Refining Prognosis of Early Stage Lung Cancer by Molecular Features (Part 2): Early Steps in Molecularly Defined Prognosis

5/17/13 Refining Prognosis of Early Stage Lung Cancer by Molecular Features (Part 2): Early Steps in Molecularly Defined Prognosis Johannes Kratz, MD Post-doctoral Fellow, Thoracic Oncology Laboratory University of California, San Francisco Resident, Department of Surgery Massachusetts General Hospital Molecular pathology of lung cancer Predictive Biomarkers L Cheng et al or the rapid detection of EGFR gh sensitivity and specificity. tion of mutations via direct necessary.27,76,77 Though not of l use, an assay that provides a EGFR mutation status in as little mart amplification process has ese may one day provide greatly nd times for this analysis.78 d paraffin-embedded tissue is r fluorescence in situ hybridizana-based tests, but tissue prefor a successful test. Decalcified ssue, as well as tissues containfigure 3 Frequency of major driver mutations in signaling molecules in lung adenocarcinomas. About 64% of all adenocarsis, should be avoided. cinoma cases harbor somatic driver mutations. According to the ect multiple driver mutations in Source: ChengNational et al. Modern Pathology 2012 Cancer Institute Lung Cancer Mutation Consortium ma has revolutionized the medidata,79 B23% of lung adenocarcinomas harbor EGFR mutations. this disease and multiplexed The EGFR mutation status of the cancer is associated with its on driver mutations will provide responsiveness or resistance to EGFR TKI therapy. KRAS mutations are more frequently found in adenocarcinomas (25%), more precise guide for therapy.9 9 which are mutually exclusive with EGFR mutations. Mutations identified 10 driver mutations in KRAS have been proposed as one of the mechanisms of rom 1000 lung adenocarcinoma primary resistance to gefitinib and erlotinib therapy. A subset n the National Cancer Institute of adenocarcinoma cases harbors a transforming fusion gene, EML4 ALK (6%), which mainly involves adenocarcinoma from on Consortium. The mutations, non-smokers with wild-type EGFR and KRAS mutations. The EGFR, ERBB2 (HER2), BRAF, 1

Predictive Biomarkers egfr 2

egfr egfr 3

egfr X X Tyrosine Kinase Inhibitors Erlotinib (Tarceva) Gefitinib (Iressa) Proposed Integration of Predictive Biomarkers 4

Proposed Integration of Predictive Biomarkers ERCC1 resistance to platinum-based therapy RRM1 resistance to gemcitabine BATTLE Study Positive Impact on Outcomes +egfr erlotinib +cyclin D erlotinib / bexarotene +egfr FISH amp - erlotinib / bexarotene +VEGFR-2 - vandetanib Core biopsy specimens screened for 11 biomarkers Source: Langer. Critical Reviews in Hematology / Oncology 2012 5

Approaches to Novel Biomarkers Many clinical and chemical factors correlate with outcome Clinical (eg. sex, symptoms, age, weight loss) Chemical (eg. hypercalcemia, Hb, LDH, albumin) Are molecular biomarkers helpful? Proteomic Oncoproteins Protein products Genomic DNA (copy number, mutations, SNPs, etc.) Predictive RNA (gene expression) Prognostic Epigenetic modifications (acetylation, methylation, etc.) Prognostic Genomic Biomarkers Oncogenic DNA mrna expression Epigenetic Modification Microarrays qpcr Acetylation, Methylation, mirna Expression Assays 6

Microarrays Developed in 1995 Genome-wide snapshot of mrna expression In 2001, applied to lung tumors ID of primary tumors vs. metastases ID novel subclasses of tumors Prognostic profiling of early stage tumors Microarray-based Expression Profiles 50-400 snap frozen tumor samples (all stages) cdna microarray expression profiling Clustering, Cox Hazard Modeling, Metagenes, etc. Outcome-related 50-100 gene expression profile Prognostic power of signature (Kaplan- Meier, HR, etc.) Apply to validation cohort (stage I/II patients only) 7

5/17/13 Bhattacharjee et. al 2001 12,600 unique gene cdna microarrays 186 frozen tumors vs. 17 normal lung specimens 4 novel subclasses of adenocarcinomas identified C1 C2 C3 C4 proliferation genes neuroendocrine markers several genes inc. ODC1 and GSTP1 alveolar pneumocyte markers Stage I patients: Median tumors Median tumors survival worse in C2 (20 vs. 48 months) survival better in C4 (50 vs. 44 months) Source: Bhattacharjee et al. Proc Natl Acad Sci USA 2001 Study Boutros 2009 Roepman 2009 Shedden 2008 Sun 2008 Skrzypski 2008 Raz 2008 Chen 2007 Lau 2007 Larsen 2007 Larsen 2007 Guo 2006 Lu 2006 Raponi 2006 Tomida 2004 Beer 2002 Platform Training Validation Successful Cohort validation? Blinded? Tissue cohort 147 0 103 69 256 1 (stage I)* 175 66 26 107 0 101 146 147 216 51 58 48 95 84 197 120 129 36 50 6 84 8

Study Platform Tissue Training cohort Validation Cohort Successful validation? Blinded? Boutros 2009 qpcr Frozen 147 0 Yes No Roepman 2009 Microarray Frozen 103 69 Yes No Shedden 2008 Microarray Frozen 256 186 No (stage I)* Yes Sun 2008 Microarray Frozen 86 175 Yes No Skrzypski 2008 qpcr Frozen 66 26 Yes No Raz 2008 qpcr Frozen 107 0 Yes No Chen 2007 qpcr Frozen 101 146 Yes No Lau 2007 qpcr Frozen 147 216 Yes No Larsen 2007 Microarray Frozen 51 58 Yes No Larsen 2007 Microarray Frozen 48 95 Yes No Guo 2006 Microarray Frozen 86 84 Yes No Lu 2006 Microarray Frozen 197 120 Yes No Raponi 2006 Microarray Frozen 129 36 Yes No Tomida 2004 Microarray Frozen 50 6 Yes No Beer 2002 Microarray Frozen 86 84 Yes No Study Platform Tissue Training cohort Validation Cohort Successful validation? Blinded? Boutros 2009 qpcr Frozen 147 0 Yes No Roepman 2009 Microarray Frozen 103 69 Yes No Shedden 2008 Microarray Frozen 256 186 No (stage I)* Yes Sun 2008 Microarray Frozen 86 175 Yes No Skrzypski 2008 qpcr Frozen 66 26 Yes No Raz 2008 qpcr Frozen 107 0 Yes No Chen 2007 qpcr Frozen 101 146 Yes No Lau 2007 qpcr Frozen 147 216 Yes No Larsen 2007 Microarray Frozen 51 58 Yes No Larsen 2007 Microarray Frozen 48 95 Yes No Guo 2006 Microarray Frozen 86 84 Yes No Lu 2006 Microarray Frozen 197 120 Yes No Raponi 2006 Microarray Frozen 129 36 Yes No Tomida 2004 Microarray Frozen 50 6 Yes No Beer 2002 Microarray Frozen 86 84 Yes No 9

Study Platform Tissue Training cohort Validation Cohort Successful validation? Blinded? Boutros 2009 qpcr Frozen 147 0 Yes No Roepman 2009 Microarray Frozen 103 69 Yes No Shedden 2008 Microarray Frozen 256 186 No (stage I)* Yes Sun 2008 Microarray Frozen 86 175 Yes No Skrzypski 2008 qpcr Frozen 66 26 Yes No Raz 2008 qpcr Frozen 107 0 Yes No Chen 2007 qpcr Frozen 101 146 Yes No Lau 2007 qpcr Frozen 147 216 Yes No Larsen 2007 Microarray Frozen 51 58 Yes No Larsen 2007 Microarray Frozen 48 95 Yes No Guo 2006 Microarray Frozen 86 84 Yes No Lu 2006 Microarray Frozen 197 120 Yes No Raponi 2006 Microarray Frozen 129 36 Yes No Tomida 2004 Microarray Frozen 50 6 Yes No Beer 2002 Microarray Frozen 86 84 Yes No Study Platform Tissue Training cohort Validation Cohort Successful validation? Blinded? Boutros 2009 qpcr Frozen 147 0 Yes No Roepman 2009 Microarray Frozen 103 69 Yes No Shedden 2008 Microarray Frozen 256 186 No (stage I)* Yes Sun 2008 Microarray Frozen 86 175 Yes No Skrzypski 2008 qpcr Frozen 66 26 Yes No Raz 2008 qpcr Frozen 107 0 Yes No Chen 2007 qpcr Frozen 101 146 Yes No Lau 2007 qpcr Frozen 147 216 Yes No Larsen 2007 Microarray Frozen 51 58 Yes No Larsen 2007 Microarray Frozen 48 95 Yes No Guo 2006 Microarray Frozen 86 84 Yes No Lu 2006 Microarray Frozen 197 120 Yes No Raponi 2006 Microarray Frozen 129 36 Yes No Tomida 2004 Microarray Frozen 50 6 Yes No Beer 2002 Microarray Frozen 86 84 Yes No 10

5/17/13 Study Boutros 2009 Roepman 2009 Shedden 2008 Sun 2008 Skrzypski 2008 Raz 2008 Chen 2007 Lau 2007 Larsen 2007 Larsen 2007 Guo 2006 Lu 2006 Raponi 2006 Tomida 2004 Beer 2002 Platform Training Validation Successful Cohort validation? Blinded? Tissue cohort 147 0 103 69 256 1 (stage I)* 175 66 26 107 0 101 146 147 216 51 58 48 95 84 197 120 129 36 50 6 84 Practical, reproducible assay with large-scale, blinded validation To be continued in next podcast 11