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1 RNAi Screen, NGS and Expression Profiling for Biomarker Discovery Namjin Chung Applied Genomics Bristol-Myers Squibb 2012 DOT Oct 2, 2012 Boston, MA

2 Pharmacogenomics Study of the role of inheritance in inter-individual variation in drug response Weinshilboum 2004, Nat Rev Drug Discov Study of interindividual variations in whole-genome or candidate gene, single-nucleotide polymorphism (SNP) maps, haplotype markers, or alterations in gene expression or inactivation that may be correlated with pharmacological function and therapeutic response. FDA 2005, Guidance for industry pharmacogenomic data submissions Personalized medicine Aims to customize drug therapy tailored to individual genetic makeup. Identify and validate various types of biomarkers that help guide toward maximal efficacy with minimal adverse effects. 2

3 Gene expression signature as a biomarker Genes Responders Nonresponders Patients Responders Non-responders Expression signature, GWAS-SNP, somatic mutations, etc. Typically done during early clinical trials. Requires a large number of patient samples for correlational data (expression response). Expensive, time-consuming with no clear promise. Inefficiency yprovides a new opportunity! 3

4 RNAi for biomarker discovery alterations in gene expression or inactivation that may be correlated with pharmacological function and therapeutic response. Germline mutations Somatic mutations Induced inactivation (RNAi) Potential benefits of RNAi-based biomarker discovery Establishing cause-effect relationship requires less samples and less time. Mechanistic insights into the pathways where a drug operates. Combination therapy targets for synthetic lethal RNAi. Right translational strategy ensures successful in vitro to clinic transition. 4

5 Overview Concept of using RNAi for biomarker discovery Technological platform Genome-wide RNAi screen based on lentiviral shrna and NGS. Application to oncology biomarker discovery Targeting notch pathway Integrative genomics approach 5

6 Drug response altered by RNAi 100 ability Tum mor cell vi RNAi 40 RNAi 20 0 Depletion Enrichment Compound [μm] 6

7 Targeting notch pathway for oncology γ-secretase inhibitors in clinical trials Company Compound Phase Indications Roche RO Colon, Breast, Lung, Melanoma, Brain, GI, Kidney, Ovarian, etc. Merck MK Breast, Pancreas, Brain, Blood Pfizer PF Blood, Solid tumor (source: clinicaltrials.gov) 7

8 Pooled lentiviral shrna screen Pooled shrna library (11k genes) A pool of lentivirus representing 55,000 shrna for 11,000 genes cell culture molecular biology pooled virus target cells TALL-1 (liquid) MDA-MB-468 (solid) Affy custom array NGS (Illumina) data analysis Luo B et al. PNAS

9 Advances in screening readout Affy microarray Illumina sequencing Affy microarray Hybridization of shrna onto affy microarray probe. Requires custom-designed microarray. Cannot detect shrnas outside chip design. 1 st gen pooled screening readout. One sample per chip. Limited dynamic range. Illumina sequencing HT sequencing of shrnas, agnostic of chip design or library composition. ~150 million reads per sequencing lane. 2 nd gen pooled screening readout. Samples can be easily multiplexed. Wide dynamic range. 9

10 Simple, fast assay development 100 1) Determination MOI = ~0.3 (to ensure unique infections) Infection Effic ciency virus (ul) 2) Replication of drug sensitivity under viral infection High Low % inhibition of TALL-1 proliferation Compound (nm) 10

11 Screening process infection assay sequencing Infection (MOI=0.3), selection & pretreatment expansion (11 days) Compound Rx, cell Collect cells, PCR counting & re-plating gdna & NGS (2-3 rounds in days) (3 weeks) Low MOI (~0.3) is critical to avoid multiple infections per cell. Pre-treatment expansion help eliminates essential (housekeeping) genes. Three compound treatment conditions after expansion (6/treatment). Repeated rounds (2-3) of compound treatment, cell proliferation and counting and re-plating to reach desired screening window. 11

12 Enrichment: more resistant to drug Cell pr roliferatio on 100 RNAi 80 alone RNAi + Drug Enrichment 0 DMSO IC90 Drug condition 12

13 Depletion: more sensitive to drug RNAi alone 100 Cell pr roliferatio on RNAi + Drug Depletion 0 DMSO IC10 Drug conditions 13

14 Identification of preliminary hits High vs. DMSO Low vs. DMSO (Enrichment) (Depletion) 4x 2x ½ x ¼ x TALL-1 16x 4x ½ x ¼ x MDA-MB

15 γ-secretase subunits: validation of screen PEN-2 APH-1 NCSTN rank 23 rank 8 p = p = PSEN-1 rank k6 rank 1676 p = p = 0.14 γ-secretase complex APH-1 and PEN-2 were not in the screening library but will be included in confirmation process. 15

16 in vitro response profiling Responder (HCC-70) Non-responder (ZR-75-1) total cells t days total cells t days Responders Non-responders Cell Line IC50 (nm) Cell Line IC50 (nm) HCC ZR-75-1 ND MDA-MB SK-BR-3 ND HCC HCC-1419 ND HCC T47D ND BT Frank Lee 16

17 Integrative genomics: function + expression Resistant when KD 16x 4x Overexpressed in sensitive cells Cell Lines Sensitive Resistant KD of overexpressed genes decreasing sensitivity RNAi Expression Genes ½ x ¼ x RNAi Expression Overexpressed in resistant cells Sensitive when KD KD of overexpressed genes changing R to S Fei Huang 17

18 Creation of confirmation screening library RNAi screen (246 genes) Expression profiling (110 genes) Notch pathway (28 genes) Controls (44 genes) Confirmation mini-pool (428 genes) 18

19 Confirmation screens (in vitro + in vivo) in vitro CRC screen Lentiviral shrna library Infect tumor cells (11 d) NGS and data analysis Inject into mouse in vivo Xenograft assay (5-13 d) 19

20 Path forward and conclusions Path forward Run confirmation screens in vitro and in vivo Identify candidate biomarkers and enriched pathways. Test individual hairpins in vitro and in vivo. Validate knockdown. Validate candidate biomarkers in clinical trials. Conclusions Pooled lentiviral shrna screen, combined with NGS, can provide a new opportunity in biomarker discovery. Integrative genomics approach helps prioritize candidate biomarkers. Translational strategy for transitioning from in vitro preclinical observations to clinical replication is desired. 20

21 Acknowledgments Applied Genomics Ericka Loffredo Yan Zhang Sarah Hu Isaac Neuhaus Aiqing He Manling Ma-Edmonds Nathan Siemers Chris Miller Mark Cockett BMS Oncology Frank Lee Mei-Li Wen Jack Hunt BMS DMCP Xi-De Wang Fei Huang Bruce Fisher John Cogswell Qiuyan Wu Broad Institute/TRC Dave Root Glenn Cowley Serena Silver Shuba Gopal John Doench 21