blood proteomics and cancer biomarkers sam hanash
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Blood Proteomics and Cancer BiomarkersSam Hanash
Potential Conflict of Interest
• Dr. Samir Hanash– None
Blood based Signatures for Lung cancer/epithelial tumors
Risk
asse
ssm
ent
E
arly
det
ectio
n
Mol
ecul
ar c
lass
ifica
tion
to
g
uide
trea
tmen
t
Dis
ease
mon
itorin
g
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
n
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
ctio
n
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
e P
osi
tive
Fra
ctio
n
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
e P
osi
tive
Fra
ctio
n
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
0
1000
2000
3000
4000
5000
6000
7000
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_
129
130_
133
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138
139_
149
150_
165
166_
189
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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
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
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