Daniel Lieber, Ph.D. Senior Scientist, Computational Biology Foundation Medicine, Cambridge, MA. AACR 2017: Clinical Biomarkers April 3, 2017

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1 Validation & clinical feasibility of a comprehensive genomic profiling assay to identify likely immunotherapy responders through tumor mutational burden (TMB) Daniel Lieber, Ph.D. Senior Scientist, Computational Biology Foundation Medicine, Cambridge, MA AACR 2017: Clinical Biomarkers April 3, Foundation Medicine, Inc. 1

2 Disclosure Information Employee and stockholder of Foundation Medicine 2017 Foundation Medicine, Inc. 2

3 Immune checkpoint inhibitor therapies PD-1/PD-L1 inhibitors Pembrolizumab (Merck) Nivolumab (BMS) Atezolizumab (Roche) PD-L1 CTLA-4 inhibitors Ipilimumab (BMS) Tremelimumab (AZ) CG Drake, 2010, Nature Reviews Immunology 2017 Foundation Medicine, Inc. 3

4 Checkpoint inhibitors improve OS in several indications Hodi et al. NEJM, 2010 Metastatic Melanoma Advanced NSCLC Borghaei et al. NEJM, 2015 Advanced Urothelial Carcinoma Bellmunt et al. NEJM, 2017 OS Docetaxel 10-20% increase in long term survival Biomarkers to predict likely responders Improved response rate 2017 Foundation Medicine, Inc. 4

5 Tumor mutational burden (TMB) as predictor of response to checkpoint inhibitors Whole Exome Sequencing mutational load associated w/ response Snyder et al, NEJM, 2014; Rizvi et al, Science, 2015 A fraction of somatic mutations will give rise to neo-antigens Measuring the total number of somatic mutations (TMB) acts as a proxy for neo-antigen burden Increased TMB Increased neo-antigen load Time Normal cell Cancer cell G Increased TMB G R Y A T-cell activation G R Y A T-cell Rizvi et al, Science 2015 Red = immunogenic mutation Black = non-immunogenic mutation 2017 Foundation Medicine, Inc. 5

6 NSCLC: 315-gene TMB significantly associated with PFS Campesato et al. (2015) narrowed Rizvi data to several gene panels, incl. FoundationOne (F1) Determined F1-like TMB predictive for stratifying anti-pd-1 responders in NSCLC Smaller gene panels insufficient for survival stratification with p < Genes 160 Genes 203 Genes F1 315 Genes (1.1 Mb) Figures from Campesato et al, Oncotarget, 2015 Months CGP: Comprehensive Genomic Profiling FM: Foundation Medicine PFS: Progression-Free Survival 2017 Foundation Medicine, Inc. 6

7 Foundation Medicine s TMB method Targeted sequencing of 315 genes (1.1 Mb coding region) Count all coding indels & substitutions incl. synonymous Remove likely germline polymorphisms dbsnp/exac FM internal database, SGZ algorithm Remove known or likely functional status (to prevent ascertainment bias of sequencing known cancer genes) Calculate mutations per megabase (mut/mb) All variants Remove polymorphisms Remove predicted drivers Tumor Mutational Burden (TMB) 2017 Foundation Medicine, Inc. 7

8 Mutations/Mb Translating TMB into clinically actionable information TMB is reported as a metric (mut/mb) as well as status Status of High 20 mut/mb, Intermediate 6-19 mut/mb, Low <6 mut/mb Bins designed to associate with checkpoint inhibitor response using data from published sources FoundationOne Pipeline High 20 Checkpoint Inhibitor Response Prediction Intermediate 6-19 Low <6 New Analysis Adds to Report Melanoma RR=73% Melanoma RR=27% NSCLC RR=64% NSCLC RR=40% Melanoma RR=27% NSCLC RR=35% Disease indication: Urothelial Carncinoma TMB: TMB High 30 mutations/mb Approved therapies: Atezolizumab References: Rosenberg et al. Lancet, 2016 Patient rank 2017 Foundation Medicine, Inc. 8

9 Foundation Medicine s TMB is validated for clinical use TMB analytically validated in FoundationOne assay (CLIA & CAP) Demonstrates high accuracy, reproducibility and sensitivity Criteria Validated Results at >500x Performance specification Achieved Status Accuracy 90% Passed Precision 96% Passed Limit of Detection 20% tumor purity Passed 2017 Foundation Medicine, Inc. 9

10 F1 genes: Mutations/Mb F1 TMB (mut/mb) 315-gene TMB correlated with Whole Exome Seq (WES) Simulation: 7,000 TCGA WES samples narrowed to genes of FoundationOne (F1) Experiment: Sequenced 29 samples by F1 and WES; calculated mutations/mb High correlation between mutations per megabase in WES vs. F1 F1 vs. WES TMB Evaluation Status concordance: 90% R² = R 2 = High 10.0 Intermediate Low WES Mutations/Mb WES TMB (mut/mb) 2017 Foundation Medicine, Inc. 10

11 TMB (mut/mb) Precision of TMB using replicate samples Precision of TMB measured using 4-6 replicates from 10 clinical samples The mean CV for all 56 samples was 14.1% Precision of F1 TMB Among 10 Patients 100 Precision of status: 96% High 10 Intermediate Low Replicate group 2017 Foundation Medicine, Inc. 11

12 TMB TMB limit of detection (LoD) for tumor purity LoD defined as lower limit of tumor purity at which TMB can still be reliably called 3 clinical DNA samples were mixed in triplicate w/ matched normal at range from 5-40% tumor purity Error exceeding 30% from undiluted sample considered LoD Average TMB Values from 3 Replicates % 10% 20% 30% 40% 50% 60% 70% 80% 90%100% Tumor DNA purity 1541T 5964T 5371T TMB Error from Replicate LoD Samples Dilution TMB Error from Replicate LoD Samples 2017 Foundation Medicine, Inc. 12

13 Mutations / Mb Analysis of TMB across FMI clinical cohort A significant number of disease types, including those not currently approved for checkpoint inhibitors, have outliers with TMB exceeding 20 mut/mb Distribution of TMB Across Indications at FMI (N=88,157) TMB 20 Disease type ZR Chalmers et al, In Press 2017 Foundation Medicine, Inc. 13

14 FMI TMB predicts immunotherapy response in several indications Melanoma (N=65) NSCLC (N=454) Urothelial carcinoma (N=94) OS DB Johnson et al, Cancer Immunology Research, 2016 doi: / CIR M Kowanetz et al, Annals of Oncology, Vol 27, 2017, Suppl 6, doi: /annonc/mdw Balar AV et al, The Lancet, 2017 doi: /S (16) Foundation Medicine, Inc. 14

15 Summary TMB analytically validated in FoundationOne assay (CLIA & CAP) Sensitivity, specificity and reproducibility established Applicable down to 20% tumor purity TMB associated with response to checkpoint inhibitor immunotherapy Clinical evidence in NSCLC, melanoma and bladder cancer 2017 Foundation Medicine, Inc. 15

16 Acknowledgements Foundation Medicine David Fabrizio Mark Kennedy Steve Roels Joel Greenbowe Jared White Garrett Frampton Caitlin Connelly Zach Chalmers Alexa Schrock Jeffrey Ross Phil Stephens Vince Miller Siraj Ali Collaborators Douglas Johnson (Vanderbilt) Jonathan Rosenberg (MSKCC) Marcin Kowanetz (Genentech/Roche) Craig Cummings (Genentech/Roche) Patients and their families 2017 Foundation Medicine, Inc. 16

17 Thanks! Any questions? 2017 Foundation Medicine, Inc. 17

18 MSI-High specimens are a subset of high TMB specimens (n=46,465) The majority of MSI-H specimens (~84%) are TMB-H, but not the reverse Only 14.5% of TMB-H specimens are also MSI-H All specimens n = 46,465 TMB-High n = 3,531 MSI and TMB High n = 550 Microsatellite stable Microsatellite ambiguous Microsatellite instable ZR Chalmers et al, In Press 2017 Foundation Medicine, Inc. 18

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