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Blood Proteomics and Cancer Biomarkers
Sam Hanash
Potential Conflict of Interest
• Dr. Samir Hanash
– None
Blood based Signatures for
Lung cancer/epithelial tumors
DRUG EFFECT
Infiltrating Cells
Stroma
Cytokines
G.F.
TUMOR
TUMOR CELL
GENOME
MICROENVIRONMENT
mutations
Methylation changes
Amplification
Deletions/rearrangements
BLOOD
Nucleic 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
DRUG EFFECT
Infiltrating Cells
Stroma
Cytokines
G.F.
TUMOR
MICROENVIRONMENT
TUMOR CELL
GENOME
mutations
Methylation changes
Amplification
Deletions/rearrangements
BLOOD
Nucleic 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
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, UCLA
Data integration and modeling J. Zhu and S. Friend SAGE
Funding Support
• NIH
National Cancer Institute
National Heart Lung and Blood Institute
• Department of Defense Lung Cancer Research
Program
• Foundations
Canary Foundation
Labrecque Foundation
Protect 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
Chemical
Modifications eg
altered glycosylation
Protein Cleavages eg
shed receptors and
adhesion molecules
Alternative
Splicing
Isoforms
Altered dynamics of
protein sorting eg
release of
chaperone proteins
Formation of
complexes eg
immune complexes
Translational
Implications
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?
Proteomic Signatures for Lung Cancer
Blood collected 3-5 yrs
prior to lung Ca Dx
Protein signatures
of risk
Blood collected
at Dx
Molecular
Classification
Early detection
Signatures
Blood collected
0-18 months
prior to Dx
Proteomic Signatures for Lung Cancer
Blood collected 3-5 yrs
prior to lung Ca Dx
Protein signatures
of risk
Blood collected
at Dx
Molecular
Classification
Early detection
Signatures
Blood collected
6-18 months
prior to Dx
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
Albumin
Immunoglobulins
mM –3
Major
Serum
Proteins
Transferrin
µM –6
Alkaline Phosphatase
nM –9
10
Disease
Tissue
Markers
• NSE
12
• PSA
pM -12
TNF
fM -15
aM -18
10
100
1’000
10’000
Signaling
Proteins
Controls
Cases
Immunodepletion
(top X proteins)
Concentration, buffer exchange and
labeling
SAMPLE A
SAMPLE B
Isotopic labeling
Isotopic labeling
SAMPLES MIXED
ANION EXCHANGE
CHROMATOGRAPHY
REVERSE-PHASE
CHROMATOGRAPHY
Shotgun LC/MS/MS
Of individual fractions
EGFR
2.26
Plasma Profiling Strategies
• Cases vs matched controls
• Before and after tumor resection
• Arterial vs venous comparison
Overview of Project
Tumor
pulmonary venous effluent
systemic radial arterial blood
Pool samples
Alkylation with HEAVY acrylamide
Alkylation with LIGHT acrylamide
Fractionation
LC-MS/MS
To identify differentially existing proteins in blood draining lung tumor
0.6
0.4
0.2
Area under the curve: 0.839
95% confidence interval (0.765, 0.913)
J Clin Oncol 2009; 27:2787-92
0.0
Sensitivity
0.8
1.0
CXCL7
0.0
0.2
0.4
0.6
1-Specificity
0.8
1.0
Figure 5
Newly
Dx
EDRN set
B
0.2
0.4
0.6
0.8
0.8
0.6
0.4
AUC = 0.839
0.0
True Positive Fraction
AUC = 0.866
0.0
0.2
1.0
0.8
0.6
0.4
0.2
0.0
True Positive Fraction
Pre-Dx
CARET set
1.0
A
0.0
1.0
0-60-6mmonth
fore Dx
0.6
0.8
1.0
0.2
0.4
0.6
0.8
False Positive Fraction
1.0
0.2
0.4
0.6
0.8
1.0
0.0
AUC = 0.888
0.0
True Positive Fraction
0.8
0.6
0.4
0.2
AUC = 0.893
0.0
True Positive Fraction
0.4
7-11m7-11before
Dx
month
D
1.0
C
0.2
False Positive Fraction
False Positive Fraction
0.0
0.2
0.4
0.6
0.8
1.0
False Positive Fraction
A.Taguchi, K. Politi et a
Mouse models of cancer
Human vs animal models
• 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)
Mouse Models Studied to Date
• 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),
•
•
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 MODEL
NETWORK #1
Cellular Assembly and Organization, Cancer, Cellular Movement
EGFR MOUSE MODEL
NETWORK #2
Hematological System Development and Function, Organismal Development, Cancer
KRAS MOUSE MODEL
KRAS MOUSE MODEL
NETWORK #2
Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry
C. Kemp
K. Spratt
S. Pitteri
Rapid induction of mammary tumors following doxycycline treatment in an ERBB2
model of breast cancer (100% between 6-12 weeks)
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
Human Plasma Proteins
6607
6138
7000
5505
6000
5000
4000
3000
2000
1000
0
1% error
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
colon_IP0036_AX06_SG39to40
colon_IP0037_AX02_SG33to34
colon_IP0037_AX04_SG25to26
colon_IP0037_AX05_SG31to32
colon_IP0037_AX05_SG39to40
colon_IP0037_AX07_SG41to42
colon_IP0037_AX08_SG31to32
colon_IP0038_AX03_SG01to25
colon_IP0038_AX06_SG40to41
colon_IP0039_AX04_SG39to40
colon_IP0039_AX06_SG39to40
colon_IP0039_AX08_SG25to26
colon_IP0041_AX02_SG43to72
colon_IP0041_AX04_SG37to38
colon_IP0041_AX06_SG39to40
colon_IP0042_AX01_SG01to24
colon_IP0042_AX01_SG39to40
colon_IP0042_AX01_SG41to42
colon_IP0042_AX01_SG43to72
colon_IP0042_AX02_SG39to40
colon_IP0042_AX03_SG01to24
colon_IP0042_AX03_SG41to42
colon_IP0042_AX04_SG41to42
colon_IP0042_AX04_SG43to72
colon_IP0042_AX05_SG39to40
colon_IP0042_AX05_SG41to42
colon_IP0042_AX06_SG41to42
colon_IP0042_AX07_SG41to42
colon_IP0042_AX08_SG33to34
colon_IP0043_AX01_SG41to42
colon_IP0043_AX02_SG41to42
colon_IP0043_AX05_SG39to40
colon_IP0043_AX05_SG41to42
colon_IP0043_AX06_SG39to40
hormone_IP0019_AX03_SG58to72_conc
hormone_IP0021_AX02_SG48to57
hormone_IP0021_AX07_SG48to57
hormone_IP0021_AX08_SG48to57
hormone_IP0021_AX09_SG48to57
hormone_IP0021_AX11_SG48to57
hormone_IP0023_AX04_SG50to55
hormone_IP0028_AX04_SG47to52
lung_IP0022_AX01_SG50to53
lung_IP0022_AX02_SG50to53
lung_IP0022_AX04_SG50to53
lung_IP0022_AX05_SG50to53
lung_IP0022_AX06_SG50to53
lung_IP0022_AX07_SG50to53
lung_IP0022_AX08_SG50to53
lung_IP0022_AX12_SG50to53
lung_IP0024_AX04_SG50to53
lung_IP0024_AX12_SG50to53
++++++++
432_451
452_454
455_467
468_478
479_479
480_487
488_489
490_494
495_500
501_521
SLK
EISDGDVIISGNK
NLCYANTINWK
K
LFGTSGQK
TK
IISNR
GENSCK
ATGQVCHALCSPEGCWGPEPR
430_431
QHGQFSLAVVSLNITSLGLR
400_414
428_429
397_399
EITGFLLIQAWPENR
TK
378_396
TVK
415_427
361_377
GDSFTHTPPLDPQELDILK
GR
358_360
NCTSISGDLHILPVAFR
TDLHAFENLEIIR
347_357
HFK
335_335
336_346
329_334
K
DSLSINATNIK
328_328
CEGPCR
VCNGIGIGEFK
326_327
295_297
K
294_294
CPR
325_325
285_293
K
CK
262_284
YSFGATCVK
310_324
256_261
DTCPPLMLYNPTTYQMDVNPEGK
K
254_255
DEATCK
298_309
253_253
FR
ACGADSYEMEEDGVR
245_252
K
NYVVTDHGSCVR
227_244
ESDCLVCR
225_226
210_212
SPSDCCHNQCAAGCTGPR
190_209
LTK
223_224
166_189
CDPSCPNGSCWGAGEENCQK
GK
150_165
DIVSSDFLSNMSMDFQNHLGSCQK
213_222
139_149
FSNNPALCNVESIQWR
CR
134_138
NLQEILHGAVR
IICAQQCSGR
130_133
ELPMR
73_80
109_129
54_72
NYDLSFLK
TGLK
38_53
MFNNCEVVLGNLEITYVQR
99_108
30_37
LTQLGTFEDHFLSLQR
GNMYYENSYALAVLSNYDANK
29_29
VCQGTSNK
81_98
24_28
K
IPLENLQIIR
3_23
ALEEK
breast_IP0019_AX02_SG56to61_Run2
breast_IP0019_AX10_SG48to51
breast_IP0026_AX01_SG49to52
breast_IP0026_AX01_SG53to56_Run2
breast_IP0026_AX02_SG53to56_Run2
breast_IP0026_AX05_SG46to48_Run2
breast_IP0026_AX06_SG49to52
breast_IP0026_AX08_SG49to52_Run2
breast_IP0026_AX10_SG49to52
breast_IP0026_AX10_SG49to52_Run2
breast_IP0026_AX11_SG49to52
breast_IP0026_AX11_SG49to52_Run2
breast_IP0026_AX11_SG62to72_Run2
breast_IP0026_AX12_SG26to30
breast_IP0026_AX12_SG49to52
breast_IP0026_AX12_SG49to52_Run2
TIQEVAGYVLIALNTVER
1_2
PSGTAGAALLALLAALCPASR
Pept_Sequences
EXTRACELLULAR
MR
EGFR
XXX
XXX
0.83
0.82
0.83
0.94
0.94
1.1
0.92
0.73
XXX
0.47
0.87
XXX
1.06
XXX
XXX
XXX
1.02
XXX
XXX
XXX XXX
0.86
0.92
XXX
XXX
XXX
0.34
0.88
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
Selected 5 raw data for glycosylation investigation
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX XXX
XXX
XXX
XXX
XXX
0.83
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
2.26
EGFR
2.26
AX08
AX07
AX06
AX05
AX04
AX03
AX02
AX01
RP_SG39to40
RP_SG41to42
2nd D
Asn 444
(K) QHGQFSLAVVGLNITSLGLR (S)
1st D
Acknowledgements
Genomic Studies
Deep genomic sequencing
Q. Zhou
X. Yang, H. Xiao
Tianjin Lung Cancer Inst.
Shanghai Genome Center
DNA methylation
Adi Gazdar
Ite Laird
UT Southwestern
USC
DNA mutation detection in blood
P. Mack, D. Gandara
UC Davis
Gene copy changes
S. Lam, W. Lam
BCCA
Transcriptomic Studies
RNA profiling
D. Beer, J. Taylor,
U of Michigan
K. Shedden, R. Kuick
D. Misek, T. Giordano
A. Gazdar
UT Southwestern
MicroRNA
M. Tewari
FHCRC
Metabolomic Studies
Glycan analysis
S. Myamoto
C. Lebrilla
U C Davis
VOCs, Primary and secondary metabolites,
Lipid profiles
O. Fiehn
UC Davis
TK inhibitor Studies
FHCRC
K. Eaton, R. Martins,
S. Wallace, M. McIntosh
USC
D. Agus, P. Mallick, K. Kani
UCLA
A. Jain
Cohort Studies
Women’s Health Initiative
R. Prentice, C. Li
FHCRC
CARET
G. Goodman
M. Thornquist
M. Barnett
C. Edelstein
FHCRC
Physicians’ Health Study
R. Perera
A. Schneider
Columbia U.
New York CT Screening Cohort
W. 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 Feng
Mark Thornquist
Matt Barnett
Ross Prentice
Martin McIntosh
Charles Kooperberg
Lynn Amon
Pei Wang
Lin Chen
Aaron 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 development
Chee-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