Roche Pharma Day 2015 Molecular Information Garret Hampton VP, Oncology Biomarker Development Pharmaceuticals Division
Personalized healthcare A cornerstone of the Genentech / Roche strategy Diagnostics Pharma Molecule 60% of pipeline programs are being developed with companion diagnostics Actemra (Systemic sclerosis) Venetoclax (R/R CLL 17p) Atezolizumab (NSCLC) ACE 910 (Hemophilia) Esbriet (IPF) Lucentis (DR) Atezolizumab (Bladder) Alectinib (ALK+ NSCLC) Gazyva (1L CLL) 4 out of 9 BTDs enabled by a Dx that identified patients most likely to benefit 2
Significant advances in cancer biology Multiple molecular subsets of disease HER2 BRAF PIK3CA AKT1 MAP2K1 ALK Fusions EGFR PD-L1 Expression Unknown KIF5B-RET ROS1 Fusions NRAS KRAS MET MET splice site 3
Molecular Information in oncology Combination of molecular and patient data will enable change in R&D and clinical practice Patients matched to clinical trials Smarter, more efficient R&D Database & Analytics interface Better patient care Treatment plan selected Molecular understanding of cancer Patient outcomes 4
Multiple capabilities required Comprehensive tumor analysis and longitudinal assessment Comprehensive tumor analysis Continuous monitoring over time DNA-mutation & CNVs mrnaexpression Proteinexpression Cell free tumor DNA Imaging Ex: EGFR, BRAF DNA Cell signatures, targets RNA PDL1, other CI targets Multiplex IHC Ex: EGFR, BRAF Blood DNA Ex: ImmunoPet Imaging 5
FMI R&D collaboration Our partnership with FMI is key to informing R&D by leveraging molecular information Comprehensive tumor analysis Continuous monitoring over time DNA-mutation & CNVs mrnaexpression Proteinexpression Cell free tumor DNA Imaging Ex: EGFR, BRAF DNA Cell signatures, targets RNA PDL1, other CI targets Multiplex IHC Ex: EGFR, BRAF Blood DNA Ex: ImmunoPet Imaging 1 2 2 3 Molecular Information from FMI database and GNE/Roche clinical trials (FM1 & FM1 heme) Immunotherapy panel development (DNA & RNA) Development of a blood-based molecular assays 6
1 Roche / FMI database queries Example: PIK3CA mutations in multiple cancers Clinical trial data 50,000+ patient data in FMI database Roche database FMI clinical database 7
1 Implementation of FMI panels in development Focus on high-value clinical samples Clinical trial data 50,000+ patient data in FMI database Roche database FMI clinical database Current prioritization*: 1. Trials with post-progression biopsy (with archival baseline samples) 2. Phase 3 trial** n>300 3. Phase 2 trial* n>100 4. Phase 1b combos ** Positive trial, evidence of activity or potential identification of subset / high value for informing our pipeline (i.e., importance of disease setting / indication / treatment) 8
FMI R&D collaboration Immunotherapy R&D collaboration Comprehensive tumor analysis Continuous monitoring over time DNA-mutation & CNVs mrnaexpression Proteinexpression Cell free tumor DNA Imaging Ex: EGFR, BRAF DNA Cell signatures, targets RNA PDL1, other CI targets Multiplex IHC Ex: EGFR, BRAF Blood DNA Ex: ImmunoPet Imaging 2 2 Immunotherapy panel development (DNA & RNA) 9
2 Checkpoint inhibitors Most effective in inflamed tumors Inflamed TILs PD-L1 expression CD8+ T cells Genomic instability Pre-existing immunity Non-inflamed 10
2 PD-L1 IHC: Staining for TCs and ICs Assay sensitivity critical in detecting both cell types Immune cells (ICs) Tumor cells (TCs) Tumor and immune cells (TCs and ICs) WCLC 2015 e.g. bladder 1 IMvigor 210 ECC 2015, 2 POPLAR ECC 2015 e.g. NSCLC 11
Overall Survival 2 Patient selection enriches for benefit PD-L1 selected lung (TC & IC) and bladder cancer (IC) Lung cancer: Survival hazard ratio* Bladder cancer: Overall survival* TC3 or IC3 TC2/3 or IC2/3 TC1/2/3 or IC1/2/3 TC0 and IC0 0.49 0.54 0.59 1.04 100 80 60 Median OS Not Reached (95% CI, 7.6-NE) ITT N=287 0.73 0.2 1 2 0.1 Hazard Ratio In favor of atezolizumab In favor of docetaxel 40 20 0 IC2/3 IC0/1 + Censored 0 Median OS 6.7 mo (95% CI, 5.7-8.0) 1 2 3 4 5 6 7 8 9 10 11 Time (months) 12 * Monotherapy data ECC 2015 12
Overall Survival Change in sum of longest diameters (SLD) from baseline (%) 2 Benefit is not the same for every patient Some patients with high expression of PD-L1 do not benefit why? 100 80 60 40 20 0 Bladder cancer: Overall survival IC2/3 IC0/1 + Censored 0 Median OS 6.7 mo (95% CI, 5.7-8.0) 1 2 3 4 Median OS Not Reached (95% CI, 7.6-NE) 5 6 7 8 9 10 11 Time (months) 12 100 80 60 40 20 0-20 -40-60 -80-100 Why do these patients progress? Why do these patients respond? 0 21 42 63 84 105 126 147 168 189 210 231 252 273 294 Time on study (days) Example: Atezolizumab phase 1 data in urothelial bladder cancer patients 13
Biomarkers for cancer immunotherapy Key platforms for discovery and development Many types of data will be needed to inform patient care including: Proteinexpression mrnaexpression DNA-mutation & CNVs Cell free tumor DNA Other Dx & patient data PDL1, other CI targets IHC Cell signatures, targets RNA Ex: EGFR, BRAF DNA Ex: EGFR, BRAF Blood DNA Ex: Imaging, outcomes etc EMRs PDL-1 IHC and multiplex IHC 1 2 Immune cell types and signatures Mutation burden & neo-epitope prediction 14
Melanoma SCC NSCLC RCC Adeno NSCLC Bladder TNBC HER2+BC CRC HR+BC 1 Gene expression & combination hypotheses Understanding the biology of immune cells in tumors enables combination hypotheses Indication Myeloid Signature APC Signature NK cell Signature IFNg Signature IB Signature B cell Signature Teff Signature Th2 Signature Treg Signature Th17 Signature 2 1.6 0.67 0.22 1.1 1.6 2 2 1 0-1 -2 15
Melanoma SCC NSCLC RCC Adeno NSCLC Bladder TNBC HER2+BC CRC HR+BC 1 Gene expression & combination hypotheses Understanding the biology of immune cells in tumors enables combination hypotheses Myeloid signature (macrophages) associated with lack of response to atezolizumab in bladder cancer Hypothesis: Anti-CSF-1R removes macrophages which may enable atezolizumab activity Indication Myeloid signature: IL1B, IL8, CCL2 Myeloid Signature APC Signature NK cell Signature IFNg Signature IB Signature B cell Signature Teff Signature Th2 Signature Treg Signature Th17 Signature P=0.01 Anti-CSF-1R 2 1.6 0.67 0.22 1.1 1.6 2 2 1 0-1 -2 Anti-PD-L1 PD = Progressive disease SD = Stable disease CR/PR = Complete/partial response Tumor associated macrophages 16
1 Unlocking full value of CI through combinations Broadest industry portfolio in oncology Priming & activation anti-cea-il2v FP anti-ox40 anti-cd27* (Celldex) entinostat* (Syndax) T cell infiltration anti-vegf (Avastin) anti-ang2/vegf (vanucizumab) Antigen presentation T-Vec oncolytic viruses* (Amgen) INFa anti-cd40 CMB305 vaccine* (Immune Design) Clinical development Preclinical development Established therapies * Partnered or external Chen and Mellman. Immunity 2013; CI=cancer immunotherapy Antigen release EGFRi (Tarceva) ALKi (Alectinib) BRAFi (Zelboraf) MEKi (Cotellic) anti-cd20 (Gazyva) anti-her2 (Herceptin; Kadcyla; Perjeta) various chemotherapies lenalidomide rociletinib* (Clovis) T cell killing Cancer T cell recognition anti-cea/cd3 TCB anti-cd20/cd3 TCB anti-her2/cd3 TCB ImmTAC* (Immunocore) anti-pdl1 (atezolizumab) anti-csf-1r (emactuzumab) anti-ox40 IDOi IDOi* (Incyte) CPI-444* (Corvus) anti-tigit IDO1/TDOi* (Curadev) 17
1 Gene expression as a predictive biomarker Presence of INFg-producing CD8 T-cells predicts benefit in NSCLC treated with atezolizumab INFLAMMED PD-L1 expression IFN-g producing CD8+ T cells Genomic instability Pre-existing immunity Patients with a high tumor IFNg-associated gene signature derive OS benefit from atezolizumab in NSCLC (POPLAR) Schmid et al ECC 2015 18
Biomarkers for cancer immunotherapy Key platforms for discovery and development Many types of data will be needed to inform patient care including: Proteinexpression mrnaexpression DNA-mutation & CNVs Cell free tumor DNA Other Dx & patient data PDL1, other CI targets IHC Cell signatures, targets RNA Ex: EGFR, BRAF DNA Ex: EGFR, BRAF Blood DNA Ex: Imaging, outcomes etc EMRs 1 2 Immune cell types and signatures Mutation burden & neo-epitope prediction 19
PFS / OS # of mutations 2 Mutation burden is clinically important Tumors with higher numbers of mutation tend to be more sensitive to anti-pdl1 / anti-pd1 2 High 1 Low e.g. Lung tumors Mutation burden High Low Tumor cell 1 Tumor cell 2 Time Hypothetical partly based on: Rivzi et a l Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer, Science, 2015; Snyder et al Genetic basis for clinical response to CTLA-4 blockade in melanoma, NEJM 2014 20
Conclusions Diagnostics Pharma R&D insights Transformational time Molecular Information Personalised healthcare 21