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Blood Proteomics and Cancer Biomarkers Sam Hanash

Blood Proteomics and Cancer Biomarkers Sam Hanash. Potential Conflict of Interest. Dr. Samir Hanash None. Risk assessment Early detection Molecular classification to guide treatment Disease monitoring. Blood based Signatures for Lung cancer/epithelial tumors. mutations

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Blood Proteomics and Cancer Biomarkers Sam Hanash

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  1. Blood Proteomics and Cancer Biomarkers Sam Hanash

  2. Potential Conflict of Interest • Dr. Samir Hanash • None

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

  4. mutations Methylation changes Amplification Deletions/rearrangements InfiltratingCells Stroma Cytokines G.F. TUMOR MICROENVIRONMENT TUMOR CELL GENOME 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

  5. mutations Methylation changes Amplification Deletions/rearrangements InfiltratingCells Stroma Cytokines G.F. TUMOR MICROENVIRONMENT TUMOR CELL GENOME 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

  6. 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

  7. Lung Cancer Molecular Diagnostics Collaborative Group Nucleic acids: - Mutated DNAP. Mack UC Davis - Methylated DNAI. Laird, USC, A. Gazdar UT Southwestern - Tumor MicroRNAM. Tewari, FHCRC Altered protein and metabolic profiles - Proteomics S. Hanash FHCRC, S. Lam BCCA - Metabolomics O. Fiehn UC Davis Immune response signatures - Cytokines/ChemokinesS. Dubinett, UCLA - AutoantibodiesS. Hanash, FHCRC Circulating tumor cells S. Dubinett, UCLA Data integration and modeling J. Zhu and S. Friend SAGE

  8. 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

  9. 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

  10. 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

  11. Chemical Modifications eg altered glycosylation Alternative Splicing Isoforms Protein Cleavages eg shed receptors and adhesion molecules Altered dynamics of protein sorting eg release of chaperone proteins Formation of complexes eg immune complexes Translational Implications Proteomic signatures

  12. 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

  13. Which is cancer?

  14. 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

  15. 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

  16. 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

  17. The plasma proteome

  18. 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

  19. EGFR 2.26

  20. Plasma Profiling Strategies • Cases vs matched controls • Before and after tumor resection • Arterial vs venous comparison

  21. 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

  22. 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; 27:2787-92 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1-Specificity

  23. A B AUC = 0.866 AUC = 0.839 C D AUC = 0.893 AUC = 0.888 Figure 5 Newly Dx Pre-Dx 0-6 m fore Dx 7-11m before Dx A.Taguchi, K. Politi et al.

  24. 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)

  25. 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),

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

  27. 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)

  28. EGFR MOUSE MODEL

  29. EGFR MOUSE MODEL NETWORK #1 Cellular Assembly and Organization, Cancer, Cellular Movement

  30. EGFR MOUSE MODEL NETWORK #2 Hematological System Development and Function, Organismal Development, Cancer

  31. KRAS MOUSE MODEL

  32. KRAS MOUSE MODEL NETWORK #2 Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry

  33. 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)

  34. Rapid regression of mammary tumors following doxycycline withdrawl Additional controls: Models of inflammation and angiogenesis

  35. Chodosh Preclinical

  36. Chodosh 0.5 cm

  37. Chodosh 1.0 cm

  38. 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

  39. Further advances in Proteomic technology • Increased depth/breadth of analysis • PTMs: Cleavages, Glycosylation • Genomic analysis of proteomic data • Alternative splicing • SNPs

  40. Selected 5 raw data for glycosylation investigation

  41. EGFR 2.26

  42. Asn 444 (K) QHGQFSLAVVGLNITSLGLR (S) 2nd D RP_SG41to42 RP_SG39to40 1st D AX01 AX02 AX03 AX04 AX05 AX06 AX07 AX08

  43. Acknowledgements

  44. 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

  45. 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

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

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

  48. 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

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