Blood Proteomics and Cancer BiomarkersSam Hanash

Potential Conflict of Interest

• Dr. Samir Hanash– None

Blood based Signatures for Lung cancer/epithelial tumors

Risk

asse

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ent

E

arly

det

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Mol

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to

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uide

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TUMOR

MICROENVIRONMENTTUMOR CELL

GENOME

mutationsMethylation changesAmplificationDeletions/rearrangements

Infiltrating CellsStromaCytokinesG.F.

DRUG EFFECT

BLOODNucleic acids:

- Mutated DNA - Methylated DNA- Blood cell RNA profile, tumor MicroRNA

Altered protein and metabolic profiles- Tumor cell derived- host response derived

Immune response signatures- Immune cells- Cytokines/chemokines

Circulating tumor cells

TUMOR

MICROENVIRONMENTTUMOR CELL

GENOME

mutationsMethylation changesAmplificationDeletions/rearrangements

Infiltrating CellsStromaCytokinesG.F.

DRUG EFFECT

BLOODNucleic acids:

- Mutated DNA - Methylated DNA- MicroRNA

Altered protein and metabolic profiles- Tumor cell derived- host response derived

Immune response signatures- Immune cells- Cytokines/chemokines

Circulating tumor cells

COMPUTATIONAL BIO

LOGY

Reviews

• The grand challenge to decipher the cancer proteome. Hanash S, Taguchi A, Nature Reviews Cancer, Aug 2010

• Emerging molecular biomarkers and strategies to detect and monitor cancer from blood. Hanash S, Baik S, Kallioniemi O. Nat Rev Clin Oncology in press

Lung Cancer Molecular Diagnostics Collaborative Group

Nucleic acids: - Mutated DNA P. Mack UC Davis- Methylated DNA I. Laird, USC, A. Gazdar UT Southwestern- Tumor MicroRNA M. Tewari, FHCRC

Altered protein and metabolic profiles- Proteomics S. Hanash FHCRC, S. Lam BCCA- Metabolomics O. Fiehn UC Davis

Immune response signatures- Cytokines/Chemokines S. Dubinett, UCLA- Autoantibodies S. Hanash, FHCRC

Circulating tumor cells S. Dubinett, UCLAData integration and modeling J. Zhu and S. Friend SAGE

Funding Support

• NIHNational Cancer InstituteNational Heart Lung and Blood Institute

• Department of Defense Lung Cancer Research Program

• FoundationsCanary FoundationLabrecque FoundationProtect Your Lungs Foundation

International Collaboration

• Qinghua Zhou, Lung Cancer Insitute, Tianjin China

• Tony Mok, Chinese University of Hong Kong

• Tetsuya Mitsudomi. Nagoya, Japan

• Rafael Rosell, Catalan Institute of Oncology, Barcelona, Spain

Cohorts for Lung Cancer Studies

• Carotene and Retinol Trial (CARET) Cohort

• NYU and BCCA lung cancer screening Cohorts

• Women’s Health Initiative Cohort

• Physicians’ Health Study Cohort

• Asian Cohort Consortium

One million subjects with varying risks for smoking and non-smoking related lung cancer

Proteomic signatures

ChemicalModifications eg

altered glycosylation

Alternative Splicing Isoforms

Protein Cleavages egshed receptors andadhesion molecules

Formation of complexes eg

immune complexes

Altered dynamics of protein sorting eg

release of chaperone proteins

TranslationalTranslationalImplicationsImplications

Blood Based Lung Cancer Diagnostics

• Assessment of lung cancer risk among smokers, former smokers and never smokers

• Early detection

• Diagnosis of indeterminate nodules

• Development of a marker panel to monitor treatment response, disease regression and progression

Which is cancer?

MolecularClassification

Early detectionSignatures

Protein signatures of risk

Proteomic Signatures for Lung CancerProteomic Signatures for Lung Cancer

Blood collected 3-5 yrs prior to lung Ca Dx

Blood collected0-18 monthsprior to Dx

Blood collectedat Dx

MolecularClassification

Early detectionSignatures

Protein signatures of risk

Proteomic Signatures for Lung CancerProteomic Signatures for Lung Cancer

Blood collected 3-5 yrs prior to lung Ca Dx

Blood collected6-18 monthsprior to Dx

Blood collectedat Dx

Mouse Models and Cell li

nes

Profiling strategies

• Deep quantitative proteomic profiling to search directly in serum and plasma for circulating biomarkers

• Proteomic profiling the humoral immune response to tumor antigens for seropositivity

• Profiling for altered glycan structures in circulating proteins and tumor antigens

• NSE

mM –3

µM –6

nM –9

pM -12

fM -15

aM -18

Albumin

Alkaline Phosphatase

Immunoglobulins

TNF

Transferrin

1012

10 100 1’000 10’000

MajorSerumProteins

DiseaseTissueMarkers

SignalingProteins

• PSA

The plasma proteom

e

Immunodepletion(top X proteins)

Concentration, buffer exchange and labeling

SAMPLES MIXED

ANION EXCHANGE CHROMATOGRAPHY

REVERSE-PHASECHROMATOGRAPHY

SAMPLE AIsotopic labeling

SAMPLE BIsotopic labeling

Shotgun LC/MS/MSOf individual fractions

Controls Cases

2.26

EGFR

Plasma Profiling Strategies

• Cases vs matched controls

• Before and after tumor resection

• Arterial vs venous comparison

Overview of Project

To identify differentially existing proteins in blood draining lung tumor

pulmonary venous effluent systemic radial arterial blood

TumorTumor

Pool samples

Alkylation with HEAVY acrylamide Alkylation with LIGHT acrylamide

Fractionation

LC-MS/MS

CXCL7

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

1-Specificity

Sensitivity

Area under the curve: 0.83995% confidence interval (0.765, 0.913)

J Clin Oncol 2009; 27:2787-92

Figure 5

A.Taguchi, K. Politi et al.

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

False Positive Fraction

Tru

e P

osi

tive

Fra

ctio

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CARET set

AUC = 0.839

B

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

False Positive Fraction

Tru

e P

osi

tive

Fra

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0-6 month

AUC = 0.893

C

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

False Positive Fraction

Tru

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osi

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Fra

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7-11 month

AUC = 0.888

D

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

False Positive Fraction

Tru

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Fra

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EDRN set

AUC = 0.866

A Newly Dx Pre-Dx

0-6 m fore Dx 7-11m before Dx

Mouse models of cancer

• Substantial heterogeneity of human subjects• Engineered animal models mimic human disease

counterparts• Sampling mice at defined stages of tumor

development• Potential to identify markers for driver genes/pathways• Potential to target and refine therapy (Co-clinical)

Human vs animal models

• Lung Cancer– Kras (Varmus/Politi), EGFR (Varmus/Politi), Urethane (Kemp/Schrump),

Small Cell (Sage)• Breast Cancer

– HER2/Neu (Chodosh), PyMT (Pollard), Telomerase (DePinho/Jaskelioff)• Colon Cancer

– D580 APC (Kucherlapati)• Pancreatic Cancer

– Kras (DePinho/Bardeesy) • Ovarian Cancer

– Kras/Pten (Jacks/Dinulescu)• Prostate Cancer

– Strain Comparison (DePinho)• Confounders

– Acute Inflammation (Kemp/Spratt), Chronic Inflammation (Kemp/Spratt),

Mouse Models Studied to Date

• Proteomic profiles from similar cancer types cluster together: Lung, breast, pancreatic

• Models with confounding conditions cluster together

Lung adenocarcinomas induced in mice by mutant EGF receptors found in human lung cancers respond to a tyrosine kinase inhibitor or to down-regulation of the receptors.Politi K, Zakowski MF, Fan PD, Schonfeld EA, Pao W, Varmus HE. Genes Dev. 2006 Jun 1;20(11):1496-510)

EGFR MOUSE MODEL

EGFR MOUSE MODELNETWORK #1Cellular Assembly and Organization, Cancer, Cellular Movement

EGFR MOUSE MODELNETWORK #2Hematological System Development and Function, Organismal Development, Cancer

KRAS MOUSE MODEL

KRAS MOUSE MODELNETWORK #2Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry

Rapid induction of mammary tumors following doxycycline treatment in an ERBB2 model of breast cancer (100% between 6-12 weeks)

C. KempK. SprattS. Pitteri

Rapid regression of mammary tumors following doxycycline withdrawl

Additional controls: Models of inflammation and angiogenesis

Chodosh Preclinical

Chodosh 0.5 cm

Chodosh 1.0 cm

What lies ahead

• Blood based diagnostics in combination with imaging for early detection

• Risk factors and molecular signatures for common cancers

• Further discoveries of driver mutations and altered pathways and networks through integrated genomics and proteomics

6607

6138

5505

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1% error

Human Plasma Proteins

total >=2 pep >=3 pep

Further advances in Proteomic technology

• Increased depth/breadth of analysis

• PTMs: Cleavages, Glycosylation

• Genomic analysis of proteomic data– Alternative splicing– SNPs

EGFR

1_2

3_23

24_2

8

29_2

9

30_3

7

38_5

3

54_7

2

73_8

0

81_9

8

99_1

08

109_

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Pept_Sequences MR

PS

GT

AG

AA

LLA

LLA

ALC

PA

SR

ALE

EK

K VC

QG

TS

NK

LTQ

LGT

FE

DH

FLS

LQR

MF

NN

CE

VV

LGN

LEIT

YV

QR

NY

DLS

FLK

TIQ

EV

AG

YV

LIA

LNT

VE

R

IPLE

NLQ

IIR

GN

MY

YE

NS

YA

LAV

LSN

YD

AN

K

TG

LK

ELP

MR

NLQ

EIL

HG

AV

R

FS

NN

PA

LCN

VE

SIQ

WR

DIV

SS

DF

LSN

MS

MD

FQ

NH

LGS

CQ

K

CD

PS

CP

NG

SC

WG

AG

EE

NC

QK

LTK

IICA

QQ

CS

GR

CR

GK

SP

SD

CC

HN

QC

AA

GC

TG

PR

ES

DC

LVC

R

K FR

DE

AT

CK

DT

CP

PLM

LYN

PT

TY

QM

DV

NP

EG

K

YS

FG

AT

CV

K

K CP

R

NY

VV

TD

HG

SC

VR

AC

GA

DS

YE

ME

ED

GV

R

K CK

K CE

GP

CR

K VC

NG

IGIG

EF

K

DS

LSIN

AT

NIK

HF

K

NC

TS

ISG

DLH

ILP

VA

FR

GD

SF

TH

TP

PLD

PQ

ELD

ILK

TV

K

EIT

GF

LLIQ

AW

PE

NR

TD

LHA

FE

NLE

IIR

GR

TK

QH

GQ

FS

LAV

VS

LNIT

SLG

LR

SLK

EIS

DG

DV

IISG

NK

NLC

YA

NT

INW

K

K LFG

TS

GQ

K

TK

IISN

R

GE

NS

CK

AT

GQ

VC

HA

LCS

PE

GC

WG

PE

PR

breast_IP0019_AX02_SG56to61_Run2breast_IP0019_AX10_SG48to51 XXXbreast_IP0026_AX01_SG49to52 XXXbreast_IP0026_AX01_SG53to56_Run2breast_IP0026_AX02_SG53to56_Run2breast_IP0026_AX05_SG46to48_Run2 0.83 0.82breast_IP0026_AX06_SG49to52 XXXbreast_IP0026_AX08_SG49to52_Run2breast_IP0026_AX10_SG49to52 0.94breast_IP0026_AX10_SG49to52_Run2 1.1breast_IP0026_AX11_SG49to52 0.47 0.83 0.92breast_IP0026_AX11_SG49to52_Run2 0.94 0.73breast_IP0026_AX11_SG62to72_Run2breast_IP0026_AX12_SG26to30breast_IP0026_AX12_SG49to52 0.87breast_IP0026_AX12_SG49to52_Run2 XXX

colon_IP0036_AX06_SG39to40 1.06colon_IP0037_AX02_SG33to34 XXXcolon_IP0037_AX04_SG25to26 XXXcolon_IP0037_AX05_SG31to32colon_IP0037_AX05_SG39to40colon_IP0037_AX07_SG41to42colon_IP0037_AX08_SG31to32 XXXcolon_IP0038_AX03_SG01to25colon_IP0038_AX06_SG40to41 1.02colon_IP0039_AX04_SG39to40 XXX 0.86 0.92 XXXcolon_IP0039_AX06_SG39to40 XXXcolon_IP0039_AX08_SG25to26 XXX XXXcolon_IP0041_AX02_SG43to72 XXXcolon_IP0041_AX04_SG37to38 XXX 0.34colon_IP0041_AX06_SG39to40 0.88colon_IP0042_AX01_SG01to24 XXXcolon_IP0042_AX01_SG39to40 XXXcolon_IP0042_AX01_SG41to42 XXX XXXcolon_IP0042_AX01_SG43to72 XXXcolon_IP0042_AX02_SG39to40 XXXcolon_IP0042_AX03_SG01to24 XXXcolon_IP0042_AX03_SG41to42 XXX XXXcolon_IP0042_AX04_SG41to42 XXXcolon_IP0042_AX04_SG43to72 XXXcolon_IP0042_AX05_SG39to40 XXXcolon_IP0042_AX05_SG41to42 XXXcolon_IP0042_AX06_SG41to42 XXXcolon_IP0042_AX07_SG41to42 XXX XXXcolon_IP0042_AX08_SG33to34 XXXcolon_IP0043_AX01_SG41to42 XXXcolon_IP0043_AX02_SG41to42 XXXcolon_IP0043_AX05_SG39to40 XXXcolon_IP0043_AX05_SG41to42 XXXcolon_IP0043_AX06_SG39to40 XXX

hormone_IP0019_AX03_SG58to72_conc XXXhormone_IP0021_AX02_SG48to57 XXX XXXhormone_IP0021_AX07_SG48to57 XXX XXXhormone_IP0021_AX08_SG48to57 XXX XXXhormone_IP0021_AX09_SG48to57 XXXhormone_IP0021_AX11_SG48to57 XXXhormone_IP0023_AX04_SG50to55 XXXhormone_IP0028_AX04_SG47to52 0.83

lung_IP0022_AX01_SG50to53 XXXlung_IP0022_AX02_SG50to53 XXX XXX XXXlung_IP0022_AX04_SG50to53 XXX XXXlung_IP0022_AX05_SG50to53 XXXlung_IP0022_AX06_SG50to53 XXX XXXlung_IP0022_AX07_SG50to53 XXXlung_IP0022_AX08_SG50to53 XXXlung_IP0022_AX12_SG50to53 XXX XXXlung_IP0024_AX04_SG50to53 XXXlung_IP0024_AX12_SG50to53 2.26

++++++++

EXTRACELLULAR

Selected 5 raw data for glycosylation investigation

2.26

EGFR

Asn 444 (K) QHGQFSLAVVGLNITSLGLR (S)

AX

01

1st D

2nd D

RP

_SG

41to

42R

P_S

G39

to40

AX

02

AX

08

AX

03

AX

04

AX

05

AX

06

AX

07

Acknowledgements

Genomic Studies

Deep genomic sequencingQ. Zhou Tianjin Lung Cancer Inst.X. Yang, H. Xiao Shanghai Genome Center

DNA methylation Adi Gazdar UT Southwestern

Ite Laird USC

DNA mutation detection in bloodP. Mack, D. Gandara UC Davis

Gene copy changesS. Lam, W. Lam BCCA

Transcriptomic Studies

RNA profilingD. Beer, J. Taylor, U of Michigan K. Shedden, R. KuickD. Misek, T. Giordano A. Gazdar UT Southwestern

MicroRNAM. Tewari FHCRC

Metabolomic Studies

Glycan analysisS. Myamoto U C Davis C. Lebrilla

VOCs, Primary and secondary metabolites,Lipid profiles

O. Fiehn UC Davis

TK inhibitor Studies

FHCRCK. Eaton, R. Martins, S. Wallace, M. McIntosh

USCD. Agus, P. Mallick, K. Kani

UCLAA. Jain

Cohort Studies

Women’s Health Initiative R. Prentice, C. Li FHCRC

CARETG. Goodman M. Thornquist M. BarnettC. Edelstein FHCRC

Physicians’ Health StudyR. PereraA. Schneider Columbia U.

New York CT Screening CohortW. Rom N.Y.U

Mouse models of cancer

Ovarian model

T. Jacks, D. Dinulescu MIT/Harvard

Lung models

K. Politi, H. Varmus MSKCC

C. Kemp, K. Spratt FHCRC

Colon Cancer

R. Kucherlapati, K. Hung Harvard

Pancreatic model

R. DePinho, N. Bardeesy Dana Farber

Breast cancer

L. Chodosh, R. Depinho, C. Kemp MMHCC

FHCRC Statistical Analysis

Ziding FengMark ThornquistMatt BarnettRoss PrenticeMartin McIntoshCharles KooperbergLynn AmonPei WangLin ChenAaron Aragaki

Hanash Laboratory

Mass spectrometry studies Hong Wang, Alice Chin, Vitor Faca, Allen Taylor

Protein microarray studies Ji Qiu, Jon Ladd, Rebecca Israel, Tim Chao

Database and software developmentChee-Hong Wong, Qing Zhang

Data analysis and validation studies Ayumu, Taguchi, Sharon Pitteri, Chris Baik, Sandra Faca, Ming Yu, Mark Schliekelman, Tina Buson, Melissa Johnson

Funding Support

National Cancer Institute- Early Detection Research Network- Glycomics Alliance- Cancer Centers of Nanotechnology Excellence- RO1 Mol. Epi. and lung Ca Case Control study R. Perera

National Heart Lung and Blood Institute

Canary, Labrecque, Avon, EIF, Paul Allen Foundations