Biology/ Disease Human Proteome Project

(B/D HPP) Workshop, Dublin 17 sept. 2017

Update Cancer-HPP

Prof.dr. Connie Jimenez c.jimenez@vumc.nl

Prof.dr. Hui Zhang

Dr. Chris Kinsinger

Prof.dr. Ed Nice

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Outline

Goals Cancer-HPP

Cancer-HPP activities past year

Data independent MS for comprehensive tumor profiling

Next steps, discussion

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Goals Cancer-HPP

To decipher the human cancer proteome Characterize the proteome of different human tumor types

Define expression and signaling of cancer proteins by quantitative

analysis of proteins, protein-protein interactions & PTMs

Develop assays for cancer detection/ precision medicine

Coordinated effort by cancer proteome researchers around the

word

Network cancer proteome scientists

Cancer-HPP sessions/ workshops at HUPO

Sharing of data and best practices

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Cancer-HPP in the network of international

cancer proteomics initiatives

Clinical Proteomic

Tumor Analysis

Consortium (CPTAC)

Applied Proteogenomics

OrganizationaL Learning and

Outcomes (APOLLO) network

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Cancer-HPP

International Cancer

Proteogenome

Consortium (ICPC)

Cancer-HPP activities past year

Chart the cancer proteomics community

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Generate database of cancer proteomics researchers:

EU: 75

USA: 79

Asia/Australia: 85

Cancer-HPP activities past year

Stimulate coordinated action

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Cancer HPP viewpoint paper (submitted to Clin. Prot.)

Cancer CPTAC paper (Kinsinger and Rondland, submitted)

Cancer proteomics paper JHU & CPTAC

The Cancer Proteomic Landscape and the

HUPO Cancer Proteome Project Connie R. Jimenez1*, Hui Zhang2, Christopher R. Kinsinger3, Edouard C. Nice4

Quality Assessments of Long-term Quantitative Proteomic

Analysis of Tumor Tissues Zhou, Zhang, Hui et al.

Cancer-HPP activities past year

Stimulate coordinated action

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Inventory of published clinical cancer tissue datasets

Down-loaded and reprocessed all public cancer tissue

proteomics datasets

Initiated a queriable data portal in cBioPortal

Cancer HPP viewpoint paper (submitted to Clin. Prot.)

TumorType Yearof

Publicati

on

First

Author

Journal StudyAim Numberof

Samples

MSPlatform Quantificati

on

Numberof

Identifications

(T=Total,D=

Differential/MostPromisingMarkers Validation(WB=Westernblot,

IHC=Immunohistochemistry,

MRM=MultipleReactionMonitoring,

Data

DownloadID

(Proteome

Reference

LungCancer 2012 Kikuchi MolCellProteomics

Toperformin-depthtissueproteinprofiling

ofSCCandADCas

majorsubtypesofnonsmallcelllungcancerandnormal

lungtissues

40tumortissues22normaltissues

4pools:20ADC=adenocarcinoma

20SCC=squamous

carcinoma19normaltissues

20normaltissues

DDA:LTQOrbitrap(Thermo

Fisher)

DIA(MRM):TSQQuantumUltra

(ThermoFisher)

labelfreespectralcounting

[filter=atleast8spectralcounts/proteinforFDR2.3%]

T=3621incl6'contaminants'and42reversehits(ADC:3513,SCC:3558,normal:2968;

2863inallpools)

D=653tumorspecific(598inboth

topuniqueinADC(notinnormal):CALCA,CPS1,CHGB,IVL,AGR2,NASP,PFKP,THBS2,TXNDC17,PCSK1,CRABP2,

ACBD3,DSG2,LRBA,STRAP,VGF,NOP2,LCN2,CKMT1B,

AKR1B10,PCNA,CPD,PSME3,VIL1;also:CEA=CEACAM5:bold:notfoundinSCCtophigherinADC(notinnormal):AGER,C10orf116,ADD2,

PRX,LAMB3,SYNM,SPTA1,ANK1,HBE1,HBG1,CA1,TNXB,

MMRN2,HBA1,CAV1,HBB,COL6A6,C1orf198,CLIC2,SDPR,EHD2,APOA2,NDUFB7,PRKCDBP,LAMA3

topuniqueinSCC(thannormal):FKBP10,SERPINB5,RPL5,

MRM_up:AGR2,PCNA,DSG2,CRABP2,GCN1L1,DSP,RCN3,AKR1B10,PDIA4,CTSB,EIF5A,SERPINB5,TPD52,

down:AGER_originalsamples

MRM_44of50NormalvADC,42of50NormalvSCCverified)_20ADC,20SCC,23Normal

WB_AGR2,STRAP,PTGES3,AKR1B10_4ADC,5SCC,3Normal

IHC_PAK2_19SCC,14Normal;inADConlymoderately

http://www.vicc.org/jimayersinstitute/d

ata/

KikuchiT,HassaneinM,AmannJM,LiuQ,Slebos

RJC,RahmanSMJ,etal.In-

depthproteomicanalysisofnonsmallcelllungcancertodiscovermoleculartargets

andcandidatebiomarkers.

MolCellProteomics.2012;11:916–32.

doi:10.1074/mcp.M111.015LungCancer 2014 Ahn JProteome

Res

Tocatalog

chromosome9-

encodedmissing

proteinsandtoidentifychromosome9-

basedlungcancer-

specificproteinsand

mutations

5tumortissues

5adjacent

normallungtissues

(pooled)

LTQOrbitrap(Thermo

Fisher)

- T=1945(cancer),1472(control)

46previouslymissingproteinsfrom

Chr9

15cancer-specificChr9-encodedproteins

NDUFA8,RAD23B,RPS6,SET,PTGR1,FKBP15,ANP32B,

FUBP3,PLIN2,ARPC5L,HINT2,NANS,SH3GLB2,FAM120A,

DPP7

- PXD000603 AhnJ-M,KimM-S,KimY-I,

JeongS-K,LeeH-J,LeeSH,et

al.Proteogenomicanalysis

ofhumanchromosome9-encodedgenesfromhuman

samplesandlungcancer

tissues.JProteomeRes.

2014;13:137–46.doi:10.1021/pr400792p.

LungCancer 2015 Kim JProteome

Res

Torevealmissing

proteinsandundiscoveredfeatures

inproteogenomes

5tumortissues

5adjacentnormaltissues

5ADC=adenocarcinoma

LTQOrbitrap(Thermo

Fisher)

- T=1257 variantproteinsfromproteogenomicanalysis:

HDLBPS61Ain1tumortissuesampleLTFE535Din1tumortissuesample

HBDA23Ein1normaltissuesample

- PXD002523 KimY-I,LeeJ,ChoiY-J,SeoJ,

ParkJ,LeeS-Y,etal.ProteogenomicStudy

beyondChromosome9:

NewInsightintoExpressedVariantProteomeand

TranscriptomeinHuman

LungAdenocarcinomaLungCancer 2016 Hsu MolCell

Proteomics

Toidentifypotential

markersforthe

diagnosisofearly-stagelungcancer

withoutlymphnode

metastasis

14adenocarcinoma

tissue

14nonmalignantadjacenttissue

LTQOrbitrapDiscovery

(ThermoFisher)

iTRAQ T=1857(1763quantified)

D=133upinnonmalignantvstageIcancer(nomets)

ERO1L,PABPC4,NARS,RCC1,RPS25,TARS WB_MX1,ERO1L,SERPINH1_originalcases+12additional

cases

IHC_ERO1L,GARS,PABPC4,NARS,RCC1,RPS9,RPS25,

TARS_3cases

IHC_ERO1L,PABPC4,NARS,RCC1,RPS25,TARS_10casesWB_ERO1L,PABPC4,NARS,RCC1,RPS25,TARS_48cases

PXD004077

HsuC-H,HsuC-W,HsuehC,

WangC-L,WuY-C,WuC-C,

etal.IdentificationandCharacterizationofPotential

BiomarkersbyQuantitative

TissueProteomicsofPrimaryLung

Adenocarcinoma.MolCellBreastCancer 2012 Yang JProteome

Res

Todiscoverpotential

predictionmarkersof

drugresistanceinneedle-biopsiedtissuesofbreast

cancerpatientsprior

toneoadjuvant

11tumortissues

6drugresistant5drugsensitive

drug=doxorubicinor

docetaxel

LTQ

(ThermoFisher)

labelfree

spectralcounting

T=2331(1608drugsensitive,1948

drugresistant,1225both)

D=298indrugsensitivevdrugresistant

Mann-Whitney:88(72up/16down)

FKBP4andS100A9 WB_RUVBL2,HSPA4,S100A9,FABP4,FKBP4,ASAH1,

POSTN_14samples

IHC_FKBP4_38(23sens,15resist)independentsamples

IHC_S100A9_30(19sens,11resist)independentsamples

- YangWS,MoonH-G,Kim

HS,ChoiE-J,YuM-H,NohD-

Y,etal.ProteomicapproachrevealsFKBP4andS100A9aspotentialprediction

markersoftherapeutic

responsetoneoadjuvantchemotherapyinpatients

BreastCancer 2014 Groessl JProteome

Res

Toassessthe

functionalstateof

cancer-associatedfibroblaststhrough

proteomeprofilingofbreastcancertissueandcancer

surroundingtissue

3tumortissuebiopsies

→

9biopsysegments

3tumor-centraltissue3tumor-neartissue3tumor-distanttissue

QExactive

(ThermoFisher)

labelfreeintensity

based

(LFQ)

T=2073

Top24proteinsintumor-distantvtumor-central

fromclusteranalysis

higherintumor-central-FKBP4,AGR3,AGR2,TPD52,

CRABP1,NCL,ATP1B1,ACP1,IFITM1,RPS16,FKBP10,

CRABP2

higherintumor-distant-LIPE,SOD3,HSPA12A,MAOA,ALDH1L1,GPD1,AKR1C2,MGST1,CACNA2D1,PLIN4,MGLL,CD151

- PXD001311

PXD001324-8

GroesslM,SlanyA,BileckA,

GloessmannK,KreutzD,

JaegerW,etal.Proteomeprofilingofbreastcancer

biopsiesrevealsawoundhealingsignatureofcancer-associatedfibroblasts.J

ProteomeRes.

2014;13:4773–82.doi:10.1021/pr500727h.

BreastCancer 2014 LiuNQ JNatlCancerInst

Toidentifyandvalidateaprognosticproteinsignatureto

predict5-year

metastasis-freesurvivalfortriple-

negativebreastcancertoreduceunnecessary

63tumortissues

38nometastasis(5yr)

25metastasis(5yr)

LTQOrbitrapXL(ThermoFisher)

labelfree T=3660/4385(testset/trainingset)

981stringentlyfilteredproteinsfrom

trainingset:

D=23innometastasis(5yr)v

metastasis(5yr)(Coxregressionanalysis)

11-proteinsignature:higherinnometastasis(5yr)-CMPK1,AIFM1,FTH1,EML4,GANAB,CTNNA1,AP1G1,STX12,AP1M1,CAPZB

higherinmetastasis(5yr)-MTHFD1

globalprofiling,Coxregression_11-proteinsignature_additional64cases(testset)

PXD000260 LiuNQ,StinglC,LookMP,SmidM,BraakmanRBH,DeMarchiT,etal.Comparative

proteomeanalysisrevealing

an11-proteinsignatureforaggressivetriple-negative

breastcancer.JNatlCancerInst.2014;106:djt376.

Cancer-HPP activities past year

PubMed Clinical Cancer Proteomics

Jimenez et al., Submitted to Clinical Proteomics; Acknowledgements members OncoProteomics Lab

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Cancer-HPP

activities past year

Literature study

Summary Clinical

Cancer Proteomics

Survey for the type of cancer,

the number of specimens, the

methods, and references

Jimenez et al., Submitted to

Clinical Proteomics

Small sample size

Variable depth of

analysis

~1/3 in public

domain

8694

(81%)

9216

proteins

10179

proteins

1485 522

R-squared= 0.8 10

8

6

4

2

0

Log2 m

ean A

MS

CR

C t

issue

0 2 4 6 8

10

Log2 mean CPTAC CRC tissue

High overlap and correlated quantification of label-free

shotgun proteomic data sets from 2 different laboratories

CRC-AMS CRC-CPTAC

Amsterdam (AMS):

40 tumors

5-band GeLC-MS/MS

on QExactive

CPTAC:

95 tumors

12-fr. 2D-LC-MS/MS on

LTQ-Orbitrap

Meta-analysis high-res data feasible, despite different workflows

Numberofsamples

Breast Ovary Bowel Kidney Lung

Pancreas Head&Neck Blood Bladder Skin

Brain Liver Eye

• 13 tumor types

• 32 studies

• 1623 tissue samples

• 6 terabyte raw data

• 150 million MS/MS spectra

• 32000 protein isoforms

• >14000 unique genes

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Cancer-HPP activities past year

Collect and reprocess all public clinical

cancer proteome datasets

Breast 543

Ovary 326

Bowel 187

Kidney 177

Lung

80

Pancreas 6

6

53 49

44 42

28 24 4

Team up with cBioPortal (JJ Gao,

MSKCC) to make data queriable

for the cancer community T. Pham (VUmc)

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Cancer-HPP activities past year

Work towards queriable data platform

human cancer proteomes

T. Pham (VUmc) in collaboration with JJ Gao, MSKCC

Intermezzo

Data-independent MS for

comprehensive tumor proteome profiling

• 24 variable MS2 windows

• 20 x 20, 2 x 40, 2 x 60

• 400-1000 m/z

• ~2 sec/cycle

• 1.5 m/z isolation

• Top15 precursors

• 350-1500 m/z, Top10 MS2

• ~1 sec/cycle DDA

DIA

MaxQuant software

Filter peptide and protein IDs to

1% FDR

Spectronaut software

Filter peptide and protein IDs to

Q-value of 1%

CMS1

CMS2

CMS3

CMS4

Colorectal

cancer

tissues

from the four

CMS

subtypes,

pooled (10

patients per

group

Comparison DDA vs DIA using Colorectal

Cancer (CRC) tumor lysates

Poster Davide

Chiasserini

3230 (88%)

280 (7.6%)

161 (4.4%)

DDA DIA

Protein ID Proteome depth

2

3

4

5

6

7

8

9

0 1000 2000 3000 4000 5

6

7

8

9

10

11

12

13

0 1,000 2,000 3,000 4,000

DIA DDA

r = 0.82

DDA

DIA

DIA DDA

Correlation

Comparable depth of DIA and DDA with small library (based on triplo DDA)

NB depth of a 2 hr DIA run may be increased to 5000-6000 with large CRC library

DIA

D

DA

2 hr

single

shot

analysis

Proteins DDA DIA

Total proteins groups 3593 3582

CV

Poster Davide Chiasserini

DIA DDA Median CV

= 15.8 Median CV

= 5.1

Comparison DDA vs DIA using Colorectal

Cancer (CRC) tumor lysates

Intensity Intensity 19% missing

data / sample

2% missing

data

/sample

DDA DIA

Superior performance DIA over DDA

16

CM

S1

CM

S2

CM

S3

CM

S4

0 .0

0 .5

1 .0

1 .5

2 .0

2 .5Q G V D D A F Y T L V R

Co

un

ts

CM

S1

CM

S2

CM

S3

CM

S4

0

2 .01 0 6

4 .01 0 6

6 .01 0 6

8 .01 0 6

1 .01 0 7Q G V D D A F Y T L V R

Inte

nsi

ty

CM

S1

CM

S2

CM

S3

CM

S4

6 .01 0 5

8 .01 0 5

1 .01 0 6

1 .21 0 6

1 .41 0 6

Q G V D D A F Y T L V R

No

rma

lize

d a

rea

DDA spectral counts DDA intensity DIA intensity

Superior quantification DIA over DDA

Comparison DDA vs DIA using Colorectal

Cancer (CRC) tumor lysates

KRAS peptide displaying CRC suptype-specific quantification

Conclusions DIA-MS for tumor proteome profiling

17

• DIA workflow is robust and reproducible

• Depth comparable to a DDA discovery experiment

• Low number of missing values (2% vs 19%)

superior reproducibility and quantitation

• Highthroughput (2 hr machine time)

• Digital tumor archive (rediscovery possible using

different spectral library)

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Continue collecting and reprocessing all public high resolution

cancer proteome expression data in a web-based, queriable

database

Link to existing cBioPortal repository of cancer genome data

Next steps Cancer-HPP 2017-2018

Next steps: Tentative time-line clinical

cancer proteome data in cBioPortal

SEPT

2017

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MRCH

2018

SEPT

2018

SEPT

2019

SEPT

2020

All public cancer

tissue proteome

data reprocessed

& in cBioPortal

Per dataset

protein query-

boxplots

Adapt database

schema for mass

spectrometry

proteomics

DDA, DIA

data in

portal

Cross

dataset

queries

Demo,

training

Dedicated

Statistics &

Visualization*

*Collaborate with Bing Zhang (CPTAC), others

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Next steps Cancer-HPP 2017-2018

To call on cancer proteome researchers

to work together to generate a

comprehensive proteome atlas of tumor

types

Call to deposit all (published) data into

ProteomeXchange

Mailing to researchers in our

database to explore whether and when

we can expect data contributions and

harmonize analysis

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Create homogeneous high resolution MS-based proteomics data for

a large range of tumor types*

Kick-start of The Cancer Proteome Atlas (TCPA) project: DIA-MS

profiling of 18 tumor types (n=20-40 per tumor types, some with

larger n >100)

DIA-MS data preferred for inter-laboratory data integration and

meta-analysis

Engage labs world-wide

Next steps Cancer-HPP 2017-2018

*Applied for local funding to the Cancer Center Amsterdam

Joe Biden: “The urgency of now” Let’s work together, decipher

the cancer proteome and make data available!

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Cancer-

HPP

Global Leadership Gala Dinner, 16 sept. 2017, Dublin