM149 and SUM190 cells were obtained from Dr. Stephen Ethier (Kramanos Institute, MI,

86

USA) and are commercially available (Asterand, Detroit, MI). SUM149 cells are ER/PR−,

HER2− (triple receptors negative), and the SUM190 cells are ER/PR−, HER2+. Both human

IBC cell lines were maintained in culture with Ham’s/F-12 medium supplemented with 10% FBS

(Tissue Culture Biologicals, Seal Beach, CA), 5 μg/mL of insulin, 1 μg/mL of hydrocortisone

and 1% of Antibiotic-Antimycotic 100X (Gibco, Carlsbad, CA).

Twenty microliters of lysis buffer (2% SDS in 50 mM NH4CO3) was added to 10 μL of cell

lysate. Cells were solubilized by sonication using 20 s bursts, followed by cooling on ice for 20 s,

in a process that was repeated for 10 times. The entire extract was concentrated down to 15 μL in

a speed vacuum and loaded onto a gel (SDS-PAGE, 4−12% gradient) to separate proteins by

molecular weight. After staining with Coomassie blue, each gel lane was cut into five individual

slices as shown in Figure S2-1 (Supporting Information).

Each slice was further minced into smaller pieces (approximately 0.5 mm2). The gel slices were

washed with 600 mL of water for 15 min and centrifuged, supernatant was removed, and 50%

ACN was added (1 mL), followed by shaking until no visible Coomassie stain remained.

Proteins were then reduced with dithiothreitol (DTT) by adding 250 μL of 10 mM DTT in 0.1 M

NH4CO3 and incubated for 30 min at 56 °C. Samples were subsequently alkylated at room

temperature and in the dark for 80 min with 250 μL of 55 mM iodoacetamide (IAA) in 0.1 M

NH4CO3. Trypsin digestion reagent (200 μL; 10 ng/mL of trypsin in 50 mM NH4CO3, pH 8.0)

was added, and samples were incubated for 30 min at 4 °C. The trypsin concentration was based

upon an estimate of approximately 0.1−0.5 mg of protein per gel slice and adjusted as necessary.

The solution was then replaced with 50 mM NH4CO3 to cover the gel pieces (50 μL) and

incubated overnight at 37 °C to elute peptides from the gel. Following this step, supernatant was

removed and stored. Gel pieces were further extracted with 5% formic acid (30 μL) and ACN,

87

(400 μL) at 37 °C for 10 min and then twice with 5% formic acid (30 μL) and ACN (200 μL).

The formic acid solution containing tryptic peptides was combined with the previous supernatant

and concentrated to 5−10 μL. The concentrated solution (trypsin-digested peptides) was

subjected to LC−MS analysis.

2.3.2 LTQ-FT MS

The in-gel digested peptides were analyzed by online LC using a linear IT coupled to a Fourier

transfer mass spectrometer (LTQ-FT MS, Thermo Electron, San Jose, CA) with a Dionex nano-

LC instrument (Ultimate 3000, Sunnyvale, CA) and a 75 mm i.d. × 15 cm C-18 capillary column

packed with Magic C18 (3 mm, 200 Å pore size) (Michrom Bioresources, Auburn, CA). The

LTQ-FT mass spectrometer was operated in the data-dependent mode to switch automatically

between MS and MS/MS acquisition. Survey full-scan MS spectra with two microscans (m/z

400−2000) were acquired in the Fourier transform ion cyclotron resonance cell with a mass

resolution of 100 000 at m/z 400 (after accumulation to a target value of 2 × 106 ions in the

linear IT), followed by ten sequential LTQ-MS/MS scans throughout the 90 min separation. The

analytical separation was carried out using a three-step linear gradient, starting from 2% B to

40% B in 40 min (A: water with 0.1% formic acid; B: ACN with 0.1% formic acid), increased to

60% B in 10 min, and then to 80% B in 5 min. The column flow rate was maintained at 200

nL/min.

2.3.3 Protein identification

Peptide sequences were identified using Thermo Proteome Discoverer 1.3 from a human

88

database SP.human.56.5 with full trypsin specificity and up to three internal missed cleavages.

The tolerance was 50 ppm for precursor ions and 0.8 Da for product ions. Dynamic

modifications were deamidation of asparagine, and static modification was

carbamidomethylation for cysteine. Peptides were identified with Xcorr scores above the

following thresholds: ≥3.8 for 3+ and higher charge state ions, ≥2.2 for 2+ ions, and ≥1.9 for 1+

ions. We used the spectral count approach to measure relative abundance of protein samples as

reported by Choi et al.13 We have selected several housekeeping proteins, glyceraldehyde-3-

phosphate dehydrogenase (GAPDH), b-actin (ACTB), b-tubulins (2A, 2B, 2C, 3 and 5),14 which

are ubiquitously expressed in a wide range of tissues and cell types,15 as internal standards for

relative quantification in order to minimize variations in the amount of samples loaded on the 1D

SDSPAGE gel. These proteins met the required criteria of high abundance and consistent ratios

across the 3 cell lines, as measured by peptide counts and extracted ions in the same gel section

between the different cell lines. The protein list also was submitted to the Gene A La Cart

(provided by www.genecards.com, uploaded to Gene A La Cart for analysis in August, 2011) to

acquire data for bioinformatics analysis, including gene symbols and other genomic information.

2.3.4 RNA-Seq measurement

Strand-specific RNA-Seq libraries were prepared and sequenced on a lane of the Illumina HiSeq

2000 instrument per sample to obtain transcript data.16 All RNA-Seq data are available at Short

Read Archive (SRS366582, SRS366583, SRS366584, SRS366609, SRS366610, SRS366611).

From total RNA, strand-specific RNA-Seq libraries were prepared according to Illumina TruSeq

standard procedures and sequenced at both ends (paired-end RNA-sequencing) on Illumina

HiSeq 2000. Tophat embedded with Bowtie was used to align the sequence reads to human

89

genome (hg19). Using Cufflinks, the alignments were assembled into gene transcripts (NCBI

build 37.2), and their relative abundances (RPKM) were calculated.

2.4 Results and discussion

We have previously studied on the role of two driver oncogenes, EGFR, ERBB2, in epithelial

cancers17,18 and have investigated the changes in their glycosylation patterns.2−4,19 To further

expand on our previous observations, we have performed a comparative study to explore the

total lysate proteome of a well established epithelial breast cancer cell line, SKBR3, which

overexpresses ERBB2 and two primary cell lines (SUM149 and SUM190) isolated from patients

with inflammatory breast cancer.5 We have employed a traditional proteomic analysis of the data

and compared these results with an alternative format, namely genome-wide proteomics using

the chromosome format (C-HPP20), which is being developed as part of the HUPO human

proteome initiative. One benefit of such approach is the facile integration of proteomic and

transcriptomics data as well as allowing for the identification of genomic regions in which a

driver oncogene may affect gene transcription of adjacent genes.

2.4.1 Analysis of cell lines SKBR3, SUM149, and SUM190

Each cell line was analyzed in triplicate, and relative quantitation was achieved with spectral

counts using a correction factor based on housekeeping proteins. With the availability of a deep

measurement of the transcriptome, by RNA-Seq (100 million reads), it is common to measure

10,000−11,000 transcripts in a cell line study. In contrast, a proteomic study comparable to what

is reported here will sample only approximately 10% of the expressed set of proteins. While the

90

transcriptome can enhance the proteomic measurement, the opposite is also true as a medium

level protein study can be used to explore the major phenotypic patterns observed in a study of

disease versus normal cell lines and patient tissue. In addition, there are examples of a protein

being identified in the absence of a measurable transcript level.21

In the proteomic analysis we used a conservative protocol for identifying proteins in replicate

analysis, which included high protein confidence and high peptide rank (Proteome Discoverer)

and with a FDR of less than 1%. We identified a total of 1444, 1396, and 964 proteins (numbers

of proteins with 2 or more peptides) in the SKBR3, SUM149 and 190 cell line samples,

respectively (numbers of proteins with 2 or more peptides were 1071, 1134, and 686 for SKBR3,

SUM149 and SUM190, respectively). In addition, selected proteins identified by one single

peptide were further analyzed using additional criteria such as high mass accuracy, fragmentation

spectra and observation of the corresponding transcript (see Table 2-1). In the cell line studies a

comparison of the SKBR3 with SUM190, SKBR3 with SUM149, and SUM190 with SUM149

proteome contents identified 751, 934, and 695 common proteins, respectively.

2.4.2 Characterization of EGFR and ERBB2

EGFR was identified in SUM149 and SKBR3 cell lysates, while ERBB2 was identified in

SKBR3 and SUM190 cell lysate preparations, consistent with IHC results in previous studies.5,22

As shown in Table S2-1 (Supporting Information), EGFR and ERBB2 were identified with 11

and 13 peptides for cell lines SUM149 and SKBR3, respectively. This table employs data from

GPMDB (Global Proteome Machine database)23 to assess the quality of peptides observed for

the two proteins. The peptides observed in our study have been frequently reported in the

literature, e.g., rank 1−5 and 1−4 for the most frequently observed, as well as other peptides for

91

EGFR and ERBB2, respectively. The MS/MS data for a diagnostic peptide for EGFR and

ERBB2 in shown in Figure S2-2 (Supporting Information). Both EGFR and ERBB2 were

detected with good sequence coverage (15.5 and 15.8%), although peptides derived from the N-

terminal domain of ERBB2 were not observed. The identification of ERBB2 was confirmed by

immunoprecipitation with the monoclonal antibody Trastuzumab (Herceptin) and subsequent

analysis on 1D SDS-PAGE and detected at an approximate molecular weight (MW) of 110 000

(theoretical 138 kD, data not shown).

2.4.3 Protein observations with RNA-Seq data and expressed in a genome wide format

(chromosomes)

Besides proteomic analysis, we have also discovered potential proteins of interest by comparing

proteomics data with the corresponding transcriptomic data in a chromosome format (see Tables

S3 and S4, Supporting Information, for the RNA-Seq results for SKBR3, SUM149 and

SUM190). We collected the genomic information from the Gene A La Cart tool provided by

www.genescards.org. In doing so, UniProt accession numbers for result files in Proteome

Discoverer, prior to submission to Gene a la Cart as identifiers to retrieve their genomic

information, including gene symbols, genomic locations (chromosome number, base pair

location of gene start and end, and gene size), and Ensemble cytobands. This will allow the

protein list to be organized by their locations on different chromosomes. The resulting data sets

for the three cell lines are shown in Table S2-3 (Supporting Information).

92

2.4.4 Use of RNA-Seq data to explore ERBB2 signaling pathways

As a first step we generated a list of 33 oncogenes associated with breast cancer from the Sanger,

Genecards databases, and literatures,24,25 which had either measurable transcript level (RPKM >1)

and in some cases proteomic data (see Table 2-1). The RNA-Seq values showed that the cell line

SUM149 had a high level of transcript for EGFR (RPKM = 60) and a relatively low value for

ERBB2 (RPKM =14). Conversely the cell line SUM190 had values of 400 and ND for ERBB2

and EGFR, respectively. The immortalized cell line SKBR3 expressed a high level of ERBB2

and a low level of EGFR (RPKM = 300 and 1.4, respectively). Other oncogenes with a high level

of transcript (RPKM > 40) were TP53, MYC (SUM149); GRB7, CRKL (SUM190) and EZR,

TOP2A (SKBR3). As described in a later section we also explored reported interactions between

the group of 31 oncogenes and the proteins observed in the SUM149 and 190 proteomic results.

93

Table 2-1: List of oncogenes associated with breast cancer with associated proteomic and

transcriptomic data

Gene

Symbol a SKBR3 b SUM190 b SUM149 b SKBR3 c SUM190 c SUM149 c

ACOT8 ND d ND ND 34.4 2.2 9.4

APC ND ND ND ND 3.4 1.2

BRCA 1 ND ND ND 1.0 28.3 1.9

CDKN2A ND ND ND 6.4 3.3 ND

CEACAM6 ND ND ND ND 16.2 ND

CRKL ND 9 ND 3.3 56.3 12.3

DEK 5 ND ND 32.4 12.5 26.0

EGFR e 12 ND 22 1.4 ND 60.1

ERBB2 e 46 19 ND 300.1 399.7 14.4

ERBB2IP ND ND ND 1.1 15.2 8.3

ERBB3 ND ND ND 6.6 23.1 6.6

ERBB4 ND ND ND ND 3.0 ND

EZR 92 40 46 54.0 10.9 26.4

GRB2 e ND ND ND 9.0 7.7 10.0

GRB7 2 f 2 f ND 30.3 57.5 5.8

JAK1 ND ND ND 1.8 12.0 7.9

KRAS ND ND 2 1.5 8.5 6.9

MET ND ND ND ND 5.6 9.3

MLH1 ND ND ND 3.5 3.6 6.3

MSH2 2 f ND 3 2.8 2.9 10.6

MTOR ND ND ND 1.7 7.2 16.2

MYC e ND ND ND 19.0 9.6 79.1

PIK3R1 ND ND ND ND 16.3 3.3

PIK3R2 ND ND ND 7.4 1.7 16.0

PIK3R3 ND ND ND 4.5 28.1 1.5

PPP1R1B ND ND ND 1.3 ND ND

PTEN e ND ND ND 0.8 6.1 0.8

RB1 ND ND ND 1.2 2.0 1.7

RHOC 11 ND 16 53.6 24.7 47.8

SRC ND ND ND 6.2 ND 7.5

STAT1 ND ND 1 f 1.5 9.7 11.3

TOP2A 58 ND 17 50.2 5.3 25.9

TP53 ND ND ND 14.8 1.3 40.6 a Gene symbols are from Genecards. b Spectral counts c RPKM values d ND = not detected e Oncogenes used for pathway analysis are highlighted by box f Identifications of single peptide proteins are shown in Supplementary Figure S2-3.

94

Figure 2-1 Part A and Part B compare the ERBB2 signaling pathway in two IBC cell lines,

SUM149 (high levels of EGFR transcript) and SUM190 (high levels of ERBB2 transcript) with

the ERBB2 pathway derived from the KEGG database. SUM190 presents an interesting situation

with high transcript levels of ERBB2 and ERBB3 (RPKM = 400 and 23, respectively) and a low

level for ERBB4 (RPKM = 3), without detectable transcript levels of EGFR (ERBB1) and a low

RNA-Seq value (4.91) for amphiregulin (AR in Figure 2-1), one of the EGFR ligands.26 ERBB2

is a special member in the ERBB family in that there has been no ligand discovered for ERBB2

and signaling largely depends on heterodimer formation with either EGFR, ERBB3 or

ERBB4.27,28 However, a high level of ERBB3 is found in the SUM190 transcript, and it has been

reported that the ERBB2/ERBB3 heterodimer is active in cell proliferation in breast tumor cells

(see highlighted blue lines in Figure 3-1 Part A).29 Conversely, as is shown in Figure 2-1 Part B

(highlighted blue lines) with the observed transcript values in the SUM149 cell line for the

EGFR family signaling pathway there are several possibilities for signaling with involvement of

EGFR dimers, ERBB2 heterodimers with EGFR or ERBB3. Since ERBB4 is not detected at

either the transcript or protein level, it is presumably not part of the signaling cascade. Thus

RNA-Seq studies identified potential differences between the two cell lines and thus set the stage

for a proteomic investigation. Another advantage of the RNA-Seq studies was the greater

dynamic range than the proteomic measurement; one important example was identification of

high levels of the transcript for the MYC oncogene in SUM149 and 190 (19 and 10, respectively)

in the absence of a proteomics signal. The importance of this oncogene is consistent with the

importance of the MEK/ERK pathway in carcinogenesis (see arrow in Figure 2-1) and is

supported by the large number of MYC interactors identified in the proteomics study (see Figure

2-2 and discussion later).

95

Figure 2-1. Part A: SUM190

96

Figure 2-1. Part B: SUM149

Figure 2-1: Annotation of KEGG ERBB2 signaling pathways with transcriptomic data.

Part A: SUM190; Part B: SUM149.

The pathway was derived from http://www.genome.jp/kegg/pathway/hsa/hsa04012.html in

January 2012.

The RPKM values are shown as follows. Green circle: RPKM value is larger than 15; yellow

circle: RPKM value is between 3 and 15; red circle: RPKM value is between 1 and 3; blue cross:

transcription value is under detection limit; blue line: potential preferred signaling based on

transcript levels.

http://www.genome.jp/kegg/pathway/hsa/hsa04012.html

97

(A) SUM149 (B) SUM190

Figure 2-2: A composite of SUM149 (A) and SUM190 (B) transcriptomic, proteomic, and

interaction data for significant oncogenes observed in SUM149 and SUM190.

The following notations are used. Line length = Interaction score (shorter line, stronger

interaction with ERBB2). Circle size = RPKM value (largest: RPKM>15, medium: RPKM

between 3 and 15, small: RPKM between 1 and 3, spot: RPKM <1). Black circle = if observed in

proteomic experiments. Percentage = percentage of proteins identified in SUM149 or 190 with

specific oncogene interactions as listed by STRING or I2D in Genecards.org

To further explore the difference between EGFR and ERBB2 signaling in SUM190 and 149

transcriptome, we used the ratio of the RPKM values to interrogate the NCI ErbB receptor

signaling network and visualized the data by assigning different colors based on the ratio values.

First, EGFR and ERBB2 are the most differentially expressed genes in this network. As can be

seen in Table 2-2 increased levels of EGFR transcript are associated with increased levels of the

ligands amphiregulin (AR), epiregulin (EPR) and transforming growth factor, alpha (TGFA) for

SUM149 vs 190, while the transcript levels for ERBB2 and associated receptors/ligands HBEGF,

98

ERBB3 and 4 are increased in SUM190 vs 149. Amphiregulin is identified as a ligand of

EGFR26 and acts as an effective mitogen for epithelial cells.30 Epiregulin is another EGFR ligand

that binds directly to EGFR and regulates tyrosine phosphorylation of EGFR.31 On the other

hand, ERBB3 and ERBB4 are reported to be part of ERBB2 heterodimer in ERBB2 signaling,

and ERBB3 has been reported to be necessary for tumor cell proliferation in breast cancer.29

Table 2-2: ErbB receptor signaling network a with RNA-Seq ratios (SUM149 vs. SUM190) b

Gene RPKM-

SUM149

RPKM-

SUM190

RATIO

(149/190) b

ERBB2

interact

Novoseek

tumor hits

EGFR 60.1 0.6 5.2 + 6585

AREG (AR) 73.7 4.9 3.7 121

EREG (EPR) 9.1 0.6 2.7 + 27

TGFA 3.9 0.5 1.7 + 926

BTC 0.5 1.0 -0.4 + 13

EGF ND ND -- + 1513

HBEGF 1.5 4.7 -1.2 + 122

ERBB3 6.6 23.1 -1.7 + 169

ERBB4 0.0 3.0 -2.0 + 103

ERBB2 14.4 399.7 -4.7 5807

More 190 More 149

a Gene set in this pathway is retrieved from the following link:

http://pid.nci.nih.gov/search/pathway_landing.shtml?pathway_id=erbb_network_pathway&path

way_name=ErbB%20receptor%20signaling%20network&source=NCI-

Nature%20curated&what=graphic&jpg=on&ppage=1

All pathways from Nature Cancer Institute were released on October 12, 2011.

b RPKM values are used to show the expression differences in two IBC cell lines, and the values

in the ratio column are calculated as followed: . By adding 1 to

RPKM values artificially, the ratio could still be calculated even if RPKM value is 0.

2.4.5 Proteomic analysis of SKBR3, SUM149, and 190 cell lines

With the importance of ERBB2 and EGFR signaling indicated by the RNA-Seq data, we then

http://pid.nci.nih.gov/search/pathway_landing.shtml?pathway_id=erbb_network_pathway&pathway_name=ErbB%20receptor%20signaling%20network&source=NCI-Nature%20curated&what=graphic&jpg=on&ppage=1



99

examined the correlation between our proteomics data, transcript levels and chromosome

location. In Table S2-2 (Supporting Information) we have ranked the 20 most abundant proteins

as measured by spectral count in the SKBR3 cell line (highest number of protein observations)

and compared these values with the corresponding RNA-Seq levels as well as the proteomic

values for SUM149 and 190 cell lines. As has been reported elsewhere there is a general

correlation between the levels of a transcript and the corresponding proteins, although relative

differences in transcript and protein stability as well as temporal events can result in exceptions

to this rule. The genes TUBB, ACTB and GAPDH, which were selected as housekeeping

proteins for normalization of the proteomic data, were indeed observed at high levels (spectral

count rank 17, 8, and 7, respectively, for SKBR3), and these genes were also observed with high

transcript values (RPKM of 209, 1391, and 2966, respectively). Conversely, the genes

HIST1H4A, EPPK1, ENO3 and FLNA offer examples of poor correlation with a rank of 15, 9,

10, 11 in the proteomic data and a RPKM of only 3, 4, 2, and 6, respectively. While the selection

of 20 examples in Table S2-2 (Supporting Information) as a representative protein set is arbitrary,

it is of interest to note that 11 of the 20 proteins are located on just 3 chromosomes: 6, 12, and 17.

The possible significance of this observation will be discussed in the next section. One of the

proteins coded by the gene MYH9 is a known oncogene,32 and such a high level of expression is

of potential interest.

It has been reported there is a relationship between levels of gene expression and gene density in

a chromosome region. Figure 2-3 shows the number of proteins identified in the SKBR3 study

reported for each chromosome together with the % observed (number of protein observations

divided by the number of protein coding genes on the chromosome). It is not surprising that the

highest number of protein observations occurs for chromosome 1 and the lowest for chromosome

100

13 (largest chromosome and chromosome with lowest number of protein coding gene density,

respectively). The highest % values were observed for genes 17, 12, 20, and 22, and while there

is some correlation with reported gene densities on each chromosome (order of gene density is

19, 17, 20 and 22, high to low) it is relevant to note that chromosome 12 had 5 of the 20 most

abundant proteins in Table S2-2 (Supporting Information), followed by chromosome 17 (3).

Another factor is that chromosome 17 contains the highly expressed oncogene ERBB2 that can

amplify a set of genes colocated near this oncogene.

Figure 2-3: Ratio of number of protein observations per number of genes for each chromosome

Solid bar = total number of proteins identified in each chromosome for proteomic analysis in

SKBR3

Squares = ratio of proteins identified in proteomic experiments relative to total gene numbers for

each chromosome (as a %)

101

2.4.6 Comparison of proteomic observations between cell lines

One of the challenges of studies with cancer cell lines compared with patient derived tumor

samples is the lack of suitable control samples. We chose the levels of ERBB2 as the comparator

and compared the relative abundance of proteins in the two ERBB2 expressing cell lines

(SUM190 and SKBR3, RPKM = 400 and 300) with SUM149 (RPKM = 14) in terms of unique

proteins and for proteins with a 10-fold higher expression (see Tables S3, Supporting

Information). Examples of proteins observed with this approach include the RAS associated

proteins that are commonly activated in tumors in which ERBB2 is overexpressed.31,33 RAS-

related proteins were preferentially observed in SUM190 and SKBR3 (ERBB2+) in that of the

24 different types of RAS-related proteins identified, SUM190 and SKBR3 accounted for 15 and

20, respectively, while only 6 were shared by all three cell lines. In addition, there are 5 RAS

proteins with relative abundance 2-fold higher in SUM190 and SKBR3 compared to SUM149.

Another example is cathepsin D which was elevated 6× and 10× more in SUM190 and SKBR3

compared to SUM149 and has previously been associated with Her2 amplification23 and is a

breast cancer marker.12 While this type of data analysis did detect some proteins with cancer

associations it did not lead to pathway discoveries similar to that observed with the RNA-Seq

analysis, and thus we explored alternative approaches.

2.4.7 Mapping of oncogene interactions with proteomic observations

With the use of interaction scores provided by Genecards (String, I2D) we recorded the values

for interactions between the proteins identified in the proteomic studies of the two IBC cell lines

102

and 21 oncogenes listed in Table 2-1. The large data set is given in Tables S3 and S4 (Supporting

Information), and a summary is given in Figure 2-2 with the proteomic and transcriptomic

experimental data as well as number of interacting proteins. First, Figure 2-2 shows oncogenes

that are known to interact with ERBB2, and the oncogenes that show a high degree of interaction

(EGFR, ERBB3, ERBB2IP, GRB2, GRB7, KRAS) are denoted by a shorter line. A relatively

high RNA-Seq measurement is shown by the size of the circle, e.g., ERBB2, GRB7 and MYC,

and those oncogenes with a proteomics value are shown with a black outline, e.g., ERBB2,

GRB7, CRKL, TOP2A (see Table 2-1 for numerical values). For each oncogene, the number of

interactions with proteins observed in the proteomic studies of either SUM149 or 190 is given in

the circle as a percentage of the total oncogene interactions. As shown in Figure 2 the top three

oncogenes with the greatest number of interactions with observed proteins are MYC, GRB2 and

EGFR with 268, 235, and 143 interactions, respectively, for SUM149. The basis for this

approach has been used by others in the development of bioinformatic processes for prioritizing

cancer associated genes with gene expression data combined with protein−protein interaction

network information, 34 as well as the observation that proteomic data when combined with

genomic information can add further discrimination to pathway analysis.35 Thus in our approach

we have combined mapping of oncogenes with RNA-Seq levels and identification of interacting

proteins in the proteomic data set, and we will now use this data to search for additional

pathways of interest in breast cancer.

2.4.8 Identification of pathways that contain ERBB2, EGFR, GRB2 and MYC interactors

As an example of our process we describe the selection process for ERBB2 interactors. From the

proteomic data set 35 proteins were found to be interacted with ERBB2 on the basis of I2D and

103

STRING databases. We then selected a subset of 14 proteins according to levels of protein

expression (spectral counts) and RNA-Seq values in the two IBC cell lines, SUM149, 190 and

the model cell line SKBR3 (see Table S2-3, Supporting Information). The process was repeated

for the three other oncogenes with greatest number of interactors with the proteomic data set

(Figure 2-2), namely, EGFR, GRB2 and MYC that resulted in 172, 289, and 336 strong

interactors with significant RNA-Seq or proteomics levels, respectively.

Table S2-3 (Supporting Information) also lists the chromosome locations of the interacting

proteins, and it is noteworthy that many of the genes in these pathways are located on cytoband

17q12, which is the site of the ERBB2 amplicon.21,36 Of this group of chromosome 17 genes,

ERBB2, GRB7, STAT3 and KRT17 are located in the same chromosome region (17q12 to q21.2)

and have the following Novoseek tumor associations based on literature text-mining (Genecards):

5807, 22, 693, and 24. The next stage in our process was to select disease relevant pathways

based on our integration of transcriptomic, proteomic and interaction data. Our goal was to find

at least one pathway for each of the four oncogenes that were well represented by the proteins

listed in Table S2-3 (Supporting Information) and we used Cytoscape and Pathway Commons in

this search. The pathways that we have selected are ERBB2, MYC, and PI3K signaling pathways

from NCI Pathway Interaction Database, EGFR from the Cancer Cell Map and Integrin

Signaling (GRB2) from GeneGo.

104

Table 2-3: EGFR1 signaling from NCI

Gene SKBR3 a SUM190 a SUM149 a SKBR3 b SUM190 b SUM149 b Ratio

149/190c

EGFR

interact

Novoseek

tomor

hits

KRT17 d 66 73 27 1.6 0.2 197.8 7.3 + 24

EGFR d 12 22 1.4 0.6 60.1 5.2 + 6585

CAV1 d 4 0.4 0.2 33.6 4.9 + 434

PLEC d 332 16 91 15.6 7.1 69.7 3.1 + 1

SRC e 6.2 0.1 7.5 3.0 + 397

PIK3R2 e 7.4 1.7 16.0 2.7 + 2

HTT 1 0.5 1.9 9.0 1.8 + 4

MTA2 3 2 19.5 11.5 37.6 1.6 5

AP2A1 8 9.4 4.3 14.9 1.6 + 0

HAT1 d 3 3.0 6.5 12.5 0.8 0

STAT5B 2 3.4 1.4 3.2 0.8 + 92

EPS15 1 2.8 4.1 7.8 0.8 + 2

KRT7 d 380 634 50 654.7 338.9 575.6 0.8 + 466

YWHAB 46 70 77 70.2 18.3 29.1 0.6 + 0

CRK 2 1 6.9 14.7 18.2 0.3 + 19

RALB 3 1 11.2 6.6 8.1 0.3 9

EEF1A1 d 162 250 903.9 309.6 357.8 0.2 + 20

STAT1 1 1.5 9.7 11.3 0.2 + 121

KRT8 d 655 286 28 2025.0 60.0 61.3 0.0 + 116

KRT18 d 334 131 21 1470.0 71.3 64.1 -0.2 + 158

KRAS 2 1.5 8.5 6.9 -0.3 + 615

RAB5A 8 5.4 6.7 5.2 -0.3 + 0

STAT3 35 23.4 13.8 10.6 -0.3 + 693

MAP2K1 d 2 2.4 10.3 7.8 -0.4 + 45

PEBP1 d 26 45 12 104.3 43.1 30.3 -0.5 22

JAK1 e 1.8 12.0 7.9 -0.6 + 9

RAC1 15 14 12 207.9 23.0 12.0 -0.9 + 88

ARF4 d 11 35 17.2 24.9 12.2 -1.0 + 0

CDC42 d 9 20 23.0 37.7 17.5 -1.1 + 43

CTNND1 d 28 7 5.5 68.9 19.3 -1.8 + 83

SH3BGRL 2 2 0.7 18.6 3.7 -2.1 + 0

CRKL d 9 3.3 56.3 12.3 -2.1 + 4

GRB7 2 2 30.3 57.5 5.8 -3.1 + 22

PIK3R3 e 4.5 28.1 1.5 -3.5 + 0

More 190 More 149

a Spectral counts

b RPKM values

c RPKM values are used to show the expression differences in two IBC cell lines, and the values

in the ratio column are calculated as followed: .

d Proteins with higher expression in SUM149 are highlighted in yellow and in blue those with

higher levels in SUM190.

e Known oncogene.

105

In Table 2-3 we have listed all the proteins identified in EGFR1 signaling pathway as well as

oncogenes (including those only observed with significant levels of transcript) in order of the

ratio of SUM149/190 RNA-Seq values. This approach allows us to take advantage of the much

greater dynamic range for RNA-Seq vs proteomics to compare differences between the two cell

lines. We then compared these ratios with the proteomics data obtained for these two cell lines.

The control cell line SKBR3 expresses high levels of ERBB2 transcript (300) and lower levels of

EGFR (1.4) and shows proteomic values that are mostly intermediate between SUM149 and 190.

In Table 2-3 we highlighted in yellow the proteins with higher expression in SUM149 and in

blue those with higher levels in SUM190. In general there was good agreement between RNA-

Seq and proteomic values, e.g., CAV1, PLEC for higher ratios of EGFR vs ERBB2 and GRB7,

CRKL and CTNND1 for higher ratios of ERBB2 vs EGFR. CRKL has been shown to associate

with lamellipodia formation in breast carcinoma,37 and coactivation of CRKL and estrogen

receptor alpha has been shown to be a promoter of tumorigenesis.38 These observations are

supported by literature reports, such as CTNND1 was genomically correlated to breast cancer

and cell proliferation in ERBB2 positive breast cancer cell lines.39−41 The overexpression of

caveolin-1 (CAV1) is frequently related to breast cancer42 and has been reported to be associated

with EGFR activation.43 Interestingly, the overexpression of both CAV1 and CAV2 has been

discovered in triple negative (TN) invasive breast cancer.44 In our study, SUM149 is only the TN

cell line, and CAV1 is only identified by proteomics in this cell line and a RPKM value (33.6)

that is much higher than for two ERBB2+ cell lines, i.e., SUM190 (0.2) and SKBR3 (0.4). At the

other extreme of Table 2-3, higher levels of ERBB2 transcript are associated with the proteomic

measurement of GRB7, growth factor receptor-bound protein 7, which is part of the ERBB2

amplicon in breast cancer.45 In addition most of the proteins in Table 2-3 have been reported to

106

interact with EGFR (30/34) and had literature associations with cancer (27/34).

Table 2-4 shows a similar analysis of the Integrin outside-in signaling pathway, which was

selected as an example of the oncogene GRB2, and shows an elevation of filamin A (FN1),

actinin alpha1 (ACTN1) in both the transcriptome and proteome of SUM149 vs 190 cell lines.

Of interest, Filamin A hosphorylation has been shown to mediate the effects of caveolin-1 on

cancer cell migration.46 For the c-MYC pathway (Table 2-5) the higher ratios of EGFR transcript

were associated with increased proteomic levels of branched chain amino-acid transaminase 1

(BCAT1), cytosolic, carbamoyl-phosphate synthetase 2 (CAD) and nucleolin (NCL), while

higher ERBB2 ratios are associated with transferrin receptor (TFRC) and metadherin (MTDH).

Examples of the significance of these proteins include the observation that nucleolin colocalizes

with BRCA1 in breast carcinoma tissue,47 and metahedrin is a valuable marker of breast cancer

progression, and high expression may play a role in tumorigenesis of breast cancer.48,49 As was

observed for the EGFR pathway, most of the proteins in Table 2-4 (GRB2) and Table 2-5 (MYC)

contained a significant number of interactors (12/17 and 28/31) and literature associations with

cancer (16/17 and 24/31) respectively.

107

Table 2-4: Integrin outside-in signaling a

Gene SKBR3b SUM190 b SUM149b SKBR3 c SUM190 c SUM149 c Ratio

149/190d

GRB2

interact

Novoseek

tomor

hits

FN1 e 0 0 1 1.3 0.1 111.9 6.6 + 159

FLNA e 420 5 524 7.0 12.6 414.3 4.9 + 9

ACTN1 e 75 54 154 8.9 8.6 81.9 3.1 + 5

SRC f 7.2 0.1 7.5 3.0 + 397

ACTN4 e 158 52 103 73.7 11.4 71.2 2.5 + 14

ACTB 499 545 476 1083.8 196.1 559.5 1.5 + 3

VCL e 100 5 17 8.6 3.1 8.9 1.3 28

ITGB1 e 0 0 7 7.6 12.9 32.1 1.3 + 71

TLN1 e 72 0 46 5.3 5.5 12.9 1.1 0

CTNNB1 0 12 12 8.4 21.9 49.0 1.1 + 1681

ITGA2 e 0 0 6 0.2 2.4 3.7 0.4 28

GRB2 f 10.2 7.7 10.0 0.3 + 35

VTN 2 0 0 2.5 0.0 0.1 0.1 102

ITGAV 2 0 0 1.3 7.1 7.9 0.1 23

ACTR3 28 16 17 15.6 20.4 20.8 0.0 + 5

MAP2K1 e 0 2 0 3.1 10.3 7.8 -0.4 + 45

RAC1 15 14 12 190.8 23.0 12.0 -0.9 + 88

More 190 More 149

a This pathway is retrieved from GeneGo in January, 2012.

b Spectral counts

c RPKM values

d RPKM values are used to show the expression differences in two IBC cell lines, and the values


e Proteins with higher expression in SUM149 are highlighted in yellow and in blue those with


f Known oncogene.

108

Table 2-5: Validated targets of C-MYC transcriptional activation (a sub-pathway of c-MYC

pathway)


149/190c

MYC

interact

Novoseek

tomor

hits

RCC1 2

0.1 0.0 26.8 4.8 + 0

HMGA1 d

1 35.6 3.5 119.0 4.7 + 99

BCAT1 d

4 0.0 0.0 11.1 3.6 + 0

CAD d 43

18 2.5 3.2 22.0 2.5

2

RUVBL2 d 2 2 4 73.0 10.4 53.9 2.3 + 0

NCL d 60 45 142 118.1 14.9 70.0 2.2 + 21

GAPDH 522 495 539 2966.0 205.2 800.7 2.0 + 88

BAX d 2

2 12.8 2.8 13.3 1.9 + 848

RUVBL1 2 7 10 13.2 6.3 26.3 1.9 + 0

CCNB1 d

1 17.3 8.8 32.3 1.8 + 138

PFKM 5

7.0 5.5 19.3 1.6 + 0

EIF4A1 49 96 77 128.0 21.6 61.8 1.5 + 2

ENO1 758 1073 661 481.8 169.7 464.3 1.4 + 49

TRRAP d 2

1 1.2 3.3 10.7 1.4 + 1

LDHA d 179 115 215 213.6 19.2 48.2 1.3 + 27

TK1 6 5 3 120.2 11.0 28.3 1.3 + 67

SLC2A1 5

22.1 7.7 19.7 1.3 + 451

HUWE1 d 15

37 3.3 16.1 36.0 1.1 + 6

HSPD1 216 117 107 73.6 14.5 31.8 1.1 + 85

PRDX3 31 7 7 23.6 14.6 31.3 1.1 + 1

ACTL6A d

4 12.1 12.6 24.1 0.9 + 0

EIF4G1 d 25 12 45 35.8 37.8 60.9 0.7 + 16

NME1 29 23 20 0.1 26.4 36.0 0.4

732

PTMA d 6

3 550.7 77.5 88.0 0.2

18

EIF2S1 6 9 6 5.9 13.1 12.9 0.0 + 14

DDX18 d 2 2 4 6.9 6.7 6.5 0.0 + 5

HSPA4 26 31 36 8.0 23.0 21.6 -0.1 + 385

HSP90-

AA1 260 326 287 254.6 58.8 42.3 -0.5 + 485

EIF4E 3

7.5 3.1 1.8 -0.5 + 181

MTDH d

5

13.6 18.4 9.7 -0.9 + 25

TFRC d 41 21 12 23.2 48.2 14.3 -1.7 + 0

More 190 More 149 a Spectral counts

b RPKM values

c RPKM values are used to show the expression differences in two IBC cell lines, and the values




109

A similar analysis of the p53 pathway is shown in Table 2-6. This pathway is a subpathway of

Class I PI3K signaling events mediated by Akt and was selected as an example of the oncogene

PTEN (phosphatase and tensin homologue). Tumor suppressor PTEN has been observed to be

deleted in TN breast cancer, which shown related to resistance of EGFR targeting therapy.50 In

our data set, SUM149, which is a classic TN breast cancer, has a very low level transcript

expression of PTEN (0.8), compared to SUM190 (6.1). Interestingly, another tumor suppressor,

SERPINB5 (Serpin B5), which has been reported to be negatively correlated with both ER and

PGR genes in a quantitative DNA analysis,51 was only observed in SUM149 (proteomics and

trancriptomics), which is the only TN cell line in the study. Likewise, amplification of S100A2

(Protein S100-A2) was observed in both proteomics and transcriptomics experiments. This

protein, as one of S100 families, has been reported to be upregulated in mRNA expression in ER-

negative breast cancer patients and potentially promote cancer metastasis.52 SFN (14-3-3 protein

sigma), which acts as p53-regulated inhibitor of G2/M progression, has been reported to be

silenced due to DNA hypermethylation in breast cancer.53,54 A similar silencing due to

methylation for PYCARD (or TMS1) has been observed in breast cancer cells.55 However, both

overexpression of SFN and PYCARD in transcript and proteomic level was detected in SUM149,

which could provide a potential diagnostic marker for TN breast cancer. Similarly, EPH2, which

overexpresses in more than 60% of breast cancer patients,56 has been listed as potential clinical

target in TN breast cancer.44 Expression of EPH2 has been observed to be stimulated by the

activation of EGFR.57 This is consistent with the EPH2 expression in our experiment, in which

EPH2 was only identified in SUM149 (proteomics) and greatly amplified in transcriptomic level.

110

Table 2-6: p53 pathway (a sub-pathway of Class I PI3K signaling events mediated by Akt)


149/190c

PTEN

interact

Novoseek

tomor

hits

S100A2 d

14 1.6 0.4 53.8 5.3

66

EGFR d 12

22 1.4 0.6 60.1 5.2

6585

CAV1 d

4 0.4 0.2 33.6 4.9 + 434

TP53 e

14.8 1.3 40.6 4.2 + 24003

SERP-

INB5 d 14 0.0 0.4 23.5 4.2

482

SFN d 6 23 88 15.2 2.8 64.2 4.1

25

PRMT1 d

2 58.8 7.4 94.2 3.5

10

NDRG1 18

50.6 0.9 19.7 3.5

43

PY-

CARD d 3 0.1 0.0 9.5 3.4

21

GPX1 d

1 0.1 13.5 132.2 3.2

12

EPHA2 d

4 2.1 6.0 51.4 2.9

208

PRKCD 3

39.6 0.1 5.3 2.5

19

CSE1L d 66 21 95 19.9 10.1 59.8 2.5

7

BAX d 2

2 12.8 2.8 13.3 1.9

848

TRIM28 d 12 9 5 42.8 18.1 69.7 1.9

1

HTT d

1 0.5 1.9 9.0 1.8

4

SMAR-

CA4 d 5

2 21.7 8.4 31.4 1.8

61

CCNB1 d

1 17.3 8.8 32.3 1.8

138

MSH2 d 2

3 2.8 2.9 10.6 1.6

338

PCNA 25 16 20 47.2 22.6 63.8 1.5

2016

TRRAP d 2

1 1.2 3.3 10.7 1.4

1

BID d

2 2.5 1.4 5.4 1.4

24

PRMT5 d

3 6.8 6.9 19.5 1.4

5

HUWE1 d 15

37 3.3 16.1 36.0 1.1

6

CTSD d 52 31 5 346.5 34.9 68.6 1.0

430

MLH1 e

3.5 3.6 6.3 0.7

1123

MET e

0.8 5.6 9.3 0.7

379

RPL5 34 35 29 658.1 98.9 145.3 0.6

0

ATR d

1 0.6 3.8 5.3 0.4

10

NEDD8 d 9

11 100.7 15.1 19.7 0.4

4

USP7 2

8.3 9.2 8.9 0.0

13

RB1 e

1.2 2.0 1.7 -0.1

1484

PPP2CA

d 5

6.4 22.0 15.1 -0.5

0

DDX5 6 5 8 32.2 40.2 19.8 -1.0

6

APC e

0.2 3.4 1.2 -1.0

573

PTEN e

0.8 6.1 0.8 -1.9

2252

More 190 More 149

a Spectral counts b RPKM values c RPKM values are used to show the expression differences in two IBC cell lines, and the values



higher levels in SUM190. e Known oncogene.

111

2.5 Conclusion

In view of the importance of EGFR/ERBB2 heterodimer signaling in breast cancer, it is of

interest to explore the transcriptomic and proteomic analysis of two primary cell lines isolated

from inflammatory breast cancer patients, one (SUM149) that expresses high levels of EGFR

transcript with much lower levels of ERBB2, while the other expresses very high levels of

ERBB2 transcript (SUM190) and no detectable EGFR transcript. As a control we used a SKBR3

cell line that expressed high levels of ERBB2 transcript and low levels of EGFR. Analysis of the

transcript levels indicated that the most likely signaling pathway for SUM190 involved the

ERBB2/ERBB3 heterodimer, while SUM149 had several possibilities with involvement of

EGFR dimers, ERBB2 heterodimers with EGFR and ERBB2 or ERBB3. We then explored the

proteome of the two cell lines in terms of correlations between the transcriptome and proteomic

measurements, identification of a panel of 21 oncogenes expressed in the two cell lines,

interaction analysis of the observed proteins with this panel of oncogenes and selection of

relevant cancer pathways. The analysis resulted in 4 pathways in addition to ERBB2 signaling

(EGFR, integrin, MYC signaling, and PI3K signaling, see Tables 2-4 to 2-6) that contained many

of the oncogene interacting proteins. In general there was reasonable agreement between the

RNA-Seq and proteomic values shown in these tables except for some housekeeping proteins

(see Discussion section). We list here those proteins that were correlated with higher levels of

EGFR or ERBB2 transcript, respectively. EGFR signaling: caveolin 1 (CAV1), plectin (PLEC)

(EGFR); growth factor receptor bound protein 7 (GRB7), Crk-like protein (CRKL) and Catenin

delta-1 (CTNND1) (ERBB2). Integrin signaling: filamin A (FLNA) and actinin alpha1 (ACTN1)

(EGFR). MYC signaling: branched chain amino-acid transaminase 1 (BCAT1), carbamoyl-

phosphate synthetase (CAD), nucleolin (NCL) (EGFR); transferrin receptor (TFRC), metadherin

112

(MTDH) (ERBB2). p53 signaling: S100-A2 protein (S100A2), caveolin 1 (CAV1), Serpin B5

(SERPINB5), stratifin (SFN), PYD and CARD domain containing (PYCARD), and EPH

receptor A2 (EPHA2)(EGFR). While the depth of the proteomic analysis was limited partly

because of technical issues with analysis of the primary cell lines, this study was designed to use

proteomics to identify higher level protein expressions that correlated with the transcriptome

study. In this study we have demonstrated that one of the goals of the chromosome-centric

human proteome project (C-HPP), which is to integrate RNA-Seq with proteomics measurement,

is of value. We plan in a future study to explore the potential of the proteins identified in this

study as markers of ERBB2 and EGFR signaling as well as activation of the oncogenes MYC

and GRB2 in a study of breast cancer tumor samples.

2.6 Acknowledgement

This work was supported by following research grants: (Korea) The World Class University

program through the National Research Foundation of Korea funded by the Ministry of

Education, Science and Technology (R31-2008-000-10086-0 (W.S.H. and Y.-K.P.), National

Project for the Personalized Genomic Medicine A111218-11-CP01 (to Y.-K.P.) from the Korean

Ministry of Health and Welfare; (USA) The National Institutes of Health Grants, U01-CA128427

to W.S.H.,U54DA021519,UL1 RR024986, RM-08-029, and U54ES017885 to G.S.O.; NIH grant

(M.P.S. and H.I.); Texas State Rider for the Morgan Welch Inflammatory Breast Cancer Program

and the G.Morris Dorrance Jr. Chair inMedical Oncology (M.C., Z.M.).

2.7 Supplementary information

113

Supplementary Figure S2-1: SDS-PAGE separation of SUM149 & SUM190 Cell lysates. Gel

bands as indicated by squares were Cutout for the subsequent in-gel digestion and LC-MS

analysis.

114

Part A. An example of a peptide of EGFR_HUMAN identified in SUM149.

Part B. An example of a peptide of ERBB2_HUMAN identified in SUM190.

Supplementary Figure S2-2: LC-MS analysis of SUM149 (gel band #1) and SUM190 (gel

band #1).

115

400 600 800 1000 1200 1400 1600m/z

0

20

40

60

80

100

Re

lati

ve A

bu

nda

nce

704.46

918.57

792.46

693.02 996.47

534.30 1088.76

1216.78

387.37

664.42 1241.58

1402.82

430.35 1453.74

493.83370.32 1480.76

[y2-H2O]+

y3+

y4+

[y12-H2O]2+

[b7-H2O]+

y6+

[b8-H2O]+

y10+

y11+

y13+

y14+

y8+

b12+

924.48b9

+

L-I-G-Q-Q-G-L-V-D-G-L-F-L-V-Rb7

y8

b8 b9

y6

b12

y3y4 y2y10y11y12y13y14CID-MS2 precursor m/z 815.49 (2+)

814.5 815.0 815.5m/z

0

50

100

Re

lati

ve A

bu

nda

nce

814.4854814.9858

815.4909

2+MS (FT)

Theoretical m/z: 814.4801

Part A. Identification of GRB7_HUMAN in SUM190.

400 600 800 1000 1200 1400 1600m/z

0

20

40

60

80

100

Re

lati

ve A

bu

nda

nce

802.06

704.38

918.18

534.381088.38

856.73

971.04

988.041217.60

679.36

1164.17412.30 642.351241.55566.08387.23274.15

1453.20

y73+ y3

+

b4+

y4+

y6+

[y7-H2O]+

y8+

y10+

y11+

y13+

y14+

1401.61

b12+

710.17

b7+

1094.56

b11+

CID-MS2 precursor m/z 814.99 (2+)

814.5 815.0 815.5m/z

0

50

100

Re

lati

ve A

bu

nda

nce

814.4851814.9849

815.4866

2+MS (FT)


L-I-G-Q-Q-G-L-V-D-G-L-F-L-V-R

y8 y6

b12

y3y4y10y11y13y14 y7

b4 b7 b11

Part B. Identification of GRB7_HUMAN in SKBR3.

116

400 600 800 1000 1200 1400 1600 1800m/z

0

20

40

60

80

100

Re

lati

ve A

bu

nda

nce

1011.57

930.49

833.26

1124.611366.63701.57

588.28

800.38 1537.08575.43460.46 676.51 1665.811482.591795.91

y4+ b5

+

y5+

y6+

y152+

b8+

[b9-H2O]+

1012.60y9

+

[b10-H2O]+

[b12-H2O]+

1367.74y12

+

[b14-H2O]+

y15+

b16+

[b7-H2O]+


971.0 971.5 972.0 972.5 973.0m/z

0

50

100

Re

lati

ve A

bu

nda

nce

971.5515

971.0506972.0521

972.9626

2+MS (FT)


L-Y-Q-G-I-N-Q-L-P-N-V-I-Q-A-L-E-K

y12

b5 b7

y9

b8 b10

y5

b12 b14 b16

y4y6y15

Part C. Identification of MSH2_HUMAN in SKBR3.

400 600 800 1000 1200 1400 1600 1800 2000m/z

0

20

40

60

80

100

Re

lati

ve A

bu

nda

nce

823.54

1000.52

795.36

1064.54

1111.41936.50

626.36 1225.59536.421452.44

400.02

1563.971338.44382.16

1923.981837.65

[b3-H2O]+

b3+

[y4-H2O]+ b5+

y132+

[b7-H2O]+

[y8-H2O]+

[b8-H2O]+

y10+

y11+

y12+

b13+

F-H-D-L-L-S-Q-L-D-D-Q-Y-S-Rb3

y4

b5

y13

b7b8

y8y10y11y12

b13


869.0 869.5 870.0 870.5 871.0m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

869.4142

870.4112

870.9378868.9272 869.9298

2+MS (FT)


Part D. Identification of STAT1_HUMAN in SUM149.

Supplementary Figure S2-3: Identifications of proteins with single peptide from Table 1.

117

Supplementary Table S2-1: Catalog of ‘quality’ observed peptides for EGFR_HUMAN and

ERBB2_HUMAN.

a. EGFR (ENSP00000275493) peptides observed in SUM149 Cell line (GPMDB,

(ENSP00000275493, most abundant ENSP has the highest total number of observations. 11

peptides are listed for ENSP00000275493).

Peptide sequence Rank in

GPMDB

Charge

state we

observed

Charge

in

GPMDB

Observed in GPMDB

z=1 z=2 z=3

LTQLGTFEDHFLSLQR 1 2 1, 2, 3 28 1952 828

VLGSGAFGTVYK 2 2 1, 2 129 1419 -

IPLENLQIIR 3 2 1, 2 3 1369 -

NYVVTDHGSCVR 4 2 1, 2, 3 4 1227 37

NLQEILHGAVR 5 2 1, 2, 3 23 782 89

FSNNPALCNVESIQWR 7 2 2, 3 - 714 35

ELVEPLTPSGEAPNQALLR 17 2 2, 3 - 201 14

GSTAENAEYLR 30 2 1, 2 4 71 -

YSSDPTGALTEDSIDDTFLPV

PEYINQSVPK 68 3 2, 3 - 7 65

RPAGSVQNPVYHNQPLNPAP

SR 69 3 2, 3 - 6 31

TIQEVAGYVLIALNTVER 104 3 3 - - 70

b. ERBB2 (ENSP00000269571) peptides observed in SKBR3.

Peptide sequence Rank in

GPMDB

Charge

state we

observed

Charge

in

GPMDB

Observed in GPMDB

z=1 z=2 z=3

VLGSGAFGTVYK 1 2 1, 2 129 1419 -

WMALESILR 2 2 2 - 102 -

FVVIQNEDLGPASPLDSTFY

R 3 2, 3 2, 3 - 124 51

NPQLCYQDTILWK 6 2 2 - 79 -

LPQPPICTIDVYMIMVK 8 2 2, 3 - 64 201

LLDIDETEYHADGGK 9 2 2, 3 - 54 27

AVTSANIQEFAGCK 10 2 2 - 53 -

GLQSLPTHDPSPLQR 11 2 2, 3 - 47 23

SGGGDLTLGLEPSEEEAPR 17 2 2 - 21 -

LGSQDLLNWCMQIAK 20 2 2 - 18 -

GIWIPDGENVKIPVAIK 41 2 1, 2 1 2 -

AVTSANIQEFAGCKK 44 2 2, 3 - 1 6

NPHQALLHTANRPEDECV

GEGLACHQLCAR - 3 - - - -

118

Su

pp

lem

enta

ry T

ab

le S

2-2

: 20 m

ost

abundan

t pro

tein

s in

SK

BR

3 c

om

par

ed w

ith t

ransc

ripto

mic

s dat

a of

SK

BR

3 a

nd p

rote

om

ic d

ata

of

SU

M149 a

nd S

UM

190

Pro

tein

na

me

Gen

e

sym

bo

l C

hr a

S

tart

a

En

d a

Siz

e a

Ban

d a

Pro

teom

ics

data

b

Tra

nsc

rip

tom

ics

Data

d

SK

BR

3

SU

M1

90

S

UM

149

EN

OA

E

NO

1

1

89

21

06

1

89

3930

8

18

247

1p

36.2

3

4 (

758

) c

1 (

1073)

1 (

661

) 4

82

FL

NB

F

LN

B

3

57

99

41

27

58

1579

82

16

3855

3p

14.3

6

(6

35

) 4

20

(9

) 3

3 (

154)

11

H4

H

IST

1H

4A

6

2

60

21

907

26

0222

78

37

1

6p

22.2

1

5 (

306)

36

(1

53)

55

(9

0)

3

TB

B5

T

UB

B

6

30

68

79

78

30

6932

03

52

25

6p

21.3

3

17

(2

70)

21

(2

35)

17

(2

56)

20

9

HS

90B

H

SP

90

AB

1

6

44

21

48

24

44

2216

20

67

96

6p

21.1

2

0 (

265)

22

(2

32)

6 (

397

) 1

73

AC

TB

A

CT

B

7

55

66

77

9

56

0341

5

36

636

7p

22.1

8

(4

99

) 5

(5

45

) 4

(4

76

) 1

39

1

EP

IPL

E

PP

K1

8

1

44

93

58

22

14

4952

632

16

810

8q

24.3

9

(4

61

) 9

9 (

54)

54

9 (

7)

4

PL

EC

1

PL

EC

8

1

44

98

93

21

14

5050

913

61

592

8q

24.3

1

4 (

332)

30

8 (

16)

53

(9

1)

16

HS

P7

C

HS

PA

8

11

1

22

92

81

97

12

2933

938

5

74

1

11q

24.1

1

6 (

286)

18

(2

54)

11

(302

) 4

9

G3P

G

AP

DH

1

2

66

43

09

3

66

4753

7

44

44

2p

13.3

1

7 (

522

) 6

(4

95

) 2

(5

39

) 2

96

6

TB

A1B

T

UB

A1

B

12

49

52

15

65

49

5253

04

37

39

12

q13.1

2

19

(2

66)

11

(3

12

) 1

2 (

298)

23

1

K2C

7

KR

T7

1

2

52

62

69

54

52

6427

09

15

755

12

q13.1

3

12

(3

80)

4 (

634

) 9

5 (

50)

65

5

K2C

8

KR

T8

1

2

53

29

09

71

53

298868

78

97

12

q13.1

3

5 (

655

) 1

5 (

286)

18

7 (

28)

20

25

K1C

18

K

RT

18

1

2

53

34

26

55

53

3466

85

40

30

12

q13.1

3

13

(3

34)

40

(1

31)

23

6 (

21)

14

70

EN

OB

E

NO

3

17

48

51

38

7

48

6042

6

90

39

17

p13.2

1

0 (

458)

Not

ID

7 (

355

) 2

K1C

19

K

RT

19

1

7

39

67

98

69

39

6846

41

47

72

17

q21.2

2

(1

011)

3 (

838

) 2

00

(26)

14

62

FA

S

FA

SN

1

7

80

03

62

14

80

0561

06

19

892

17

q25.3

1

(2

738)

2 (

854

) 1

5 (

263)

14

0

EF

2

EE

F2

1

9

39

76

05

4

39

8546

1

94

07

19

p13.3

1

8 (

266)

13

(3

05)

9 (

324

) 2

53

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Supplemental figures and tables. This material is available free of charge via the Internet at

http://pubs.acs.org/doi/suppl/10.1021/pr4001527.

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Chapter 3 Identification of ErbB2 Isoforms from SKBR3

Cell Lysate by Immunoprecipitation and Liquid

Chromatography - Tandem Mass Spectrometry (LC-

MS/MS)

Contributions:

Cell line preparation was carried out by our collaborators in Fred Hutchinson Cancer Institute.

RNA-Sequencing analysis was performed by Rajasree Menon, Hogune Im, and Michael P.

Snyder. My contribution was the experiment design and perform, data analysis and manuscript

preparation.

Publication:

Emma Yue Zhang, Rajasree Menon, Hogune Im, and Michael P. Snyder, Shiaw-lin Wu, William

S. Hancock, “Identification of ErbB2 Isoforms from SKBR3 Cell Lysate by Immunoprecipitation

and Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)”. Manuscript in

preparation.

125

3.1 Abstract

Overexpression of epidermal growth factor receptor 2 (ERBB2) presents in about 25% of all

breast cancer patients and has been associated with poor prognosis and higher metastases.

Several different ErbB2 isoforms have been identified, including the normal full-length

transmembrane forms, soluble forms, as well as truncated forms. Currently Trastuzumab is the

most widely applied monoclonal antibody drug for the treatment of ErbB2 positive breast cancer.

However, the resistance of Trastuzumab has been shown to be related to the soluble and

truncated forms of ErbB2. Therefore the identification of different ErbB2 isoforms may

potentially be applied in the evaluation of drug efficacy of Her-targeted mAbs. In this chapter,

we investigated the ErbB2 isoforms in SKBR3 cell line. First we studied the spent medium of

SKBR3, and no circulated ErbB2 was identified. We then focused on the cell lysate, and two

ErbB2 isoforms were identified in SKBR3 cell lysate by the combination of immunoprecipitation

(IP) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The two ErbB2

isoforms have 1240 and 1255 amino acid residues, respectively. The proteomics result shows

agreement with RNA-Sequencing results. Since most ErbB2 isoforms shared identical primary

structure, we found it difficult to identify different isoforms using bottom-up proteomics strategy.

In the future studies, Multiple-Reaction Monitoring (MRM) based method, or top-down

proteomics will be preferably developed to study protein isoforms.

3.2 Introduction

Amplification of human epidermal growth factor receptor 2 (ERBB2) exists in about 25% of all

breast cancer cases.1-2 Overexpression of ErbB2 has been associated with inferior diagnosis,

126

more aggressive disease forms, higher metastases, and shorter overall survival-rate.3-4 Like the

other family members of epidermal growth factor receptors, an ErbB2 molecule consists of three

domains: an extracellular domain (ECD), a transmembrane domain (TMD), and an intracellular

domain (ICD).5 In addition to the full length form of ErbB2, soluble ErbB2 isoforms (sErbB2)

and proteolytically truncated ErbB2 (tErbB2) isoforms are also present in normal or malignant

cells and serum. In sErbB2, TMD and ICD are missing; moreover, sErbB2 may undergo further

proteolytic cleavage or alternative RNA processing to generate tErbB2 isoforms.6-9

Therapeutic monoclonal antibodies (mAbs) have become an attractive approach in cancer

treatment because of their capability to selectively target tumor cells and trigger subsequent

responses.10 Trastuzumab (Trade name: Herceptin; developed and marketed by Genentech) is the

first mAb drug approved by the United States Food and Drug Administration in 1998 for the

treatment of ErbB2 positive breast cancer. It was engineered by inserting the complementary

determining regions of a murine antibody Mab4D5 into the constant domain of human IgG1. It

can target the ECD of ErbB2, and therefore decreases the concentration of ErbB2 at the

membrane and prevents ErbB2 dimerization.11 The tErbB2, which preserves ECD in the

molecule, is still capable of bind Trastuzumab. Elevated tErbB2 level in serum has been shown

to be one of the main reasons responsible for Trastuzumab resistance.12 Therefore, the emerging

study of the identification of different ErbB2 isoforms holds promise for understanding the

functions of sErbB2 and tErbB2 in clinical samples.

Antibody-capture method combined with liquid chromatography– tandem mass spectrometry

(LC-MS/MS) has been shown to be an effective approach to identify protein splice isoforms.13-15

Wu et al. has characterized the secreted form of epidermal growth factor receptor by the

combination of immunoprecipitation and LC-MS/MS analysis.16 Here, a similar approach has

127

been applied to explore the isoforms of ErbB2 in SKBR3 cell lysate. ErbB2 and its potential

alternative splice isoforms are first enriched by anti-ErbB2 mAbs (Trastuzumab or pertuzumab)

through an optimized experimental condition. The proteins from IP eluents are digested into

tryptic peptides and subjected for LC-MS/MS analysis, and the resulting MS spectra are searched

against a self-built ErbB2 isoform database. Two different ErbB2 isofroms, ENSP00000446466

and ENSP00000462438, are identified.

3.3 Experiments

3.3.1 Material

Two recombinant anti-ErbB2 antibodies were used in this study and both of them were provided

by Genentech (South San Francisco, CA). The first one is Trastuzumab, a liquid formulation

product with a concentration of 22 μg/μL; the second antibody is pertuzumab, a liquid

formulation product with a concentration of 5 μg/μL.

The human breast cancer cell line SKBR3 (ER/PR-, ERBB2+, metastatic pleural effusion), was

obtained from the American Type Culture Collection (Manassas, VA) and maintained in culture

with DMEM/F-12 medium supplemented with 10% FBS (Tissue Culture Biologicals, Seal Beach,

CA) and 1% of Antibiotic-Antimycotic 100X (Gibco, Carlsbad, CA).

Ammonium bicarbonate, formic acid (FA), dithiothreitol (DTT), and iodoacetamide (IAA) were

purchased from Sigma-Aldrich (St. Louis, MO). Trypsin (sequencing grade) was obtained from

Promega (Madison, WI). LC-MS grade water and HPLC grade acetonitrile were purchased from

J.T. Baker (Phillipsburg, NJ). NuPAGE® MES SDS Running Buffer (20X, 500 ml), NuPAGE®

LDS Sample Buffer (4X, 10 ml), Novex® Sharp Unstained Protein Standard, NuPAGE® Novex

128

4-12% Bis-Tris Gel (1.5 mm, 10 Well), SimplyBlue™ SafeStain,and Dynabeads® Protein A for

Immunoprecipitation (5 ml) were obtained from Invitrogen (Carlsbad, CA).

3.3.2 ErbB2 immunoprecipitation (IP) with anti-ErbB2 antibodies

The ErbB2 IP includes two parts of experiments, the cross linking of anti-ErbB2 to protein A

magnetic beads, followed by ErbB2 IP from breast cancer cell lysate. The procedure is described

in the following. Protein A magnetic beads (100 μL) were firstly activated by washing with 500

μL Na-phosphate buffer (0.1 M, pH 8) for three times, and then anti-ErbB2 antibody

(Trastuzumab or pertuzumab) was added and tumbled overnight at 4 °C. The bead-antibody

complex was washed with 1 mL PBS-NP40 buffer (0.1% NP40, pH 7.4) twice and then with 1

mL triethanolamine buffer (0.2 M, pH 7.4) twice, followed by 1 h incubation in 1 mL of 20 mM

DMP (prepared in 0.2 M dimethyl pimelimidate dihydrochloride, pH 8.2) at room temperature.

In this step, anti-ErbB2 antibody is chemically attached to the protein A beads. This reaction can

be stopped by transferring the antibody-bead complex in 1 mL tris buffer (50 mM, pH 7.5) and

tumbling for 15 minutes at room temperature. The antibody cross-linked beads are washed by 1

mL PBS-NP40 buffer (0.1% NP40, pH 7.4) for three times.

For ErbB2 immunoprecipitation, 1 mL cell lysate was added into the previous anti-ErbB2 cross-

linked beads and incubated at 4 °C for 1 hour. After incubation, all the supernatant was

transferred and kept at 4 °C for further analysis. This part of fluid is named as ‘flow-through

sections’ in the following text. The antibody cross-linked beads were washed with 1 mL of PBS-

NP40 buffer (0.1% NP40, pH 7.4) for three times and then with 1 mL NaCl (1M, pH 7.0) once.

To elute ErbB2 from anti-ErbB2 cross-linked beads, two methods were applied: (1) adding 60 μL

citrate buffer (0.1 M, pH 2.0) to the beads; (2) boiling the beads with 60 μL of 2% SDS buffer

129

(prepared in 50 mM NH4HCO3) at 90 °C for 15 minutes. The eluent was concentrated to about

20 μL in speed vacuum for the subsequent SDS-PAGE separation.

3.3.3 SDS-PAGE

Around 20 μL eluent from immunoprecipitation was mixed with 5 μL NuPAGE LDS Sample

Buffer (4X), 2 μL 1M DTT, and 1 μL NuPAGE MES SDS Running Buffer (1X). The protein

mixture was reduced by 15 min incubation at 90 °C and then separated by 4-12% Bis-Tris gel

and stained with SimplyBlue™ SafeStain.

3.3.4 In gel tryptic digestion

Gel bands containing the expected ErbB2 were cut out from gel and further excised into small

pieces. Gel pieces were destained by 2 to 3 cycles of alternating washing by acetonitrile and

ammonium bicarbonate buffer (0.1 M, pH 8) as described in following. The small gel pieces

were first washed by 300 μL HPLC water by shaking for 15 min. All liquid was removed, and

300 μL acetonitrile was added to dehydrate gel pieces by vortex for 30 sec and shaking for 15

min. After the acetonitrile was removed, the gel pieces were dried in speed vacuum for 5 min and

then rehydrated with 300 μL of 0.1 M NH4HCO3 for 10 min. The sample was vortexed in 300 μL

of acetonitrile to dehydrate and centrifuged to remove liquid. This procedure was repeated three

times or more until no visible Coomassie blue stain remained. The sample was reduced by

incubation with 200 μL DTT (10 mM, prepared in 0.1 M NH4HCO3) at 56 °C for 30 min, then

successively alkylated by incubation with 200 μL IAA (55 mM, prepared in 0.1 M NH4HCO3) in

the dark for 80 min at room temperature. The gel slices were dehydrated by 300 μL acetonitrile

130

and dried in speed vacuum, and then incubated in 150 μL of trypsin buffer (10 ng/μL trypsin in

50 mM NH4HCO3 and 5 mM CaCl2, pH 8) at 4 °C for 30 min. After that, 50 μL NH4HCO3 (25

mM, pH 8) was added to cover gel pieces, and the sample was incubated at 37 °C overnight. The

supernatant was removed and kept. The remaining gel pieces were extracted with 200 μL

acetonitrile and 30 μL 5% formic acid and shaken for 15 min for three times. Each mixture of

acetonitrile and formic acid, containing digested tryptic peptides, was combined with the

previous supernatants, and then concentrated to about 10 μL in speed vacuum before subsequent

LC-MS analysis.

3.3.5 LC-MS analysis

The tryptic in-gel digested peptides were analyzed by Dionex nano liquid chromatography

(Ultimate 3000, Sunnyvale, CA) using a linear ion trap coupled to a Fourier transfer mass

spectrometer (LTQ-FT MS, Thermo Electron, San Jose, CA). A self-packed column (75 μm ID x

15 cm, Magic C18, 200 Å pore, particle size = 5 μm) was used for LC separation. The LTQ-FT

mass spectrometer was operated in the data-dependent mode. The first survey scanned from m/z

400 to 2000, followed by nine sequential LTQ-MS/MS scans throughout 90 minutes gradient.

Mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in

acetonitrile. The LC gradient was from 2% B to 60% B in 60 min, and then from 60% B to 80%

B in 10 min, and keep at 80% B for 10 min.

3.3.6 Data analysis

ErbB2 was immunoprecipitated from SKBR3 cell lysate. The eluent was separated by SDS-

131

PAGE, and the gel sections containing ErbB2 were cut for in-gel trypsin digestion. The tryptic

peptides were analyzed by LC-MS, followed by peptide identification by two software,

Bioworks 3.3.1 and Proteome Discoverer 1.2. The database used for peptide identification was

human SP 56.5 database with full trypsin specificity and up to three missed cleavages. The m/z

tolerance was 50 ppm for precursor ions, and 0.8 Da for product ions. Static modification was

carbamidomethylation for cysteine, and dynamic modification was set as asparagine deamidation.

Peptides were identified with Xcorr scores above the following thresholds: ≥3.8 for 3+ and

higher charge state ions, ≥2.2 for 2+ ions, and ≥1.9 for 1+ ions.

3.3.7 RNA-Seq Measurement

Strand-specific RNA-Seq libraries were prepared and sequenced on a lane of the Illumina HiSeq

2000 instrument per sample to obtain transcript data17. All RNA-Seq data are available at Short

Read Archive (SRS366582, SRS366583, SRS366584, SRS366609, SRS366610, SRS366611).

From total RNA, strand-specific RNA-Seq libraries were prepared according to Illumina TruSeq

standard procedures and sequenced at both ends (paired-end RNA-sequencing) on Illumina

HiSeq 2000. Tophat embedded with Bowtie was used to align the sequence reads to human

genome (hg19). Using Cufflinks, the alignments were assembled into gene transcripts (NCBI

build 37.2) and their relative abundances (RPKM) were calculated.

3.4 Results

We have previous analyzed SKBR3 cell lysate and spent medium samples (data not shown). In

SKBR3 cell lysate, ErbB2 was only identified in the SDS-PAGE gel band of molecular weight

132

above 110 kDa, and no shorter form of ErbB2 was identified in the gel bands of lower molecular

weight. Moreover, no ErbB2 peptide was identified in the SKBR3 spent medium. This indicates

that there is no circulated ErbB2 form in SKBR3 cell line. Therefore, we have focused on the

identification of transmembrane ErbB2 variants in SKBR3 lysate.

3.4.1 SDS-PAGE gel image

The eluent from ErbB2 immnoprecipitation was first separated by SDS-PAGE to further isolate

ErbB2 from other co-eluted proteins. Figure 3-1 shows the gel image of the elution and flow-

through from ErbB2 IP from SKBR3 cell lysate by pertuzumab antibody. Here, compared to a

typical 45 min run under 200 V, a longer running time was applied for further separation of

ErbB2 from other protein mixture. For the eluent fraction, two gel bands can be seen near the

position of ErbB2 (~140 kDa). It could be possible that two different forms of ErbB2 were

enriched from the cell lysate, and we will demonstrate it in the following sections. Both upper

and lower bands were cut out from the elution at the same molecular weight positions for in-gel

trypsin digestion and LC-MS analysis. The gel bands at the same position of the flow-through

fractions were also analyzed side by side for comparison.

133

kDa

40

30

20

15

260

160

110

80

60

50

standard ElutionFlow-

through

Band 1

Band 2

Band 3

Band 4

kDa

40

30

20

15

10

260

160

11080

60

50

standard elution flow-through

Band 1

Band 2

Band 3

Band 4

Figure 3-1: SDS-PAGE gel images of eluents and flow-through from ErbB2

immunoprecipitation using different antibodies. Left: 100 μg Trastuzumab; middle: 20 μg

Trastuzumab; right: 10 μg pertuzumab.

3.4.2 Efficiency of ErbB2 immunoprecipitation

The efficiency of ErbB2 enrichment was examined after each IP experiment in order to optimize

the IP conditions. In each IP experiment, raw data were searched against human database. The

resulting protein list includes all the proteins identified in the protein mixture from each gel

section. The abundance of ErbB2 in the protein mixture can be estimated from the following

numbers: spectral counts of ErbB2 and the ranking of ErbB2 relative to all protein identified.

Higher spectral counts and ranking indicate that ErbB2 was more effectively enriched from

SKBR3 cell lysate, as shown in Table 3-1.

Table 3-1 lists experiment results of six IP trials. In the first trial, ErbB2 was enriched by 10 μg

of pertuzumab and was eluted from magnetic beads at 70 °C for 10 minutes with a satisfactory

result. ErbB2 was the second most abundant protein in the protein mixture from upper section of

eluents, wherein protein with the highest abundance was fatty acid synthase (FASN). The

134

expression of FASN has been reported to be amplified as a result of ErbB2 activation and its

downstream signaling.18 Here, co-expression of FASN and ErbB2 has been observed in SKBR3

cell lysate in every IP trial. Though longer time period was applied for SDS-PAGE running,

separation of FASN and ErbB2 cannot be accomplished. The spectral counts of ErbB2 in elute

gel sections were relatively high, compared to those in flow-through gel sections.

The same method was repeated in IP trial using Trastuzumab, as trial 2 and 3 in Table 2-1. The

efficiency of IP was observed to be decreased significantly. The spectral counts of ErbB2 were

relatively low. For example, although the same experiment conditions were applied, only 18

peptides were hit for ErbB2 in the upper gel section of elution.

From Trial 5, elevated temperature was used for eluting binding proteins from protein A beads,

which were boiled with 2% SDS buffer (prepared in 50 mM NH4HCO3) at 90 °C for 15 minutes.

The raised temperature cannot only elute ErbB2 and other co-IP proteins, but also break the

covalent bonding between anti-ErbB2 antibodies and protein A. The eluted antibodies

contributed to gel bands around 50 kDa in Figure 3-1. Under elution at 90 °C, higher spectral

counts of ErbB2 were able to be recovered for both IP even with limited amounts of

Trastuzumab (e.g. 20 μg in trial 5). By increasing Trastuzumab amount using in IP in Trial 6, the

peptide hits of ErbB2 also augmented in both upper and lower gel sections of elution. The

number in flow-through gel sections still remained at low level. The IP conditions have been

optimized and will be applied in future projects.

135

135

Tab

le 3

-1:

Eff

icie

ncy

of

Erb

B2 e

nri

chm

ent

Tri

al n

um

ber

1

2

3

4

5

6

Anti

body

per

tuzu

mab

T

rast

uzu

mab

T

rast

uzu

mab

per

tuzu

mab

T

rast

uzu

mab

T

rast

uzu

mab

Am

ount

(μg)

10

100

100

10

20

200

Elu

tion c

ondit

ion

Hea

t at

70 °

C f

or

10 m

in

Hea

t at

90 °

C f

or

10 m

in

Elu

ents

Upper

ban

d

SC

a

131

55

26

18

163

291

Ran

kin

g b

2/1

00

2/1

38

2/7

2

2/5

6

2/9

2

3/5

7

Cover

age

(%)

c 44.8

50.3

26.0

18.4

37.9

35.8

# o

f in

tera

tants

d

6

8

8

4

6

4

Low

er

ban

d

SC

47

32

12

11

94

269

Ran

kin

g

5/1

70

6/2

49

2/7

7

3/5

0

2/1

23

2/9

4

Cover

age

(%)

c 23.4

30.6

12.5

11.5

33.9

32.6

# o

f in

tera

tants

d

11

13

5

5

6

9

Flo

w-

thro

ugh

Upper

ban

d

SC

a

66

22

NA

12

22

16

Ran

kin

g b

17/2

24

21/2

55

N

A

30/2

42

30/2

39

44/1

36

Cover

age

(%)

c 33.7

26.0

N

A

13.1

10.4

5.7

# o

f in

tera

tants

d

7

9

NA

10

8

7

Low

er

ban

d

SC

0

NA

N

A

4

3

0

Ran

kin

g

- N

A

NA

146/3

05

148/2

70

-

Cover

age

(%)

c -

NA

N

A

4.1

2.1

-

# o

f in

tera

tants

d

14

NA

N

A

18

15

11

a S

C =

spec

tral

count

b R

ankin

g =

ra

nk o

f E

rbB

2/a

ll p

rote

in i

den

tifi

ed i

n t

his

gel

sec

tion b

ased

on i

den

tifi

cati

on s

core

c C

over

age

= p

rote

in c

over

age

of

Erb

B2

d #

of

inte

rata

nts

= n

um

ber

s of

Erb

B2 i

nte

rata

nts

iden

tifi

ed i

n t

he

gel

ban

ds

(Erb

B2 i

nte

rata

nts

wer

e re

trie

ved

fro

m

htt

p:/

/gen

ecar

ds.

org

/cgi-

bin

/car

ddis

p.p

l?gen

e=E

RB

B2&

rf=

/ho

me/

gen

ecar

ds/

curr

ent/

web

site

/car

ddis

p.p

l&in

tera

ctio

ns=

226&

sear

ch=

erbb%

202#in

tera

ctio

ns)

http://genecards.org/cgi-bin/carddisp.pl?gene=ERBB2&rf=/home/genecards/current/website/carddisp.pl&interactions=226&search=erbb%202#interactions

http://genecards.org/cgi-bin/carddisp.pl?gene=ERBB2&rf=/home/genecards/current/website/carddisp.pl&interactions=226&search=erbb%202#interactions

136

3.4.3 Overall ErbB2 coverage

Here the most common form of ErbB2 is used to shown the ErbB2 coverage of IP experiments.

This isoform of ErbB2, listed as isoform 1 in Uniprot website (http://www.uniprot.org/uniprot/

P04626), contains 1,255 amino acids, with four confirmed (Asn68, 259, 530, and 571) and three

potential (Asn124, 187, and 629) N-glycosylation sites. This isoform was used as standard to

show the ErbB2 coverage as shown in Table 3-2. 815 of 1,255 amino acid residues were

identified in the combined results of six IP trials, and this number contributes to 65% coverage of

ErbB2 isoform 1. Among the seven glycosylation sites, only Asn187 and Asn530 were able to be

identified in trypsin digestion. Identification of the other glycosylation sites may depend on

digestion with Peptide -N-Glycosidase F (PNGaseF) to remove the N-linked glycans in order to

reduce peptide mass to appropriate sizes. We will investigate these N-glycosylated sites in

further studies.

3.4.4 Identification of different ErbB2 isoforms

A database of ErbB2 isoforms was built based on Ensembl website for the purpose of identifying

different ErbB2 isoforms in gel bands as shown in Figure 3-1. The sequence information used in

this database was retrieved from Ensemble website, where fourteen different ErbB2 splice

variants were listed as protein coding genes as listed in Table 3-2.

137

Table 3-2: Protein coding splice variants of ErbB2

This information is retrieved from Ensembl website: http://useast.ensembl.org/Homo_sapiens/

Gene/Family?g=ENSG00000141736

Transcript ID Length (bp) Protein ID Length (aa) CCDS

ENST00000269571 4545 ENSP00000269571 1255 CCDS32642

ENST00000541774 4341 ENSP00000446466 1240 -

ENST00000406381 4806 ENSP00000385185 1225 CCDS45667

ENST00000584601 4792 ENSP00000462438 1225 CCDS45667

ENST00000540147 4624 ENSP00000443562 1225 CCDS45667

ENST00000584450 3730 ENSP00000463714 1055 -

ENST00000445658 3238 ENSP00000404047 979 -

ENST00000578199 2526 ENSP00000462808 603 -

ENST00000540042 2147 ENSP00000446382 603 -

ENST00000580074 754 ENSP00000463002 251 -

ENST00000582818 529 ENSP00000464252 177 -

ENST00000578502 497 ENSP00000464420 166 -

ENST00000584099 583 ENSP00000462270 139 -

ENST00000578709 559 ENSP00000463719 102 -

The ErbB2 isoforms listed in Table 3-2 are encoded from different translations. For example, the

first protein listed in Table 3-2, ENSP00000269571, which is ErbB2 isoform 1 discussed in the

previous text, is encoded by 27 exons, while ENSP00000385185 is encoded by 29 exons.

However, it is possible that proteins encoded from different numbers of exons share identical

primary sequence. For example, ENSP00000385185, ENSP00000462438, and

ENSP00000443562 share the identical primary sequence while they are encoded from different

translations. The 1255 and 1225 amino acid variants of the twelve ErbB2 isoforms listed in this

table have been accepted in the consensus coding sequence (CCDS) database, which represents

high quality and consistently annotated protein coding regions of human and mouse genome.

In Uniprot, four different forms are listed for ErbB2 kinase: besides the 1255 and 1240 amino

acid variants in Table 3-2, there are other two variants provided by Uniprot, containing 645 and

138

569 amino acids, respectively. Since no short version of ErbB2 was identified in SKBR3 lysate,

the two Uniprot isoforms are unlikely to be discovered in this study.

Figure 3-2: Protein coverage of the two ErbB2 isoforms identified.

Green bars represent the peptides identified in the proteomics experiments, and white bars show

the missing parts of ErbB2.

An ErbB2 isoform database has been built to identify the various forms of ErbB2 present in

SKBR3 cell lysate. It should be noted that ENSP00000462438, ENSP00000385185, and

ENSP00000443562 have an identical primary sequence. In Ensembl, each transcript is correlated

with a unique protein identifier. The raw data of the eluents and flow-through fluids from ErbB2

IP trials were searched against the ErbB2 isoform database. Since the isoforms listed in Table 3-2

share a common primary sequence except for additional N-terminal residue for

ENSP00000269571 and ENSP00000446466 (containing 1255 and 1240 amino acid residues), it

is important to detect unique peptides in mass spectrometry for the identification of one specific

isoform. Here, two forms of ErbB2 were identified by proteomics from SKBR3 cell lysate,

which corresponds to 1240 (ENSP00000446466) and 1225 (ENSP00000385185,

ENSP00000462438, and ENSP00000443562) amino acids variants (The sequences are shown in

Figure 3-3.). In upper gel sections (shown as Band 1 in Figure 3-1), only the 1240 isoform

(ENSP00000446466) was identified. However, two different isoforms, the 1240 and 1224

139

isoforms, were present in lower gel sections (shown as Band 2 in Figure 3-1). Figure 3-4 shows

the identification of unique peptides in ENSP00000446466. The RNA-Sequencing of SKBR3

cell lysate was fulfilled by our collaborator. As shown in Table 3-3, five ErbB2 variants were

identified from RNA-Seq data of SKBR3 cell lysate.

MPRGSWKPQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSF

LQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTG

ASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRS

RACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGP

KHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLS

TDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQE

FAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPD

LSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHT

VPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRG

QECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPF

CVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTS

IISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQ

MRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILD

EAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCM

QIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVP

IKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQ

PPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLD

STFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDL

TLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPT

VPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSP

GKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAP

PSTFKGTPTAENPEYLGLDVPV

Figure 3-3: Comparison of the primary sequences of the two ErbB2 isoforms identified.

The red sequence shows the unique peptide of ENSP00000446466, and the black part is the

identical sequence shared by the ErbB2 isoforms that contain 1240 and 1255 amino acid residues.

The identification of the peptide underlined in green color is shown in Figure 3-4.

140

830.5 831.0 831.5 832.0 832.5 833.0 833.5 834.0 834.5 835.0m/z

0

20

40

60

80

100

Re

lati

ve A

bu

ndan

ce

832.1887832.4395

832.6888831.9374 832.9417

46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76Time (min)

0

20

40

60

80

100R

ela

tive

Ab

und

ance

57.81

300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000m/z

0

20

40

60

80

100

Re

lati

ve A

bu

ndan

ce 818.86

791.19

651.27

597.16 845.50944.38

1027.97

370.94423.47

1045.56y134+,y9

3+

y113+

y214+

[b12-H2O]2+

[y27-H2O]4+

y142+

b152+

b8+

[b9-H2O]+

[b28-H2O]3+

G-S-W-K-P-Q-V-C-T-G-T-D-M-K-L-R-L-P-A-S-P-E-T-H-L-D-M-L-R

y9y11y13y21

b15b12

y27 y14

b8 b9 b28

NL: 1.29E4

MS (FT)

LC-MS

A

B 4+Theoretical m/z 831.9223

MS 2 (Ion Trap)

C

Figure 3-4: Identification of the unique peptide of ENSP00000446466

Part A. Extracted chromatography of precursor ion; Part B. MS pattern of precursor ion; Part C.

assignment of MS/MS fragmentation

Table 3-3: ErbB2 variants identified from RNA-Sequencing data of SKBR3 cell lysate

Transcript ID Protein ID RPKM Corresponding

ErbB2 length (aa)

ENST00000269571 ENSP00000269571 47 1255

ENST00000541774 ENSP00000446466 658 1240

ENST00000540147 ENSP00000443562 6 1225

ENST00000406381 ENSP00000385185 1 1225

ENST00000582818 ENSP00000464252 1 177

141

3.5 Conclusion

In this chapter, ErbB2 was enriched from SKBR3 cell lysate by immunoprecipitation with

Trastuzumab and pertuzumab anti-ErbB2 monoclonal antibodies. The experiment conditions

have been optimized to improve IP efficiency. Elevated temperature and increased time period

during the elution of IP greatly helped to enrich ErbB2 from SKBR3 cell lysate more effectively.

These conditions will be applied in further projects. Different ErbB2 isoforms were identified by

LC-MS/MS using a self-built ErbB2 isoform database retrieved from the Ensembl website. In the

future study, Multiple-Reaction Monitoring (MRM) will be used to detect the low abundant

peptides from different ErbB2 isoforms.

3.6 References

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2. Slamon, D. J.; Godolphin, W.; Jones, L. A.; Holt, J. A.; Wong, S. G.; Keith, D. E.; Levin,

W. J.; Stuart, S. G.; Udove, J.; Ullrich, A.; et al., Studies of the HER-2/neu proto-oncogene in

human breast and ovarian cancer. Science 1989, 244 (4905), 707-12.

3. Slamon, D. J.; Leyland-Jones, B.; Shak, S.; Fuchs, H.; Paton, V.; Bajamonde, A.; Fleming,

T.; Eiermann, W.; Wolter, J.; Pegram, M., Use of chemotherapy plus a monoclonal antibody

against HER2 for metastatic breast cancer that overexpresses HER2. New Engl J Med 2001, 344

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4. Vogel, C. L.; Cobleigh, M. A.; Tripathy, D.; Gutheil, J. C.; Harris, L. N.; Fehrenbacher, L.;

Slamon, D. J.; Murphy, M.; Novotny, W. F.; Burchmore, M., Efficacy and safety of Trastuzumab

as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Clin

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5. Lafky, J. M.; Wilken, J. A.; Baron, A. T.; Maihle, N. J., Clinical implications of the

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7. Doherty, J. K.; Bond, C.; Jardim, A.; Adelman, J. P.; Clinton, G. M., The HER-2/neu

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8. Zabrecky, J.; Lam, T.; McKenzie, S. J.; Carney, W., The extracellular domain of p185/neu

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9. Pupa, S.; Menard, S.; Morelli, D.; Pozzi, B.; De Palo, G.; Colnaghi, M., The extracellular

domain of the c-erbB-2 oncoprotein is released from tumor cells by proteolytic cleavage.

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10. Reichert, J. M.; Valge-Archer, V. E., Development trends for monoclonal antibody cancer

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11. Baselga, J.; Albanell, J., Mechanism of action of anti-HER2 monoclonal antibodies. Ann

Oncol 2001, 12 (suppl 1), S35-41.

12. Nahta, R.; Esteva, F. J., Molecular mechanisms of Trastuzumab resistance. Breast Cancer

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13. Portelius, E.; Hansson, S. F.; Tran, A. J.; Zetterberg, H.; Grognet, P.; Vanmechelen, E.;

Höglund, K.; Brinkmalm, G.; Westman-Brinkmalm, A.; Nordhoff, E., Characterization of tau in

cerebrospinal fluid using mass spectrometry. J Proteome Res 2008, 7 (5), 2114-20.

14. Makinen, T.; Olofsson, B.; Karpanen, T.; Hellman, U.; Soker, S.; Klagsbrun, M.; Eriksson,

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143

Chapter 4 Structural Characterization of Two Zybody

Candidates by Liquid Chromatography Coupled with Online

Tandem Mass Spectrometry (LC-MS) Analysis

Contributions:

Zybody samples and the anti-Zybodies were provided by Dr. Rajesh Krishnamurthy. The

experiments were designed and carried out by Emma Yue Zhang. Dr. Shiaw-Lin Wu and Dr.

William S. Hancock were taken part in experiment design.

Publication:

Emma Yue Zhang, Rajesh Krishnamurthy, Shiaw-Lin Wu, William S. Hancock.

Pharmacokinetics and Metabolism Study of Zybodies by Liquid Chromatography Coupled with

Mass Spectrometry (LC-MS). Manuscript in preparation.

144

4.1 Abstract

Monoclonal antibodies (mAbs) have gained great interest in the treatment of cancer during the

past decade. More than ten mAb drugs have been approved by the US FDA for cancer therapy

since the first marketing approval for Herceptin in 1998. Because of the complicated

manufacturing process for mAb drugs, various modifications could occur and therefore introduce

heterogeneity and potential instability into the final products. Consequently, a comprehensive

characterization of mAb drugs is necessary to track the functionality-sensitive modifications and

monitor possible structural changes in mAbs. In this study, two Zybody molecules, which belong

to a new family of therapeutic bi- or multi-specific mAbs, were characterized by LC-MS based

approaches. Full sequence coverage was achieved for both Zybody candidates using multi-

enzyme digestion strategies. The stability of bi-specific binding sites in two molecules were

accessed and compared, and a better candidate was selected for further pharmacokinetics study.

Besides common modifications, including disulfide bond linkages, formation of N-terminal

pyroglutamic acid, oxidation of methionine, deamidation of asparagine, isomerization of aspartic

acid, and glycosylation were identified using different LC-MS platforms. The extent of common

modifications were determined and compared between the two Zybody molecules.

145

4.2 Introduction

Monoclonal antibody (mAb) and its related products are one of the most increasing therapeutic

agents for oncology because of its advantages in high specific binding, long half-time, and

abilities to activate immune response.1 Currently, there are more than ten mAb drugs approved

by the US FDA for cancer therapy,2-3 among which are three approved mAbs for targeting Her2:

Trastuzumab, Pertuzumab, and Trastuzumab emtansine. Trastuzumab binds the C-terminus of

domain IV of Her2 and blocks the homodimerization of Her2,4 as well as the subsequent cell

signaling for cell growth. Pertuzumab, approved by the FDA in 2012, is designed to bind to the

junction position of domain I, II and IV of Her25 and therefore is able to block the

heterodimerization of Her2 and Her3. Combination of Pertuzumab and Trastuzumab has shown

to be more effective in the treatment of patients with Her2 positive metastatic breast cancer.

Patients who received a combined treatment of Pertuzumab plus Trastuzumab plus docetaxel

have a longer median progression-free survival in comparison to patients who only are treated

with Trastuzumab plus docetaxel.6

In recent years, bi-specific antibody drugs have emerged as a promising agent for cancer

therapy.7 A bi-specific antibody comprises two different targeting properties in one molecule.

Compared to traditional mono-specific mAbs, bi-specific antibodies have an advantage in higher

tumor cell selectivity and concurrent binding of two antigens, which can yield higher drug

efficacy.7-8 One example is Catumaxomab, which is an anti-EpCAM x anti-CD3 bi-specific mAb

drug and was approved by the European Union in April 2009 for the intraperitoneal treatment of

patients with malignant ascites.9 Preclinical studies have shown that Catumaxomab was able to

improve the stimulation of patient’s immune system against the tumor, and ongoing clinical

146

studies had applied Catumaxomab in the treatments of various carcinomas such as lung cancer

and ovarian cancer.10-11

Zybody, a new technology developed by Zyngenia Inc., is one of the next-generation antibody

therapeutics that is designed to target two or multiple targets (up to five) of cancers and

autoimmune disorders. A Zybody molecule consists of a full-length mAb fused Molecule

Recognition Domain (MRD) to N- or/and C- terminal of heavy or/and light chain. MRDs are

patented peptides that are able to recognize specific antigens. The Zybodies used in this study

were an anti-Her2 bi-specific mAb that can suppress Her2 over-expression and angiogenesis

simultaneously. There were two candidates in this study: Candidate 1 and Candidate 2. These

two candidates had different amino acid sequences for MRDs, both of which were fused to the

C-terminus of the heavy chain of anti-Her2 as the red part in the molecule shown in Figure 4-1.

In this study, the stability of the two MRDs were compared, and the two candidates were

characterized using mass spectrometry coupled with online liquid chromatography (LC-MS),

which has been a very powerful tool to characterize and quantitate protein pharmaceuticals

including mAb drugs.12-16

anti-Her2

Molecularrecognition domain (MRD)

Figure 4-1: Structure of Zybody molecule used in this study

147

4.3 Experiments

4.3.1 Materials

Candidate 1 and Candidate 2 were provided by our collaborator in a liquid formulation

(containing 5.4 and 1.732 mg/mL, respectively). Candidate 1 and Candidate 2 are recombinant

humen IgG1 with the variable Fc domains and C-terminal regions.

4.3.2 In solution enzyme digestion

The protein solution (1 µg/µL) was denatured with 6 M guanidine hydrochloride containing 100

mM NH4HCO3, reduced with 5 mM DTT at 56 °C for 30 min, and alkylated with 10 mM IAA at

room temperature for 1 h in the dark. The protein solution was then buffer exchanged into 100

mM NH4HCO3 five times by using a 10 kDa molecular weight cut off column. Trypsin (1:100

w/w) was added to the protein solution, and incubated overnight at 37 °C. The digestion was

terminated by adding 1% formic acid. For digestion at pH 6.8, 50 mM Tris-HCl was used to

replace NH4HCO3 in all of the previous procedures. For pepsin digestion, 0.01 M HCl (pH=2)

was used. For native digestions, enzyme digestion was performed directly without reduction and

alkylation.

4.3.3 SDS-PAGE and in gel digestion

A mini gel (4-12% Bis-tris) was used to separate the eluents from antibody or protein A

enrichment. Coomassie blue was used to stain the proteins on the gels. The gel bands containing

148

the heavy chain of the IgG drug (at the position of expected molecular weight) were cut into

small pieces for subsequent gel digestion. Coomassie blue stain was removed by shaking the gel

pieces overnight in 50% acetonitrile and 50% 25 mM ammonium bicarbonate. The destained gel

pieces were reduced by 100 µL 10 mM DTT in 100 mM NH4HCO3 and incubated for 30 min at

56 °C, then alkylated with 100 µL 55 mM IAA in 100 mM NH4HCO3. The gel pieces were

covered by an enzyme digestion reagent (12.5 ng/µL trypsin, Lys-C, or Glu-C in 50 mM

NH4HCO3, pH 8.0), stored at 4 °C for 30 min. The trypsin reagent was replaced by 100 µL 25

mM NH4HCO3, and then incubated overnight at 37 °C. The digested peptides were extracted by

acetonitrile and 5% formic acid three times. All of the supernatant was collected and combined,

and then concentrated to about 5 µL. The concentrated tryptic digestion peptides were diluted to

an appropriate volume with mobile phase A before being subjected to LC-MS analysis.

4.3.4 LC-MS analysis by LTQ-Orbitrap

The peptides were separated by an ultimate 3000 nano LC pump (Dionex, Mountain View, CA)

and a self-packed C18 column (Magic C18, 5 µm particle size, 200 Å pore) (Michrom

Bioresourese, Auburn, CA), and analyzed by LTQ-Obritrap mass spectrometer (Thermo Fisher

Scientific, San Jose, CA) equipped with New Objective (Waltham, MA) nanospray source. The

column flow rate was maintained at 200 nL/min after splitting. The LC gradient was from 5% B

to 65% B in 60 min (A: water with 0.1% formic acid; B: acetonitrile with 0.1% formic acid),

then from 65% B to 80% B in 10 min, and hold at 80% B for 10 min. For the LTQ-Orbitrap

operation, a full-scan MS spectra (m/z 400-2000) was acquired, followed by 8 sequential MS2

scans using LTQ.

149

4.3.5 LC-MS analysis by Q-TOF

Agilent 1200 HPLC-chip system was used for separation and was coupled to Agilent 6520 Q-

TOF for MS analysis through Agilent 1100 Chip Cube (G4240). An Agilent C18 Chip (G4240-

62010, 150mm 300 Å C18 chip with 160 nL trap column) was used to separate tryptic peptides

of Zybodies. The LC gradient was from 2% to 40% in 50 min, and 40% to 60% in 5 min, and

then back to 2% in 2 min. Mobile phase A was 0.1% formic acid in water, and mobile phase B

was 0.1% formic acid in acetonitrile. In the electrospray ionization, the voltage was set to 2000 V,

and the drying gas was maintained at 6 L/min at 325 °C. A 175 V fragmentor voltage was used,

and skimmer voltage was kept to 65 V. The instrument was operated at data-dependent mode,

full-scan MS spectra was acquired from m/z 300 to 1900, followed by five MS2 scan from m/z

100 to 1900.


Candidate 1 and Candidate 2 belong to Zybodies, a novel family of therapeutic monoclonal

antibodies. They are essentially standard mAbs, but with multi molecular recognition domains

attached to the N-termini or C-termini of heavy or light chains. Here, two Zybody molecules

were comprehensive characterized by LC-MS.

4.4.1 Primary structure identification

150

The correct amino acid sequence is fundamental for drug efficacy and safety. The initial task was

to identify the primary structure of two Zybody molecules and make sure our collaborator had

manufactured the correct products. In order to confirm the amino acid sequence, the mAb drugs

were digested using both in-solution and in-gel digestions under either reduced or native

condition. In reduced digestion, the Zybody molecule was firstly reduced and alkylated then

digested by enzymes. In native condition, the Zybodies were directly digested into peptides

without reduction and alkylation. To achieve the 100% sequence coverage, multiple enzymes

were selected to generate peptides with proper sizes which are able to be retained in the LC

separation. Undertryptic digestion, some peptides, such as T17H, T28H, T32H, etc. were too

small and therefore were not identified. They were further identified using Lys-C, Glu-C, and

pepsin digestion. By using a multi-enzyme approach, 100% sequence coverage was achieved

(data not shown). Full sequence coverage was achieved for both Candidate 1 and Candidate 2,

showing that both Zybodies have identical amino acid sequences except for the N-terminal MRD

peptides.

The C-terminal peptide (T42H) which contains a longer peptide sequence in comparison with

traditional mAb, as shown in Figure 4-2, was identified at 79.48 min for Candidate 1 (Figure 4-2

Part I), and 64.33 min for Candidate 2 (Figure 4-2 Part II), with the accurate precursor mass

measurement (m/z 1391.9883, charge 3+, and m/z 1272.5472, charge 3+, respectively) and CID-

MS2 of the precursor ion. The high abundance of product ions and characteristic fragmentation

patterns show the complete structure of the C-terminal regions for both molecules, however,

some truncated forms of the two peptides were also observed as described in the following

section.

151

S-L-S-L-S-P-G-S-G-G-G-S-G-G-A-Q-T-N-F-M-P-M-D-Q-D-E-A-L-L-Y-E-E-F-I-L-Q-Q-G-L-E

b4 b5 b8 b11 b19 b25 b34b20 b39

y5y6y7y16y17y18 y10

b38

y25y36y35

b37

y28

b31

400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000m/z

0

50

100

Rel

ati

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bu

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1797.8

1368.51745.01191.1

1844.6

1305.7 1888.31663.7687.5

1958.2

1291.1 1595.7947.6574.2 1077.4 1550.4488.2 800.9 901.4401.5 729.3

MS 2 (Ion Trap)

C

b4+ b5

+b8

+ b11+

b252+

b19+

b342+

b20+

y5+

y6+

y7+ [y16-H2O]2+

y172+

1206.6y10

+

b383+

y252+

b382+

y362+

y352+

1930.1

b372+

y282+

b312+

b393+

1343.8

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90Time (min)

0

50

10079.48 NL: 3.17E7

1389.5 1390.0 1390.5 1391.0 1391.5 1392.0 1392.5 1393.0 1393.5 1394.0 1394.5 1395.0 1395.5 1396.0 1396.5m/z

0

50

1001392.6531

1392.3212 1392.9849

1393.3175

1391.9883 1393.65111393.9858

1394.3206

Rel

ati

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bu

nd

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ceR

ela

tive

Ab

un

da

nce

MS (Orbitrap)

LC-MS

A

B

3+Theoretical m/z 1391.9801

Figure 4-2 Part I: Identification of intact C-terminal peptide (T42H) in Candidate 1

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95Time (min)

0

50

100

Rel

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64.33

NL: 3.11E8

1270.0 1270.5 1271.0 1271.5 1272.0 1272.5 1273.0 1273.5 1274.0 1274.5 1275.0 1275.5 1276.0 1276.5 1277.0 1277.5m/z

0

50

100

Rel

ati

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bu

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1273.21331272.8804

1273.5459

1273.87921272.54721274.2128

1274.5472

MS (Orbitrap)

LC-MS

A

B

3+Theoretical m/z 1271.5430

MS 2(Ion Trap)

C

400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000m/z

0

50

100

Rel

ati

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bu

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ce

1665.4

1235.1

971.3 1330.41709.6665.5 1108.6 1852.011808.0

488.1 739.0 1015.6

y112+

b5+

729.5b8

+

y122+

[b12-H2O]+

y162+

b373+

1244.6y37

3+y11

+

b282+

1318.4

y332+

y342+

b362+ b37

2+b302+

1423.9

S-L-S-L-S-P-G-S-G-G-G-S-G-G-A-G-G-G-G-S-L-W-D-D-C-Y-F-F-P-N-P-P-H-C-Y-N-S-Pb5

y11

b8

y12

b36b12

y16

b37

y37 y33y34

b30

Figure 4-2 Part II: Identification of intact C-terminal peptide (T42H) in Candidate 2

Figure 4-2: Identification of intact C-terminal peptide (T42H) in two Zybodies

152

4.4.2 C-terminal truncation

Zybody is a novel therapeutic mAb, programmed to target two or up to five antigens

simultaneously. In our study, Candidate 1 and Candidate 2 are bi-specific mAbs that contain

patented sequences to recognize multiple antigens,17-18 therefore Candidate 1 and Candidate 2

have longer C-terminal regions in comparison to traditional mAb molecules. The integrity of the

C-terminus plays an important role in the drug efficacy, therefore it is significant to identify and

quantify both the intact and truncated forms of Zybodies. Here, we observe the truncated forms

of the C-terminal peptides of the two mAb molecules and relatively quantitated their percentages

in neat samples.

The C-terminal truncations were firstly searched by setting the parameter as ‘no enzyme’ in

Bioworks; the output results were further extracted from raw data for confidence checking. To

prevent false positive results, we only examine the truncated peptides with high Xcorr score

(>2.3 for 2+ peptides, >3 for 3+ peptides). It should be noted that some truncation could result

from proteolysis cleavage. Here, we used four enzymes to perform digestions. If a truncated

form can be identified in all the digestions, it is believed that the truncation occurred due to the

mAb sample itself.

Figure 4-3 shows the identification of one truncated form of T42H in Candidate 1. This truncated

peptide was eluted at 35.92 min, and was identified by accurate mass assignment. A series of y

ions can also confirm the truncation site at Asn460. Using a similar method, all the identified

truncated forms of T42H can be identified.

153

200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500m/z

0

20

40

60

80

100

Rel

ativ

e A

bund

ance

1133.46758.50

488.36

701.31401.12 1300.51805.35547.24

b4+

b5+

b172+

y61+ y10

+

y14+

1046.42

y13+

S-L-S-L-S-P-G-S-G-G-G-S-G-G-A-Q-T-N

b4 b5 b17

y13 y6y10y14

767.0 768.0 769.0m/z

0

50

100

Re

lati

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bu

nd

ance

767.3625

767.8622

768.3630

768.8641769.3654

MS2+CID-MS2

Figure 4-3 Part A: Identification of one heavy chain C-terminal truncated peptide in Candidate

1: SLSLSPGSGGGSGGAQTNFMPMDQDEALLYEEFILQQGLE a

S-L-S-L-S-P-G-S-G-G-G-S-G-G-A-G-G-G-G-S-L-W-D-Db5b4

y4 y3y20 y19

b21

y16

b15b14

1018.0 1019.0 1020.0m/z

0

50

100

Re

lati

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bu

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ce

1018.45671017.9555

1018.9577

1019.4590

300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000m/z

0

20

40

60

80

100

Rel

ativ

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bund

ance

1547.54

1634.62

1110.23

893.82

1600.45

488.101172.46 1299.55435.01

729.28548.22

b5+

y4+b4

+ y3+

y20+

y19+

b21+

y16+b15

+

b14+

401.12

MS2+

CID-MS2

Figure 4-3 Part B: Identification of one heavy chain C-terminal truncated peptide in Candidate

2: SLSLSPGSGGGSGGAGGGGSLWDDCYFFPNPPHCYNSP

Figure 4-3: Examples of identification of C-terminal truncated peptides in two Zybody

molecules.

Part A. CID-MS2 of one heavy chain C-terminal truncated peptide in Candidate 1, m/z 767.36

(2+) ion, with the measurement of the precursor ion in the insert.

Part B. CID-MS2 of one heavy chain C-terminal truncated peptide in Candidate 2, m/z 1017.96

(2+) ion, with the measurement of the precursor ion in the insert.

a The grey amino acids represent missing part in this tryptic peptide.

Table 4-1 shows all the truncated forms of C-terminal peptides (T42H) of Candidate 1 and

Candidate 2. The grey amino acids indicate the missing part for T42H. For the truncated and

intact species of T42H, the most abundant charge state and corresponding Xcorr scores are listed.

The percentages are calculated based on the corresponding intensities for all species. For

Candidate 1, only 52% of the molecules keep the whole T42H in the neat Candidate 1 sample,

154

and the main truncation occurs at Asn460 (23%) and Thr459 (10%).

Table 4-1: Summary of identified C-terminal truncated peptides in two Zybody molecules with

the corresponding intensity and percentages

Part A: Summary of identified C-terminal truncated peptides in Candidate 1

Sequence z a XC b NL c Percentage d

K.SLSLSPGSGGGSGGAQTNFMPMDQDEALLYEEFILQQGLE.- e 2 3.81 1.70E+07 23.2%

K.SLSLSPGSGGGSGGAQTNFMPMDQDEALLYEEFILQQGLE.- 2 2.73 7.30E+06 10.0%



K.SLSLSPGSGGGSGGAQTNFMPMDQDEALLYEEFILQQGLE.- 3

3.80E+07 51.8%

Total

7.33E+07

Part B: Summary of identified C-terminal truncated peptides in Candidate 2

Sequence z a XC b NL c Percentage d

K.SLSLSPGSGGGSGGAGGGGSLWDDCYFFPNPPHCYNSP.- e 2 2.26 4.90E+05 1.4%

K.SLSLSPGSGGGSGGAGGGGSLWDDCYFFPNPPHCYNSP.- 2 2.64 7.39E+05 2.1%





K.SLSLSPGSGGGSGGAGGGGSLWDDCYFFPNPPHCYNSP.- 3 2.89E+07 82.1%

Total 3.52E+07

a Z: charge state of the highest intensity species observed b XC: corresponding Xcorr score c NL: corresponding intensity of the truncated peptides with the indicated charge states d Percentages: percentages were calculated based on the intensities e Grey amino acid sequences represent the missing part for this peptide

155

Candidate 2, as an improved molecule compared to Candidate 1, shows higher molecule stability.

More than 80% of Candidate 2 molecules keep the intact C-terminus, and the most abundant

truncated form only accounts for about 5%, as shown in Figure 4-3 Part B. A main truncation site

is Asp466, and the truncated form takes about 5% in neat Candidate 2 sample. Therefore,

Candidate 2 keeps higher stability in C-terminus in comparison to its previous product,

Candidate 1, and was selected as a candidate for further pharmacokinetics and metabolism study.

4.4.3 Disulfide bond linkages

Same to other human IgG1s, a Zybody molecule consists of two identical heavy chains and two

light chains linked via disulfide bonds. In Zybody, heavy chain and light chains are linked by an

inter-chain disulfide bond between Cys223 in heavy chain and Cys214 in light chain. One heavy

chain is linked to the other heavy chain by two parallel disulfide bonds by connecting the same

position of Cys229 and Cys232 on both heavy chains. Besides the four intra-chain disulfide

bonds, a Zybody molecule also has 12 inter-chain disulfide bonds as other human IgG1 as shown

in Table 4-2.To map disulfide bond linkages, Zybodies are digested in a native condition without

reduction or alkylation to preserve their original disulfide bonds. The disulfide-linked peptides

were analyzed by LC-MS, and manually extracted and assigned since no disulfide bond linkage

information was included in database.

Considering the larger peptides due to native digestion, it is necessary to select proper enzymes

to generate peptides that are 1-5 kDa.19 Table 4-2 lists all peptide linkages and their

corresponding MH+ in Candidate 1 and Candidate 2. Trypsin digestion is sufficient to generate

156

suitable sizes for most disulfide-linked peptides except for T18H-T19L. In this case, Lys-C was

chosen to generate a peptide that is large enough to be held on the reverse column for LC-MS

analysis. Here, a second dose of enzyme was used for both trypsin and Lys-C digestion in order

to fully digest native antibodies.

Table 4-2: Disulfide bond linkages in two Zybody molecules

Sequence Cys # Start End 1+ RT (min)

In Heavy Chain

T2H (R)LSCAASGFNIK(D) 22 20 30

T11H (R)AEDTAVYYCSR(W) 96 88 98

T14H (K)STSGGTAALGCLVK(D) 147 137 150

T15H

(K)DYFPEPVTVSWNSGALTSGVHTFPAVL

QSSGLYSLSSVVTVPSSSLGTQTYICNVNH

KPSNTK(V) 203 151 213

T20H (K)THTCPPCPAPELLGGPSVFLFPPKPK(D) 229, 232 226 251

T20H (K)THTCPPCPAPELLGGPSVFLFPPKPK(D) 229, 232 226 251

T22H (R)TPEVTCVVVDVSHEDPEVK(F) 264 259 277

T28H (K)CK(V) 324 324 325

T36H (K)NQVSLTCLVK(G) 370 364 373

T41H (R)WQQGNVFSCSVMHEALHNHYTQK(S) 428 420 442

In Light Chain

T2L (R)VTITCR(A) 23 19 24

T7L

(R)SGTDFTLTISSLQPEDFATYYCQQHYTT

PPTFGQGTK(V) 88 67 103

T11L (K)SGTASVVCLLNNFYPR(E) 134 127 142

T18L (K)VYACEVTHQGLSSPVTK(S) 194 191 207

Lys-C BETWEEN Heavy Chain and Light Chain

L12H (K)SCDK(T) 223 222 225

L13L (K)SFNRGEC(-) 214 208 214

Tryptic

peptide

2385.0857 51.54

7917.9274 74.56

5455.7915 72.40

2329.1058 49.52

3845.8317 57.63

4820.2503 68.27

3556.7571 63.97

1261.4944 2.12

Disulfide-linked peptides were dissociated with CID, which predominantly generate cleavages

on peptide bonds while maintaining the integrity of disulfide bonds. An example is shown in

Figure 4-4. A disulfide-linked peptide T2H-T11H was eluted at 51.05 min and was assigned by

157

its accurate mass. The disulfide bond linkage was confirmed by the CID MS2 as shown in panel

C. A series of disulfide-linked y ions were generated as a result of backbone cleavages on P1 and

P2 in CID. These product ions can be used to identify the disulfide bond linkage between T2H

and T11H by manual assignment. This process is tedious and is not achievable when there are

multiple intertwined disulfides in one peptide.19

Recently, Wu et al. has established a novel mass spectrometry-based analytical strategy to

elucidate disulfide bond linkages in mAbs and other protein pharmaceuticals.19-22 In this method,

CID-MS2, ETD-MS2, and CID-MS3 of the isolated charge-reduced ions were employed to

reveal the disulfide linkages including scrambling, cystine knot, and Nested Disulfides.19, 22 In

ETD-MS2, disulfide bonds are preferentially dissociated to generate two dissociated peptides, P1

and P2. The corresponding P1 and P2 are able to confirm the linkages of two peptides. An

example is shown in Figure 4-4, Part D. Two main product ions in ETD-MS2 are P1 and P2,

which confirms the disulfide linkage between T2H and T11H. Besides, one of the charge-

reduced species m/z 1193.8 ([M + 3H] 2+·) can also be observed as a major species generated in

the ETD spectrum.

158

44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62Time (min)

0

20

40

60

80

100R

ela

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Ab

un

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50.38

795.5 796.0 796.5 797.0 797.5 798.0 798.5m/z0

50

100

Rel

ati

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bu

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795.7005 796.3667

796.6999

797.0336797.3671 797.7010 798.0355 798.3714 798.7076

m/z

300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 20000

20

40

60

80

100

Rel

ati

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bu

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900.20

1093.29950.08

818.64736.67 985.51

665.27

y52+ (P2)

y62+ (P2)

y92+ (P2)

y72+ (P2) y8

2+ (P2)

y42+ (P2)

y32+ (P2)

y61+ (P1)

1036.07

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000m/z

0

20

40

60

80

100

Rel

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1193.87

1109.40 1277.61358.09

1881.601666.55 1737.69649.27505.24 1595.61

Z31+ (P1)

Z41+ (P1) /

C51+ (P2) Z5

1+ (P1) C71+ (P1)C3

1+ (P1)C4

1+ (P1)C51+ (P1)

P2 1+P1 1+

[M+3H] 2+·

Part CCID MS2 of796.03 (3+) ion

Part BMS (Orbitrap)

Part AXIC

Part DETD MS2 of796.03 (3+) ion

T2H: L-S-C-A-A-S-G-F-N-I-K (P1)

T11H: A-E-D-T-A-V-Y-Y-C-S-R (P2)

y6

y3y4y5y6y7y8y9

T2H: L-S-C-A-A-S-G-F-N-I-K (P1)

T11H: A-E-D-T-A-V-Y-Y-C-S-R (P2)

C7

Z3

C5C4

C5

Z4Z5

C3

Figure 4-4: Identification of one disulfide bond (T2H-T11H) by mass spectrometry

Part A. Extracted ion chromatography (XIC); Part B. Precursor ion MS scan at 50.38 min using

Orbitrap (only m/z 795.5-799.0 regions is shown for illustration purpose); Part C. CID-MS2

pattern of the precursor ion in Part B, the peptide sequence with the fragmentation observed is

shown in the insert; Part D. ETD-MS2 pattern of the precursor ion in Part B, the peptide

sequence with the fragmentation observed is shown in the insert.

A similar approach can be applied to identify all disulfide bond linkages in Candidate 1 and

Candidate 2. All the disulfide bonds are correctly linked in the two Zybody samples.

159

4.4.4 Chemical modifications

4.4.4.1 Pyroglutamic acid (PyroE) at the N-terminus of heavy chain

PyroE formation from N-terminal glutamic acid residues has been discovered and investigated in

recombinant monoclonal antibodies.23 The mechanism of this reaction has been discovered,

however, a trace level of spontaneous pyroE formation in a fully humanized IgG1 showed

evidence for a non-enzymatic reaction.24 It has been reported that prolonged storage life-time and

storage in phosphate buffer could accelerate the cyclization of Glu.25

As shown in Figure 4-5, pyroE formed from the N-terminal E was identified by the accurate

mass assignment. For glutamic acid residues, the loss of one H2O molecule during the

cyclization reaction will bring an 18 Da mass loss for 1+ charge ions, and a 9 Da mass loss for

2+ charge ions. For Candidate 1, the intact T1H and pyro-T1H were identified by the accurate

mass of precursor ions as shown in the insert of Figure 4-5. The mass difference between intact

T1H (2+) and pyro-T1H (2+) was 9.003 (941.5062-932.5029=9.003), which matches the mass

loss from the pyroE formation for 2+ charged ions. The position of pyroE can be further

confirmed by CID-MS2 comparison between intact T1H and pyro-T1H, as shown in Figure 4-5.

A series of same y ions, but different b ions that have 18 Da difference in singly charged ions,

confirm the dehydration at the N-terminus.

We also performed the relative quantitation of the pyro-T1H amount based on the assumption

that both the cyclized and non-cyclized peptides have the same response in mass spectrometer.

Figure 4-6 displays the extracted ion chromatogram (XIC) of the non-cyclized T1H and the

cyclized T1H. The percentage of pyro-T1H can be calculated by the intensity of the pyro-T1H

divided by the sum of the intensity of both pyro-T1H and non-modified T1H as following:

160

The formation of pyro-T1H on N-terminus of heavy chain can also be identified and quantified

using similar methods. And the percentages of pyro-T1H formation can be compared in two

Zybody candidates as shown in Table 4-3. Candidate 2 has a slightly higher extent of

pyroglutamic acid formation at N-terminus of heavy chain compared to Candidate 1.

300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800m/z

0

20

40

60

80

100

Rel

ativ

e A

bu

nd

ance 586.4

714.4

1052.3

813.5551.2 881.2

b5+

y6+

y7+

y8+

b9+

b11+

1151.4b12

+

1278.5

300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800

m/z

0

20

40

60

80

100

Re

lati

ve A

bu

nd

an

ce

586.4

714.4

1070.5813.6

899.5569.3

y6+

y7+

y8+

b9+

b11+

b5+

b12+

1296.41169.5[y14-H2O]+

[y14-H2O]+

E-V-Q-L-V-E-S-G-G-G-L-V-Q-P-G-G-S-L-R

y6y7y8

b9 b11

y14

b5 b12

940 941 942 943 944 945 946m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

941.5062942.0060

942.5063

943.0078943.5095

2+MS (Orbitrap)

b9 b11 b12

E#-V-Q-L-V-E-S-G-G-G-L-V-Q-P-G-G-S-L-Rb5

y6y7y8y14

932 933 934 935m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

933.0031932.5029

933.5038

934.0047934.5061

2+MS (Orbitrap)

Part ACID MS2 of941.50 (2+) ion

Part BCID MS2 of932.50 (2+) ion

Figure 4-5: Identification of N-terminal peptide on heavy chain and formation of pyro-glutamic

acid on N-terminus of heavy chain of Candidate 1

Part A. CID-MS2 of the N-terminal peptide on heavy chain, m/z 941.5062 (2+) ion, with the MS

measurement of the precursor ion by Orbitrap in the insert

Part B. CID-MS2 of the formation of pyro-glutamic acid on N-terminal peptide of heavy chain,

m/z 932.5029 (2+) ion, with the MS measurement of the precursor ion by Orbitrap in the insert

161

40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70Time (min)

0

10

20

30

40

50

60

70

80

90

100R

ela

tive

Ab

un

da

nce

48.09

NL: 2.30E8

40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70

Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e A

bund

ance

56.84NL: 4.81E6

LC-MST1H: (-)EVQLVESGGGLVQPGGSLR(L)2+, 941.50

LC-MSPyro-T1H: (-)EVQLVESGGGLVQPGGSLR(L)2+, 932.50

Figure 4-6: Relative quantification of extent of pyro-E formation at N-terminus of heavy chain

Upper: XIC of non-modified T1H; Lower: XIC of pyro-T1H

Table 4-3: Comparison of the percentage of pyroglutamic acid formation on N-terminus of

heavy chain for two Zybody molecules

Tryptic peptide sequence

Percentage of pyro-T1H

(average ± SD) %, N=3

Candidate 1 Candidate 2

E1VQLVESGGGLVQPGGSLR 2.2 ± 0.9 4.8 ± 0.9

Apart from the E at the N-terminus of heavy chain, the pyroE formation from the N-terminus of

light chain was also examined. For glutamine, formation of pyroE brings a mass loss of 17 Da.

Here, no pyroglutamic acid formed as a light chain N-terminus was detected in both Candidate 1

and Candidate 2.

162

4.4.4.2 Oxidation

Methionine (Met) in protein pharmaceuticals including mAbs could be oxidized into Met

Sulfoxide in the processing and storage. Met oxidation may not cause the conformational change

of mAb; 26 however, it still can lead mAbs to lose its bioactivity and stability, therefore it results

in a decreased half-life and shelf-storage time. Here, the extents of oxidation of Candidate 1 and

Candidate 2 were examined to evaluate their stability. 14

By using the similar approach, the amount of oxidation formed at five sites can be examined. As

shown in Figure 4-7, the oxidation and non-oxidized peptides (T21H) were identified by their

accurate precursor mass as in the insert. The one oxidation site, M, will bring an extra oxygen

mass 15.9944 Da for singly charged ions, and 7.9972 for doubly charged ions in comparison to

non-oxidized M. In this case, the difference of the monoisotope ion (1+ charge) between the

oxidized and non-oxidized peptide was 15.9939 (851.4294-835.4355=15.9939). This matched

the mass difference brought by one oxygen atom for a single charged ion. The position of

oxidation was identified by CID-MS2 fragmentation of the oxidized peptide as shown in Figure

4-7. The oxidation site is located at the methionine residue (M). We also examined the oxidized

T42H and T1L peptides of Candidate 1 and Candidate 2 through the same method.

163

250 300 350 400 450 500 550 600 650 700 750 800 850m/z

736.5590.3477.3330.1b6

+b3+ b4

+

y6+

0

20

100

Rel

ativ

e A

bund

ance 80

60

40

250 300 350 400 450 500 550 600 650 700 750 800 850m/z

720.9

461.1330.0b3

+ b4+

y6+

0

20

100

Rel

ativ

e A

bund

ance 80

60

40

574.1b6

+

Part A. CID MS2 of the 835.43 (2+) ion

Part B. CID MS2 of the 851.43 (2+) ion

D-T-L-M-I-S-Rb3

y6

b4 b5

D-T-L-M*-I-S-Rb3

y6

b4 b5

834 835 836 837 838 839 840m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

835.4353

836.4377

837.4388838.4316

850 851 852 853 854 855 856m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

851.4299

852.4335

853.4394854.4261

Figure 4-7: Identification of methionine oxidation of T21H of Candidate 1

Part A. CID-MS2 of the non-oxidizedT21H, m/z 835.4353 (1+) ion, with the MS measurement

of the precursor ion by Orbitrap in the insert

Part B. CID-MS2 of the oxidizedT21H, m/z 932.851.4299 (1+) ion, with the MS measurement of

the precursor ion by Orbitrap in the insert

Since the amount of oxidation might be artificially amplified in the electrospray ion source under

atmospheric pressure, we examined the oxidation extent of the same oxidation sites by Agilent

Q-TOF. The result is summarized in Table 4-4. Four of the five methionine sites listed in this

table have small extents of oxidation except Met107 for both Zybody molecules. Though it has

been reported that Met255 is much susceptible to be oxidized under exposure of light and higher

temperature, 27 here, the extent of Met255 is comparable with the other Met sites in both

Candidate 1 and Candidate 2.

164

Table 4-4: Comparison of the percentage of oxidation for two Zybody molecules


Percentage of M[O]



NTAYLQM83

NSLR (HC a

) 1.3 ± 0.2 3.4 ± 3.7

WGGDGFYAM107

DYWGQGTLVTVSSASTK (HC) 0 0

DTLM255

ISR (HC) 4.6 ± 1.1 4.6 ± 0.7

WQQGNVFSCSVM432

HEALHNHYTQK (HC) 4.9 ± 0.6 3.3 ± 0.2

DIQM4TQSPSSLSASVGDR (LC

b

) 3.4 ± 0.6 3.2 ± 0.2

a

HC: heavy chain; b

LC: light chain

4.4.4.3 Deamidation

Asparagine (Asn) deamidation of mAbs could occur at many stages of manufacture, such as

secretion, purification, during storage, etc.28 It is also a major concern for mAbs because it is

associated with protein degradation, and contributes to antibody heterogeneity and instability.29

Asn can lose a primary amine group in its side chain, and cyclize to form the succinimide

intermediate, which can easily be hydrolyzed into fully deamidated products, aspartic acid or iso-

aspartic acid.30 The rate of deamidation is influenced by amino acid sequence, e.g. Asn followed

glycine (Gly) is the most susceptible deamidation site. Besides, elevated pH and temperature

could also lead to deamidation, therefore extra care needs to be taken during sample preparation

for characterization of mAbs by LC-MS.31

The amount of deamidation of Candidate 1 and Candidate 2 can be examined by the same

method as what is used for the determination of oxidation. Asparagine first loses its –NH2 group

in the side chain to form a succinimide intermediate, which can be easily hydrolyzed into the

165

deamidation product, aspartic acid or iso-aspartic acid. Compared to the original asparagine,

deamidation brings a mass addition of 0.9840 Da for singly charged ion or 0.4920 Da for doubly

charged ion. This information can be used to identify aspartic acid or iso-aspartic acid formed

from Asn deamidation. It should be noticed that a higher pH and temperature during the sample

preparation would significantly increase the deamidation amount. Therefore, we examine the

deamidation amount using Tris-HCl buffer (pH 6.8) to eliminate artificial asparagine

deamidation using classic ammonium bicarbonate buffer (pH 8.0), and no deamidation was

detected in either Zybody molecules.

Although no full deamidated product was found, cyclization intermediate formed during

asparagine deamidation was identified in both Zybody samples. Figure 4-8 shows the

identification of succinimide intermediate formed from T6H in Candidate 1. The accurate m/z

precursor ion (2+) shifted from 542.7748 to 534.2635 due to the loss of NH3. The formation of

succinimide was further confirmed by the MS2 pattern. The ions presented at 249.1 (y42+) and

277.1 (b21+) are the same in the two MS2 patterns, but the ion presented at 404.9 (y7

2+) shifted to

396.4 in the lower panel as a result of cyclization of asparagine. The similar shift also occurred to

the ions present at 808.4 (y71+) and 792.4 (b7

1+). Table 4-5 shows the comparison of percentage

of succinimidation formed during asparagine deamidation in Candidate 1 and Candidate 2. Both

Zybodies endure a similar extent of succinimidation on the asparagine sites listed in the table.

166

200 300 400 500 600 700 800 900 1000 1100m/z0

20

40

60

80

100

Rel

ativ

e A

bund

ance

404.9

808.4

809.4

277.1249.1

y42+

b2+

b7+

y72+

y7+

200 300 400 500 600 700 800 900 1000m/z0

20

40

60

80

100

Rel

ativ

e A

bund

ance

791.4

396.4

277.1

249.2y4

2+

b2+

b7+

y72+

792.4

y7+

1100

Part A. CID MS2 of 542.78 (2+) ion

Part B. CID MS2 of 534.26 (2+) ion

I-Y-P-T-N-G-Y-T-R

y4

b2

y7

b7

I-Y-P-T-N-G-Y-T-R

y4

b2

y7

b7

543 544 545m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

543.2690

542.7748543.7692

544.2690

2+MS (FT)

534 535 536m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

534.2635

534.7653

535.2667535.7681

2+MS (FT)

Figure 4-8: Identification of succinimide intermediate formed during asparagine deamidation for

two Zybody molecules

Part A. CID-MS2 of the non-cyclizedT6H, m/z 542.7748 (2+) ion, with the MS measurement of

the precursor ion by FTICR in the insert

Part B. CID-MS2 of the cyclizedT6H, m/z 534.2635 (2+) ion, with the MS measurement of the

precursor ion by FTICR in the insert

Table 4-5: Comparison of the percentage of succinimide intermediate formed during asparagine

deamidation for two Zybody molecules


(all in heavy chain)

Percentage of succinimide intermediate



IYPTN55

GYTR 1.0 ± 0.3 1.1 ± 0.2

VVSVLTVLHQDWLN318

GK 2.9 ± 1.3 0.6 ± 0.3

GFYPSDIAVEWESN387

GQPENNYK 0 0

167

4.4.4.4 Isomerization of aspartic acid

Aspartic acid (Asp) can lose water from its side chain and cyclize into succinimide, and then

hydrolyze into iso-aspartic acid (iso-Asp). This process is named isomerization.32 Isomerization

brings an additional methyl group to the peptide bond, which may lead to a protein structure

change. The most labile site for isomerization is the Asp followed by Gly.33

Similar to deamidation, Asp residues that are followed by glycine are easily isomerized. Asp and

iso-Asp could not be identified separately in CID-MS2; however, they have a different retention

time and therefore can be separated in HPLC as a result of their different chromatographic

behaviors. Here, no iso-Asp was detected, but a small amount of succinimide formation was

observed at Asp283 and Asp404 in both Candidate 1 and Candidate 2 under pH 6.8. One example is

shown in Figure 4-9, the formation of succinimide intermediate was identified on T23H of

Candidate 1 by an accurate mass assignment shown in the insert. The mass difference between

cyclized T23H (Figure 4-9 Part B) and non-cyclized T23H (Figure 4-9 Part A) was 9 Da, which

matches the mass loss for dehydration in doubly charged ions. The modification site can be

further confirmed by CID-MS2 fragmentation pattern as shown in Figure 4-9. Compared to the

non-cyclized T23H, the cyclized-T23H has the same y51+ ion, but a different y9

1+ ion that has a

mass of 18 Da due to dehydration during cyclization. Using a similar method, succinimide

intermediate formed during Asp isomerization can be identified in Candidate 2, and the

percentages of the extent of Asp isomerization can be relatively quantified and compared as

shown in Table 4-6. Candidate 2 has a slightly higher percentage of isomerization compared to

Candidate 1, which can lead to a weaker drug efficacy; however, a succinimide intermediate was

not detected on the complementary determining region Asp102 in both Zybody molecules.

168

m/z

y9+

y10+

y11+ b11

+

b2+ y3

+

b3+

y5+

b4+

y122+

300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 16000

20

40

60

80

100

Rel

ativ

e A

bund

ance

968.27

1067.39708.98

853.36

1230.37448.06

568.28

1346.32611.10

332.271460.49

1416.451531.36262.13

469.29

y4+

b12+

y121+

b13+

y8+

F-N-W-Y-V-D-G-V-E-V-H-N-A-Kb11b2

y12

b3 b4

y9 y5 y4 y3

b12b13

y10y11

F-N-W-Y-V-D-G-V-E-V-H-N-A-Kb11b2

y12

b3 b4

y9 y5 y4 y3

b12b13

y10y11 y8

Part A. CID MS2 of 839.41 (2+) ion

Part B. CID MS2 of 830.40 (2+) ion

840 841 842 843m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

839.4063839.9061

840.4067

840.9078841.4096

841.9117

2+MS (FT)

1328.28

y9+

y10+

y11+

b11+

b2+

y3+

b3+ y5

+

b4+

y122+

821.70

300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600m/z

0

20

40

60

80

100

Rel

ativ

e A

bu

nd

ance

950.40 1049.37

700.44 1212.46

448.29 568.18

332.26611.29

1398.47

1443.30 1513.53262.03

469.36y4

+ y12+

b12+ b13

+

830 831 832 833 834m/z

0

50

100

Rel

ati

ve A

bu

nd

an

ce

830.3997

830.9013

831.4024

831.9030832.4045

2+MS (FT)

Figure 4-9: Identification of succinimide intermediate formed during aspartic acid isomerization

for two Zybody molecules

Part A. CID-MS2 of the non-cyclizedT23H, m/z 839.4063 (2+) ion, with the MS measurement of

the precursor ion by FTICR in the insert

Part B. CID-MS2 of the cyclizedT23H, m/z 830.3997 (2+) ion, with the MS measurement of the

precursor ion by FTICR in the insert

Table 4-6: Comparison of the percentage of succinimide intermediate formed during aspartic

acid isomerization for two Zybody molecules


(all in heavy chain)

Percentage of succinimide intermediate



WGGD102

GFYAMDYWGQGTLVTVSSASTK 0 0

FNWYVD283

GVEVHNAK 1.8 ± 0.5 3.4 ± 0.3

TTPPVLDSD404

GSFFLYSK 1.3 ± 0.3 3.4 ± 0.3

169

4.4.5 Identification of glycopeptides

Glycosylation can enhance the in vitro stability of protein drugs. It is very important to maintain

the correct glycosylation structure of mAb in order to keep the drug efficacy.34-35 Like canonical

monoclonal antibody drugs, Zybody has a bi-antennary N-glycosylation site at Asn300 of heavy

chain in conserved Fc region. To identify the glycopeptides, Zybodies were digested in solution

by trypsin, and the subsequent tryptic peptides were subjected to LTQ-Orbitrap for LC-MS

analysis. An example of the identification of glycopeptides is shown in Figure 4-10. In Figure 4-

10, Part A, glycopeptides were extracted according to their accurate precursor masses. Five

forms of glycopeptides were eluted at the same time as shown in Figure 4-10, Part B and were

assigned by their precursor masses. In Figure 4-10, Part C, G0 was further confirmed by CID-

MS2. Since CID broke the glycosidic bonds while little information can be obtained about

peptide information, ETD is a supplementary fragmentation method to acquire linkages of

peptides. ETD breaks the peptide backbone mainly and maintains the intact moiety, therefore it

can be applied to peptide backbone identification. An example of ETD spectrum of

glycopeptides is shown in Figure 4-10, Part D. A mass increase of 1445.32 was observed in z26

and z27 compared to z6, z8, z15, and z18, which confirmed the glycan was attached to the first Asn

(Asn300) not the second Asn (Asn318). Besides, the peptide sequence can also be assigned using

ETD spectrum. By combining the results of CID and ETD spectra, peptide sequence,

glycosylation sites, as well as structures of glycans can be acquired at the same time with high

confidence.

170

T25H

T25HT25H

T25H

21.0 21.5 22.0 22.5 23.0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0 28.5 29.0 29.5 30.0Time (min)

0

20

40

60

80

100

Rel

ati

ve A

bu

nd

an

ce24.99

NL: 3.75E7

G0

G1

G2G1-GlcNAc

1220 1240 1260 1280 1300 1320 1340 1360 1380 1400 1420 1440 1460 1480m/z

0

20

40

60

80

100

Rel

ati

ve A

bu

nd

an

ce

1318.0303z=2

1399.0568z=21216.4907

z=21297.5191

z=2 1480.0832z=2

G0-GlcNAcT25H

Part A. XIC

Part B. MS (Orbitrap)

400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000m/z

0

20

40

60

80

100

Rel

ati

ve A

bu

nd

an

ce

1216.15

1143.08

1114.631392.58 1538.661244.98

1041.50960.57690.23 798.41

[M- ]2+

[M- - ]2+

[M- ]2+[M- - ]2+

EEQYNSTYR

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000m/z

0

20

40

60

80

100

Rel

ati

ve A

bu

nd

an

ce

1636.74 1964.57980.61

303.03523.28

1554.19845.07

1141.191050.15 1808.441858.45716.42

959.90627.69829.18 1345.02

C95+

C154+

C182+

C192+

C8+

Z6+

Z8+

Z152+

Z273+Z18

7+

[M+4H]3+ · C222+

Z263+

T-K-P-R-E-E-Q-Y-N-S-T-Y-R-V-V-S-V-L-T-V-L-H-Q-D-W-L-N-G-KC9 C15 C18C19 C21C8

Z6Z8Z15Z27Z26 Z18Part D. CID-MS2 of 982.47 (5+)

Part C. CID-MS2 of 1318.03 (2+)

Figure 4-10: LC-MS analysis of glycopeptides of Candidate 1

Part A. Extracted ion chromatogram (XIC)

Part B. Precursor ion scan at 24.99 min using Orbitrap. For illustration purpose, only m/z 1200-

1500 region is shown.

Part C. CID-MS2 of the precursor ion m/z 1318.03 (2+) ion

Part D. ETD-MS2 of the precursor ion m/z 982.47 (5+) ion.

N-acetylglucosamine Mannose Fucose Galactose

171

We also compared the glycopeptides distribution between Candidate 1 and Candidate 2, as

shown in Figure 4-11. Candidate 2 has a much higher level of G0, but a lower level of G1 and

G2. The percentage of fucose-loss glycopeptides, G0-Fu, G1-Fu, and G2-Fu, decreased

significantly in Candidate 2 in comparison to Candidate 1, which could lead to a higher antigen

binding affinity and improved drug efficacy.36

Figure 4-11: Glycopeptides distribution comparison between two Zybody molecules

CV% is measured based on three times measurement and is shown by the error bar in this figure.

Blue bar: Candidate 1; Green bar: Candidate 2

4.5 Conclusion

The entire Zybody sequence was successfully identified using a multi-enzyme digestion strategy

combined with different LC-MS platforms for both Candidate 1 and Candidate 2. The stability of

molecule recognition domain was evaluated and compared for Candidate 1 and Candidate 2.

Candidate 2 was selected for further pharmacokinetics and metabolism study because it exhibited

172

higher stability for C-terminal region.

Common chemical modifications, such as formation of N-terminal pyroglutamic acid,

methionine oxidation, asparagine deamidation, and aspartic acid isomerization, were identified

by accurate mass measurement and peptide sequence assignment in Candidate 1 and Candidate 2.

These modifications were also relatively quantitated, and compared the extents in two Zybody

candidates.

4.6 References

1. Ludwig, D. L.; Pereira, D. S.; Zhu, Z.; Hicklin, D. J.; Bohlen, P., Monoclonal antibody

therapeutics and apoptosis. Oncogene 2003, 22 (56), 9097-106.

2. Vacchelli, E.; Eggermont, A.; Galon, J.; Sautès-Fridman, C.; Zitvogel, L.; Kroemer, G.;

Galluzzi, L., Trial watch: Monoclonal antibodies in cancer therapy. Oncoimmunology 2013, 1 (1),

28-37.

3. Scott, A. M.; Wolchok, J. D.; Old, L. J., Antibody therapy of cancer. Nature Rev Cancer

2012, 12 (4), 278-87.

4. Cho, H. S.; Mason, K.; Ramyar, K. X.; Stanley, A. M.; Gabelli, S. B.; Denney, D. W., Jr.;

Leahy, D. J., Structure of the extracellular region of HER2 alone and in complex with the

Herceptin Fab. Nature 2003, 421 (6924), 756-60.


X., Insights into ErbB signaling from the structure of the ErbB2-pertuzumab complex. Cancer

Cell 2004, 5 (4), 317-28.




109-19.

7. Chames, P.; Baty, D., Bispecific antibodies for cancer therapy. Curr Opin Drug Discov

Devel 2009, 12 (2), 276-83.

8. Hollander, N., Bispecific antibodies for cancer therapy. Immunotherapy 2009, 1 (2), 211-

22.

9. Linke, R.; Klein, A.; Seimetz, D. Catumaxomab: Clinical development and future

directions. mAbs 2010, 2 (2), 129-36.

10. Burges, A.; Wimberger, P.; Kumper, C.; Gorbounova, V.; Sommer, H.; Schmalfeldt, B.;

Pfisterer, J.; Lichinitser, M.; Makhson, A.; Moiseyenko, V.; Lahr, A.; Schulze, E.; Jager, M.;

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Chapter 5 Pharmacokinetics and Metabolism Study of

Zybodies by Liquid Chromatography Coupled with Mass

Spectrometry (LC-MS)

Contributions:

Zybody samples and the anti-Zybodies were provided by Dr. Rajesh Krishnamurthy. The

experiments were designed and carried out by Emma Yue Zhang. Dr. Shiaw-Lin Wu and Dr.

William S. Hancock were taken part in experiment design.

Publication:

Emma Yue Zhang, Rajesh Krishnamurthy, Shiaw-Lin Wu, William S. Hancock.

Pharmacokinetics and Metabolism Study of Zybodies by Liquid Chromatography Coupled with

Mass Spectrometry (LC-MS). Manuscript in preparation.

176

5.1 Abstract

Monoclonal antibodies (mAbs) have been a very promising class of protein therapeutics since

the past decade. Using mAbs for cancer treatment has achieved great success since the approval

of the first mAb drug in 1998. Recently, using on-line liquid chromatography coupled to mass

spectrometry (LC-MS) has become an attractive approach for protein quantitation, including the

study of protein pharmacokinetics (PK).

In this chapter, analytical platforms using LC-MS were developed to quantitate Zybodies in

mouse serum. Two different enrichment techniques were used: a specific approach for Zybody

(anti-Zybody immunoprecipitation), and a general method for any conventional mAbs (protein A

enrichment). In general, the results from the two different enrichment methods highly correlated

with each other, and produced good agreement with the ELISA approach. This can confirm the

desired functionality of the anti-Zybody provided by our collaborator. Two LC-MS platforms

were applied for quantitation: either using intensity of precursor ions for quantitation in

nanoflow LC, or using multiple reaction monitoring (MRM) in industry standard LC-MS

platform. The first method provided higher sensitivity, while LC-MS is time-consuming and

possible coelutions in LC cannot be avoided. The second method offers higher throughput. In

both methods, the half life of Zybody in mouse serum was determined as about 48 hours. Besides,

most tryptic peptides as well as their major modified forms, including oxidized, deamidated, and

glycosylated peptides can be quantified using our platform. We have demonstrated that LC-MS

is an accurate and high-throughput method for PK and metabolism study of mAbs.

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5.2 Introduction

Amplification and overexpression of Her2 exists in about 20% of breast cancer patients, which is

associated with lower survival rate. 1 Currently there are three marketed mAbs for targeting Her2,

Trastuzumab and pertuzumab, and Trastuzumab-emtansine. Combination of pertuzumab and

Trastuzumab has shown to be more effective at the treatment of people with Her2 positive

metastatic breast cancer. Patients who received combined treatment of pertuzumab plus

Trastuzumab plus docetaxel have a longer median progression-free survival compared to patients

who are only treated with Trastuzumab plus docetaxel. 2

Bispecific antibodies are one of the most promising therapeutic antibodies in drug development

all over the world. 3 MM-111, developed by Merrimack, is currently under clinical trial phase II.

It consists of fully human anti-ErbB2 and anti-ErbB3 single chain antibody moieties linked by

modified human serum albumin, and is able to target ErbB2 and ErbB3 simultaneously. 4

Nowadays, there is a novel type of bispecific or multispecific antibodies called Zybody. Zybody

is one of the next-generation’s antibody therapeutics that is designed to target two or multiple (up

to five targets) of cancers and autoimmune disorders with no loss of the original functionality

and specificity of the scaffold antibody. 5

A Zybody consists of a full length mAb and fused molecule-recognition domains (MDR)

attached to its N-terminus or C-terminus of heavy or light chains. MDR is usually a short

polypeptide with less than 60 amino acids selected from combinatorial libraries for specifically

targeting any of the following five antigens: ErbB2, EGFR, IGF-1R, Ang2 and integrin αvβ3. It

has been reported that ADA-a2H, which consisted of an anti-TNF adalimumab fused with Ang2-

178

binding peptides to its heavy chain by Zybody technology, is able to bind Ang2 and TNF

simultaneously and display higher binding efficacy in vivo in a rheumatoid arthritis model

compared with adalimumab at the same dose. 6

In recent years, on-line liquid chromatography coupled to mass spectrometry (LC-MS) has been

a promising and powerful tool in protein quantification and pharmacokinetics/

pharmacodynamics assessments. In LC-MS analysis, selected reaction monitoring (SRM) or

multi-stage reaction monitoring (MRM) has always been applied for quantifying proteins by

measuring the selected peptides of the specific protein drugs. Compared to traditional techniques

such as ELISA, the LC-MS approach has the following advantages: short time for method

development, high specificity, less interference from patients’ own antibodies, and flexible

platforms to be applied to similar protein drugs.7 Besides, LC-MS is able to provide more

information about site-specific post-translational modifications in PK samples of protein drugs.

Here we developed an analytical platform in order to absolutely quantitate the concentration of

Zybodies in mouse serum using the MRM method. In our method, Zybodies were efficiently

enriched from mouse serum by two approaches. In a PK study, the half life of Candidate 2 (~ 48

hr in mouse serum) was determined by enriching the mAb with an antibody or protein A column,

and the peptide quantitation was realized by MS (XIC or MRM). In a metabolism study, the

degradation in mouse serum of mAb was assessed from the N-terminal to C-terminal end,

comparing intact and C-terminal truncated species, intact and N-terminal pyro-glu species,

oxidized and non-oxidized species, deamidated and non-deamidated species, and different glyco-

variants. Similar degradation profiles were obtained for the majority of species.

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5.3 Experiments

5.3.1 Materials

Candidate 1 and Candidate 2 were provided by Zyngenia (Gaithersburg, MD) in a liquid

formulation (containing 5.4 and 1.732 mg/ML, respectively). Candidate 1 and Candidate 2 are

recombinant human IgG1 with the variable Fc domains and C-terminal regions. Both of them are

bi-specific monoclonal antibodies with a molecule recognition domain fused to the C-termini of

anti-Her2 antibody. Candidate 1 and Candidate 2 pharmacokinetic samples were also provided

by our collaborator, containing different amounts of protein drugs in mouse serum at three time

points. Anti-huIgG antibody immobilized to agarose (500 μg antibodies is conjugated to 72 mg

agarose and the beads are stored in PBS), was provided by Zyngenia. Guanidine hydrochloride,

and ammonium bicarbonate, dithiothreitol (DTT), iodoacetamide (IAA), and mouse serum were

purchased from Sigma-Aldrich (St. Louis, MO). Protein-A magnetic beads were purchased from

Invitrogen (Carlsbad, CA). Trypsin (sequencing grade) was obtained from Promega (Madison,

WI), and Lysyl Endopeptidase (mass spectrometry grade) was purchased from Wako (Richmond,

VA). HPLC-grade water was from J. T. Baker (Bedford, MA), and formic acid and acetonitrile

were purchased from Fisher Scientific (Fair Lawn, NJ).

5.3.2 Preparation of spike-in samples

Variable amounts (0, 0.25, 1.5, 1, and 2.5 μg) of neat Candidate 1 or Candidate 2 were spiked

into 50 μL of mouse serum and used for pharmacokinetics studies to generate external standards

in which the Zybody concentration is 0, 5, 20, 20, and 50 μg/mL.

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5.3.3 Antibody (anti-Zybody) enrichment

Antibody conjugated agarose was gently pipetted before usage for even distribution. 50 μL anti-

huIgG slurry (containing 12.5 μg antibodies) was used to immunoprecipitate 50 μL spike-in

sample (diluted to 300 μL with lysis buffer). Anti-huIgG slurry was added to spin columns, and

washed with 200 μL Pierce IP lysis buffer for three times, then mixed with diluted spike-in

samples, and incubated with rotational mixing for 2 h at 4 °C. After incubating, the antibody

slurry was washed by lysis buffer three times and condition buffer twice. The IgG drug was

eluted twice by incubating with 20 μL elution buffer at room temperature for 15 minutes. The

eluent was concentrated to about 20 μL in speed vacuum before the subsequent SDS-PAGE

analysis.

5.3.4 Protein A enrichment

Protein A beads were firstly pipetted for even distribution before usage. 50 μL beads were used

for enriching each sample (the concentration of beads is 30 mg/mL, and 50 µL beads can bind up

to 10 µg antibodies). Protein drugs were spiked into 50 μL mouse serum as the following

description, and diluted to 500 µL by lysis buffer. The beads were activated by 200 µL PBS

twice, then mixed with diluted sample by rotating at room temperature for one hour. After

incubation, protein A magnetic beads were washed three times with 300 µL lysis buffer for three

times. By using a magnet, the procedure was greatly facilitated. The enriched protein was eluted

by boiling with 40 µL SDS running buffer for 15 min. The eluent was concentrated to about 20

µL before the following SDS-PAGE.

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5.3.5 SDS-PAGE and in gel digestion

A mini gel (4-12% Bis-tris) was used to separate the eluents from antibody or protein A

enrichment. Coomassie blue was used to stain proteins on gels. The gel bands containing the

heavy chain and light chain of the IgG drug (at the position of expected molecular weight) were

cut into small pieces for subsequent in-gel digestion. Coomassie blue stain was removed by

shaking the gel pieces overnight in 50% acetonitrile and 50% 25 mM ammonium bicarbonate.

The destained gel pieces were reduced by 100 µL 10 mM DTT in 100 mM NH4HCO3 and

incubated for 30 min at 56 °C, then alkylated with 100 µL 55 mM IAA in 100 mM NH4HCO3.

The gel pieces were covered by trypsin digestion reagent (12.5 ng/µL trypsin in 50 mM

NH4HCO3, pH 8.0), stored at 4 °C for 30 min. The trypsin reagent was replaced by 100 µL 25

mM NH4HCO3, and then incubated overnight at 37 °C. The digested peptides were extracted by

acetonitrile and 5% formic acid three times. All of the supernatant was collected and combined,

and then concentrated to about 5 µL. The concentrated tryptic digestion peptides were diluted to

an appropriate volume with mobile phase A before being subjected to LC-MS analysis.

5.3.6 LC-MS analysis

Peptides were separated by an ultimate 3000 nano LC pump (Dionex, Mountain View, CA) and a

self-packed C18 column (Magic C18, 5 µm particle size, 200 Å pore) (Michrom Bioresourese,

Auburn, CA), and analyzed by LTQ-Orbitrap mass spectrometer (Thermo Fisher Scientific, San

Jose, CA) equipped with New Objective (Waltham, MA) nanospray source. The column flow

rate was maintained at 200 nL/min after splitting. The LC gradient was from 5% B to 65% B in

60 min (A: water with 0.1% formic acid; B: acetonitrile with 0.1% formic acid), then from 65%

182

B to 80% B in 10 min, and hold at 80% B for 10 min. For the LTQ-orbi trap operation, full-scan

MS spectra (m/z 400-2000) were acquired, followed by 8 sequential MS2 scans using LTQ.

5.3.7 QQQ

An Agilent C18 (Zorbax Eclipse Plus C18, 2.1 x 50 mm) was used for the peptide separation.

Peptides were separated by Agilent 1200 series LC pump, and analyzed by Agilent 6460 Triple

Quad mass spectrometer. The column flow rate was maintained at 200 μL/min. The LC gradient

was from 5% B to 40% B in 20 min (A: water with 0.1% formic acid; B: acetonitrile with 0.1%

formic acid), then hold at 90% in 2 min and kept for 2 min. For the QQQ source, gas temperature

was kept at 300 °C, and gas flow was maintained at 5 L/min. For the sheath gas, temperature was

held at 350 °C, and the flow rate was kept at 11 L/min. The MRM acquisition method was listed

in Table 5-3.


Candidate 1 and Candidate 2 belong to the Zybody class - a novel family of therapeutic

monoclonal antibodies. They are essentially standard mAb, but with multi molecular recognition

domains attached to it. Here, we characterize the two molecules, and developed MRM methods

to study the pharmacokinetic of this new mAb by two different enrichment strategies. The

workflow of the experiment is shown in Figure 5-1.

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Figure 5-1: Workflow of IgG enrichment and LC-MS analysis

5.4.1 Protein A enrichment

Since the concentrations of protein drugs in PK sample are relatively low to be detected in our

study, we use two different approaches to enrich Candidate 1 and Candidate 2 from mouse serum.

One is traditional immunoprecipitation with company-provided antibodies to Candidate 1 and

Candidate 2, and the second method is to use protein A to enrich protein drugs by taking

advantage of the fact that protein A has a much higher affinity to Fc region in human antibodies

than to that in mouse antibodies. With the two complementary methods, the integrity of both N-

terminus and C-terminus of the antibody drugs can be determined. In spite of the fusion peptide

on Fc part, it has been proved that protein A enrichment is an effective method for improving the

detection limit in our experiment.

The protein drugs (from 0, 0.25, 1.5, 1, and 2.5 μg) were spiked into 50 μL mouse serum. The

184

spike-in samples were first captured by protein A beads, then eluted by SDS buffer under high

temperature (99 °C), and followed with SDS-PAGE for further separation (see Figure 5-2). The

expected heavy chain bands were cut for in-gel digestion and injected for LC-MS analysis.

Figure 5-2: Gel image of Zybody enrichment by protein A beads. All the heavy chain and light

chain sections were cut for subsequent in-gel tryptic digestion.

For MRM analysis, we tried to target all peptides in the heavy chain including T25H

glycopeptides in order to obtain deeper understanding of the degradation of Zybodies in serum.

Figure 5-6 shows the standard curve of the peak areas of one peptide measured by MRM used a

QQQ mass spectrometer.

185

5.4.2 Enrichment by antibody immunoprecipitation

An alternative enrichment method is to immunoprecipitate Zybody from mouse serum by its

antibody. Here, we used a company-provided anti-Candidate 1/Candidate 2 conjugated to protein

A agarose slurry. Due to the limited quantity of antibody slurry, the elution method we used was

low pH elution, as shown in Figure 5-1. The used antibody slurry was further washed with

elution buffer for 30 min to prevent carry-over, and then stored in PBS buffer for reuse. Similar

to enrichment by protein A beads, the eluent was first separated by SDS-PAGE, followed by in-

gel tryptic digestion. Then tryptic peptides were subjected for LC –MS analysis. The spike-in

samples for developing a standard curve were prepared following the same method. The gel

image of eluents of antibody immunoprecipitation is shown in Figure 5-3.

186

Figure 5-3: Gel image of Zybody enrichment by antibody immunoprecipitation. All the heavy

chain and light chains sections are cut for subsequent in-gel tryptic digestion.

5.4.3 Quantitation by data dependent mode

Here, we were initially trying to quantitate Candidate 2 by data-dependent mode on LTQ-

Orbitrap. The precursor ions were extracted from raw data, and the MS2 patterns were used for

identification. In Figure 5-3, T1H was shown as an example of the quantitative process. The

highest monoisotope m/z for doubly charged T1H, 941.51 was extracted from all the five

standards and three real samples (mass tolerance was 20 ppm), and a representative extracted ion

chromatogram is shown in Figure 5-4, part A. The MS2 pattern (data not shown) confirmed that

the peaks were T1H. The corresponding standard curve is shown in Figure 5-4, part B, with a

linear regression value 0.9662. The lowest quantitation concentration was 5 μg/mL. Table 5-1

lists the quantitative results of several representative peptides at three time points: 15 min, 48

hours, and 96 hours. It can be observed from the table that the concentration of Zybodies in

187

mouse serum decreased dramatically in the first 48 hours, and then declined slowly from 48 to

96 hours.

Figure 5-4: Quantifications of a representative peptide on the heavy chain (T1H) of Candidate 2

by data dependent mode on LTQ-Orbitrap.

Left: Representative of extracted ion chromatogram of T1H.

Right: The standard curve generated by the peak area.

The advantage of quantification by data-dependent mode by LTQ-Orbitrap is that almost all of

the peptides, including modified peptides and glycopeptides can also be quantitated at the same

time. This allows a prospective evaluation of the molecular degradation, as well as the site-

specific post-translational modifications in serum such as oxidation, deamidation, and

pyroglutamic acid formation. Besides, we can always extract the necessary information in the

future because all the MS and MS/MS data were collected. However, a slight drawback of this

method is the use of capillary column and nano-flow pump, which resulted in a slow flow rate

and a long separation speed. The analytical results will have need of a much longer time period

to be acquired especially when large sample sets are being handled. When we have better

188

understanding of the chromatographic behavior of Zybody’s tryptic peptides, it is possible to

enhance the speed of analysis significantly and therefore reduce the time of analysis by using the

large id column and monitoring multiple reactions in a triple quadrupole mass spectrometer.

Table 5-1: Concentration of Candidate 1 in mouse serum at three time points

Peptide Candidate 1 concentration (μg/mL) ± CV% a

0.25 hr 48 hr 96 hr

T1H 23.73 ± 31.31% 14.35 ± 46.91% 10.73 ± 13.53%

T2H 27.50 ± 22.87% 11.43 ± 39.99% 13.62 ± 21.34%

T3H 23.01 ± 25.12% 10.00 ± 42.99% 12.19 ± 6.75%

T5H 23.12 ± 43.16% 9.98 ± 41.41% 12.37 ± 32.67%

T6H 24.50 ± 35.72% 10.06 ± 47.00% 12.65 ± 31.54%

T38H 24.91 ± 34.33% 12.97 ± 32.82% 7.38 ± 30.25%

a CV% is measured based on two runs.

5.4.4 Optimization of LC condition in Agilent 1200 series

The flow rates for both sample injection and gradient were optimized for higher intensity for all

peptides as shown in Table 5-3. When the loading flow rate increased from 10 μL/min to 80

μL/min, the intensity of glycopeptides improved significantly, and reduced slightly when the

loading flow rate increased to 100 μL/min. The intensity of other peptides shows similar trend

when the loading flow rate changed. Besides, when the gradient flow rate decreased from 400

μL/min to 200 μL/min, the intensity of all peptides increased significantly. For example, a 10

times higher intensity for G0 under the gradient flow rate of 200 μL/min. In spite of the

advantages of high flow rate such as faster separation and low residues, a balanced flow rate was

189

selected.

Table 5-2: Optimization of gradient flow rate and loading flow rate

Gradient flow rate

(μL/min) 200 400

Loading flow rate

(μL/min) 10 20 30 50 80 100 100

Peptide m/z Intensity Intensity Intensity Intensity Intensity Intensity Intensity

G0 879.0 3.00E+05 2.60E+05 3.20E+05 2.80E+05 2.80E+05 3.20E+05 2.80E+04

G1 933.0 8.20E+04 6.80E+04 8.20E+04 8.20E+04 8.20E+04 9.00E+04 1.10E+04

G2 987.1 1.10E+04 8.00E+03 9.00E+03 5.50E+03 6.00E+03 3.50E+03 4.00E+03

T42H 1273.2 3.30E+05 3.00E+05 3.20E+05 3.20E+05 3.20E+05 3.00E+05 1.50E+05

T15H 1344.2 4.80E+05 5.50E+05 5.00E+05 4.90E+05 4.90E+05 4.90E+05 2.80E+04

5.4.5 Optimization of collision energy in MRM

With the development of triple quadrupole mass spectrometer, several compound dependent

parameters, including declustering potential (DP), entrance potential (EP), and collision cell exit

potential (CXP), will not greatly affect the intensities of product ions generated from different

precursor ions. Therefore in setting up MRM methods in QQQ, a crucial procedure is to

determine the collision energy in CID for each tryptic peptide that needs to be quantitated. Here,

the most suitable energy was chosen based on 1) full fragmentation of precursor ion; and 2) high

intensity of three product ions. An example of the selection of collision energy is shown in

Figure 5-5. For T1H, 30 eV was selected because it can not only fragment the precursor ion

completely, but also generate three product ions with relatively high intensity. The three product

ions are y61+, y7

1+ and y131+.

190

Figure 5-5: Optimization of collision energy in CID for T1H. 30.0 eV was selected based on the

fragmentation of precursor ion and the intensity of product ions.

Here, three product ions were monitored in MRM for further confirmation of precursor ions;

therefore the total number of reactions monitored was three times the number of precursor ions.

Since we were trying to monitor all tryptic peptides in the heavy chain, as well as some

modifications, 21 minutes LC gradient and 12 segments were used for better separation and

monitoring. The MRM method is shown in Table 5-3.

Additional to normal tryptic peptides, the glycopeptides (G0 and G1), modified peptides

(oxidation forms of T21H and T41H, pyro-E form of T1H), as well as the important heavy chain

C-terminus (T42H), were also under monitoring. All of them were successfully quantified except

for T42H, which was believed to be intact because of the identification of three product ions.

Using the same method, all peptides on light chain can be monitored (data not shown).

Table 5-3: MRM method for monitoring all tryptic peptides on the heavy chain of Candidate 2a

RT

(min) Peptide

RT

(min)

Precursor

ion m/z

Product

ion 1

m/z

Product

ion 2

m/z

Product

ion 3

m/z

Collision

energy

2.0 T7H, 2+ 2.82 341.8 207.0 448.2 519.3b 12

T39H, 2+ 3.67 288.1 262.1 361.3 462.2 10

4.3 G0, 3+ 4.63 879.0 203.6 1134.8 1216.0 12

G1, 3+ 4.61 932.9 336.1 1216.0 1297.0 8

5.3 T6H, 2+ 5.80 543.5 248.9 405.3 809.5 16

191

RT

(min) Peptide

RT

(min)

Precursor

ion m/z

Product

ion 1

m/z

Product

ion 2

m/z

Product

ion 3

m/z

Collision

energy

T11H, 2+ 5.89 667.9 584.8 748.0 847.1 24

T21H[O], 2+ 5.94 426.4 375.1 522.0 635.3 16

6.2 T6H, 2+ 5.80 543.5 248.9 405.3 809.5 16

T9H, 2+ 6.38 485.4 175.8 608.1 721.3 16

T30H, 2+ 6.51 419.6 327.7 486.2 654.3 12

T21H, 2+ 6.90 418.2 375.2 506.2 619.2 12

7.2 T2H, 2+ 7.78 584.5 665.0 736.3 807.1 20

T34H+T35H, 3+ 7.49 635.9 337.1 537.7 726.4 20

T41H[O], 4+ 7.98 705.4 169.6 828.2 979.8 20

T41H[O], 5+ 7.98 564.5 527.4 653.1 702.4 12

8.3 T34H+T35H, 3+ 7.49 635.9 337.1 537.7 726.4 20

T5H, 2+ 8.45 415.9 246.0 531.3 660.3 12

T14H, 2+ 8.58 661.6 245.9 576.2 760.5 30

T3H, 2+ 8.75 545.3 139.5 597.1 710.4 24

T10H, 2+ 8.85 656.0 216.0 748.4 1024.3 24

9.3 T36H, 2+ 9.50 581.5 243.1 820.2 919.3 20

T13H, 2+ 9.72 594.0 418.2 699.4 846.3 20

T41H, 4+ 9.65 701.5 159.9 297.9 823.5 24

T41H, 5+ 9.65 561.4 523.4 648.1 697.0 12

T23H, 3+ 10.00 560.2 469.1 615.9 708.7 16

10.1 T23H, 3+ 10.00 560.2 469.1 615.9 708.7 16

T22H, 3+ 10.23 714.0 199.0 328.2 472.3 30

T1H, 2+ 10.28 941.8 586.3 714.5 1313.4 30

T1H, 3+ 10.28 628.3 489.3 586.3 714.5 24

11.5 T37H, 2+ 12.55 1273.4 426.2 764.2 950.7 48

T37H, 3+ 12.55 849.1 259.2 764.0 949.8 24

Pyro-T1H, 2+ 12.62 933.2 586.3 714.5 938.1 24

T20H, 4+ 13.09 712.3 566.3 799.6 912.4 20

T38H, 2+ 13.07 937.8 522.2 836.7 1150.6 40

T38H, 3+ 13.07 625.6 657.1 836.3 948.6 12

14.0 T26H, 3+ 14.34 603.7 712.7 762.1 805.8 16

T42H, 3+ 14.49 1273.2 270.2 971.3 1182.3 28

T42H, 4+ 14.49 955.3 487.9 591.7 1294.9 24

15.0 T12H, 3+ 15.29 929.0 581.1 879.1 944.5 16

16.0 T15H, 5+ 16.69 1344.2 1217.5 1317.9 1573.4 32

T15H, 6+ 16.69 1120.1 911.4 1030.1 1217.3 20

192

a MRM acquisition method for the peptides on light chain is listed in Supplementary Table S5-1.

b Underlined numbers are m/z value of the product ion used for quantification.

5.4.6 Candidate 2 absolution quantitation by MRM

After optimization of LC conditions and selection of collision energies for each peptide, the

peptides on the heavy chain of Candidate 2 can be absolutely quantified based on the MRM

method built, as shown in Table 5-3. Each peptide was fully fragmented into three or more

product ions, from which one product ion (m/z value is underlined in Table 5-3) was used for

quantification, and the other two product ions were used for identification. The peak area of the

quantification reaction was extracted for quantification, as one example shown in Figure 5-6.

This figure shows one of the three reactions monitored for doubly charged T1H (2+): 941.8-

>586.3, the other two reactions, 941.8->714.5, and 941.8->1313.4 were extracted for

confirmation of T1H (data not shown). The standard curve based on the five standards is shown

on the right panel. The lowest detection concentration is 5 μg/mL with a good linearity

(R2=0.9858). With the standard curve, the concentration of Zybody based on T1H can be

calculated as shown in Table 5-4. It can be observed that the standard deviation for both

standards and real samples were significantly reduced compared to the quantification by data-

dependent mode using the LTQ-Orbitrap as shown in Figure 5-4. Besides, both the gradient

length and clean-up time have been significantly reduced compared to quantitation by LTQ-

Orbitrap because of the application of analytical scale column and MRM mode.

193

Figure 5-6: Representative of quantitation results of Zybodies.

Top: Representative of extracted ion chromatogram of T1H and corresponding standard curve

using protein A enrichment.

Bottom: Representative of extracted ion chromatogram of T1H and corresponding standard curve

using anti-Zybody immunoprecipitation.

Following the same procedure, all of the peptides monitoring as listed in Table 5-3 can be

quantified. Although each peptide has a different response in the mass spectrometer, it is still

possible to track the concentration changes for each peptide in real samples, and then profile

protein degradation from N-terminal to C-terminal ends including the glycopeptides and

modified peptides, as shown in Table 5-4. It can be observed that the modified peptides, pyro-

T1H, oxidized T21H and T41H have a similar degradation trend as their original forms.

194

5.4.7 Comparison of two enrichment methods

In this study, Zybody molecules were enriched from mouse serum by two different methods

before subsequent LC-MS analysis: protein A and anti-Zybody. As described in the experimental

section, protein A beads are commercially available magnetic beads, and anti-Zybodies were

conjugated to protein A agarose provided by our collaborator. The parallel enrichment can further

determine the functionality of the anti-Zybody antibodies. After comparing the quantitation

results of two different enrichment methods, the Zybody concentrations determined by anti-

Zybodies showed high consistency among all peptides with those by protein A enrichment. This

can confirm that the anti-Zybodies developed by our collaborator functioned properly and

efficiently to capture Zybodies in serum.

Table 5-4: Comparison of the concentrations of Candidate 2 determined by two enrichment

methods

Protein A Her1H8 concentration in mouse serum (μg/ML)

Time T1H Pyro-T1H T21H T21H[O] T41H T41H[O] G0

0.25 h 13.1 ±

12.5%

16.1 ±

50.0%

8.1 ±

16.7%

16.0 ±

27.3%

7.6 ±

24.9%

11.8 ±

44.9%

15.1 ±

13.4%

48 h 3.3 ±

12.1%

4.3 ±

47.1%

3.7 ±

7.3%

6.3 ±

32.4%

4.1 ±

33.1%

8.1 ±

11.5%

8.0 ±

22.3%

antibody Her1H8 concentration in mouse serum (μg/ML)

Time T1H Pyro-T1H T21H T21H[O] T41H T41H[O] G0

0.25 h 9.2 ±

18.6%

11.2 ±

32.5%

5.8 ±

6.9%

17.6 ±

9.3%

4.9 ±

85.5%

10.9 ±

94.4%

8.5 ±

19.9%

48 h 5.4 ±

12.2%

4.0 ±

14.3%

1.6 ±

8.6%

13.6 ±

5.2%

1.6 ±

12.4%

1.4 ±

173.2%

7.0 ±

9.0%

5.5 Conclusion

Both protein A and anti-Zybody can efficiently enrich Zybodies (Candidate 1 and Candidate 2)

from mouse serum. The concentrations of Zybody determined by enrichment of protein A and

195

anti-Zybody highly correlate with each other. This indicates that anti-Zybody functions properly

to target Zybody molecules. In PK study, the half life of Zybodies (~ 48 hr in mouse serum) was

determined by enriching the mAb with antibody or protein A column, and most of the tryptic

peptide were quantitated by MRM. In the metabolism study, the degradation in mouse serum of

Zybodies was assessed from the N-terminal to C-terminal end, intact and N-terminal pyro-glu

species, oxidized and non-oxidized species, and different glyco-variants. Similar degradation

profiles were obtained for the majority of species.

5.6 Supplementary Table S5-1

MRM method for monitoring all tryptic peptides on the light chain of Candidate 2

Rt

(min)

Tryptic peptids

on light chain

RT

(min)

Precursor

ion m/z

Product

1

Product

2

Product

3

Collision

energy

0.1 T16L, 1+ 1.2 625.3 136.1 258.0 276.0 34

T16L, 2+ 1.2 313.1 140.8 276.4 554.3 6

T6L, 1+ 1.2 553.2 120.2 158.1 245.2 38

T6L, 2+ 1.2 277.1 120.2 319.1 406.0 7

2.0 T19L+T20L, 2+ 3.3 435.3 207.2 635.3 691.4 12

4.2 T13L, 1+ 4.6 560.3 159.0 228.2 333.2 30

T13L, 2+ 4.6 280.7 129.2 269.4 444.3 12

T2L, 2+ 4.8 375.3 436.1 549.4 650.4 10

5.2 T14L, 3+ 5.5 712.8 301.2 707.5 893.5 20

6.2 T18L, 3+ 7.0 626.2 135.8 235.1 808.0 21

T1L[O], 2+ 7.3 948.1 692.4 1075.8 1391.9 30

T1L[O], 3+ 7.3 632.5 533.1 691.2 891.1 20

7.6 T3L, 3+ 8.0 664.6 606.1 853.7 917.1 18

8.4 T1L, 2+ 7.0 940.1 691.3 1075.8 1162.3 32

T1L, 3+ 7.0 627.2 201.0 539.0 691.4 12

10.0 T15L, 2+ 10.6 752.2 286.3 449.2 836.8 28

13.0 T10L, 2+ 13.4 973.7 913.4 1060.9 1604.0 28

T10L, 3+ 13.4 649.6 444.1 457.5 914.4 12

T5L, 2+ 14.2 887.1 515.2 765.4 878.4 25

T5L, 3+ 14.2 591.8 359.2 602.3 765.6 12

14.5 T7L, 4+ 15.0 1048.2 947.0 1139.9 1419.9 28

196

T11L, 2+ 15.1 899.7 272.3 1196.5 1295.8 28

T11L, 3+ 15.1 600.1 435.5 582.4 810.6 18

5.7 References

1. Nahta, R.; Hung, M. C.; Esteva, F. J., The HER-2-targeting antibodies Trastuzumab and

pertuzumab synergistically inhibit the survival of breast cancer cells. Cancer Res 2004, 64 (7),

2343-6.




109-19.

3. Holmes, D., Buy buy bispecific antibodies. Nat Rev Drug Discov 2011, 10 (11), 798-800.

4. McDonagh, C. F.; Huhalov, A.; Harms, B. D.; Adams, S.; Paragas, V.; Oyama, S.; Zhang,

B.; Luus, L.; Overland, R.; Nguyen, S.; Gu, J.; Kohli, N.; Wallace, M.; Feldhaus, M. J.; Kudla, A.

J.; Schoeberl, B.; Nielsen, U. B., Antitumor activity of a novel bispecific antibody that targets the

ErbB2/ErbB3 oncogenic unit and inhibits heregulin-induced activation of ErbB3. Mol Cancer

Ther 2012, 11 (3), 582-93.

5. LaFleur, D.; Abramyan, D.; Kanakaraj, P.; Smith, R.; Shah, R.; Wang, G.; Yao, X. T.;

Kankanala, S.; Boyd, E.; Zaritskaya, L., Monoclonal antibody therapeutics with up to five

specificities: Functional enhancement through fusion of target-specific peptides. mAbs 2013, 5

(2), 208-18.

6. Kanakaraj, P.; Puffer, B. A.; Yao, X. T.; Kankanala, S.; Boyd, E.; Shah, R. R.; Wang, G.;

Patel, D.; Krishnamurthy, R.; Kaithamana, S., Simultaneous targeting of TNF and Ang2 with a

novel bispecific antibody enhances efficacy in an in vivo model of arthritis. mAbs 2012, 4, 600-

13.

7. Lu, Q.; Zheng, X.; McIntosh, T.; Davis, H.; Nemeth, J. F.; Pendley, C.; Wu, S. L.;

Hancock, W. S., Development of different analysis platforms with LC-MS for pharmacokinetic

studies of protein drugs. Anal Chem 2009, 81 (21), 8715-23.

197

Chapter 6 Conclusions and Future Work

This dissertation describes the application of HPLC and MS to the analysis of the structure of

protein kinase ErbB2 and the therapeutic applications. We have identified potential protein

signatures related to the over-expression of ErbB2 and EGFR, as well as two different ErbB2

isoforms in breast cancer cell lines. A potential therapeutic bi-specific monoclonal antibody has

also been characterized and quantitated in mouse serum using LC-MS/MS.

In Chapter 2 genomic and proteomic approaches were integrated to investigate three breast

cancer cell lines and the related signaling networks. A deeper proteomic study can be realized

with patients’ samples and advanced mass spectrometers, and therefore additional sub-pathways

can be revealed for a better understanding of ErbB2-positive breast cancer and inflammatory

breast cancer. The potential protein signatures identified in this study also need to be validated.

Chapter 3 provides a proteomic method to discover ErbB2 isoforms in the cell lysate of breast

cancer cell lines. A more selective and sensitive MRM-based method can be established in order

to identify and quantify ErbB2 isoforms in other ErbB2-positive breast cancer cell lines. Besides,

with the development of mass spectrometers, top-down proteomics becomes a very promising

tool for the identification of protein isoforms. The developed method can also be applied in the

study of breast cancer patients’ samples. This will significantly support the research of Herceptin

resistance. In addition, glycosylation of the identified ErbB2 isoforms can also be investigated.

Chapters 4 and 5 describe a potential therapeutic monoclonal antibody drug for the potential

treatment of ErbB2 positive breast cancer, named as Zybody. Two Zybody candidates were

198

comprehensively characterized, and an analytical method was developed to study the

pharmacokinetics and metabolism of Zybodies in mouse serum. The primary sequence of

Zybody molecules can be further modified to realize multiple antigen binding and to enhance the

drug stability during circulation. For pharmacokinetics and metabolism study, fully stable isotope

labeled Zybodies can be manufactured and introduced to reduce the errors during sample

handling, and therefore the quantitation results will be more accurate. Moreover, protein

enrichment and digestion procedures can also be optimized to realize more high-throughput

analysis. For example, most of the experiments can be accomplished using multi-channel

pipettes and 96-well plates, and the in-gel digestion can also be replaced by in-solution digestion

using two enzymes. This can shorten the sample preparation time by two or even three days.

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Author: Young-Ki Paik, Seul-Ki Jeong,Gilbert S Omenn, MathiasUhlen, Samir Hanash, Sang YunCho

Publication: Nature Biotechnology

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Title: Orbitrap Mass Spectrometry: AProposal for Routine Analysis ofNonvolatile Components ofPetroleum

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Title: Fragmentation Characteristicsof Collision-InducedDissociation in MALDI TOF/TOFMass Spectrometry

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Title: Genome Wide Proteomics ofERBB2 and EGFR and OtherOncogenic Pathways inInflammatory Breast Cancer

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