Blood Proteomics and Cancer Biomarkers

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Blood Proteomics and Cancer Biomarkers Sam Hanash
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Potential Conflict of Interest

• Dr. Samir Hanash
• None

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Risk assessment Early detection
Molecular classification
to guide treatment
Disease monitoring
Blood based Signatures for Lung
cancer/epithelial tumors
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Host factors
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

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Host factors
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 BIOLOGY

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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

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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

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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

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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

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Cohorts for Lung Cancer Studies

• Carotene and Retinol Trial (CARET) Cohort
• NYU and BCCA lung cancer screening Cohorts
• Womens Health Initiative Cohort
• Physicians Health Study Cohort
• Asian Cohort Consortium

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

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Which is cancer?
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Proteomic Signatures for Lung Cancer
Blood collected 3-5 yrs prior to lung Ca Dx
Protein signatures of risk
Blood collected at Dx
Blood collected 0-18 months prior to Dx
Molecular Classification
Early detection Signatures

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Proteomic Signatures for Lung Cancer
Blood collected 3-5 yrs prior to lung Ca Dx
Protein signatures of risk
Mouse Models and Cell lines
Blood collected at Dx
Blood collected 6-18 months prior to Dx
Molecular Classification
Early detection Signatures

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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

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The plasma proteome
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Controls
Cases
Immunodepletion (top X proteins)
Concentration, buffer exchange and labeling
SAMPLE A Isotopic labeling
SAMPLE B Isotopic labeling
SAMPLES MIXED
ANION EXCHANGE CHROMATOGRAPHY
REVERSE-PHASE CHROMATOGRAPHY
Shotgun LC/MS/MS Of individual fractions
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EGFR
2.26
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Plasma Profiling Strategies

• Cases vs matched controls
• Before and after tumor resection
• Arterial vs venous comparison

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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
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CXCL7
1.0
0.8
0.6
Sensitivity
0.4
Area under the curve 0.839 95 confidence
interval (0.765, 0.913) J Clin Oncol 2009
272787-92
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity
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Figure 5
Newly Dx
Pre-Dx
0-6 m fore Dx
7-11m before Dx
A.Taguchi, K. Politi et al.
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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)

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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),

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• Proteomic profiles from similar cancer types
  cluster together Lung, breast, pancreatic
• Models with confounding conditions cluster
  together

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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 120(11)1496-510)
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EGFR MOUSE MODEL
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EGFR MOUSE MODEL
NETWORK 1 Cellular Assembly and Organization,
Cancer, Cellular Movement
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EGFR MOUSE MODEL
NETWORK 2 Hematological System Development and
Function, Organismal Development, Cancer
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KRAS MOUSE MODEL
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KRAS MOUSE MODEL
NETWORK 2 Lipid Metabolism, Molecular Transport,
Small Molecule Biochemistry
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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)
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Rapid regression of mammary tumors following
doxycycline withdrawl Additional controls
Models of inflammation and angiogenesis
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Chodosh Preclinical
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Chodosh 0.5 cm
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Chodosh 1.0 cm
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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

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Further advances in Proteomic technology

• Increased depth/breadth of analysis
• PTMs Cleavages, Glycosylation
• Genomic analysis of proteomic data
• Alternative splicing
• SNPs

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Selected 5 raw data for glycosylation
investigation
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EGFR
2.26
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Asn 444 (K) QHGQFSLAVVGLNITSLGLR (S)
2nd D
RP_SG41to42
RP_SG39to40
1st D
AX01
AX02
AX08
AX03
AX04
AX05
AX06
AX07
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Acknowledgements
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Genomic Studies

• Deep genomic sequencing
• Q. 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 blood
• P. Mack, D. Gandara UC Davis
• Gene copy changes
• S. Lam, W. Lam BCCA

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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

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Metabolomic Studies

• Glycan analysis
• S. Myamoto U C Davis
• C. Lebrilla
• VOCs, Primary and secondary metabolites,
• Lipid profiles
• O. Fiehn UC Davis

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TK inhibitor Studies

• FHCRC
• K. Eaton, R. Martins,
• S. Wallace, M. McIntosh
• USC
• D. Agus, P. Mallick, K. Kani
• UCLA
• A. Jain

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Cohort Studies

• Womens 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

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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

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FHCRC Statistical Analysis

• Ziding Feng
• Mark Thornquist
• Matt Barnett
• Ross Prentice
• Martin McIntosh
• Charles Kooperberg
• Lynn Amon
• Pei Wang
• Lin Chen
• Aaron Aragaki

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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

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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