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Genomics Enhances Clinical Immuno-Oncology Trials

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Genomics Enhances Clinical Immuno-Oncology Trials Genomics enhances Clinical Immuno-oncology Trials Patrice Hugo, Ph.D. Chief Scientific Officer Personalized Medicine World Congress, Mountain View, January 26, 2016 COMPANY CONFIDENTIAL Copyright ©2015 Q2 Solutions. All rights reserved. Introducing Q2 Solutions, a Quintiles-Quest Joint Venture Established 02 Jul 2015 Consulting Real World and Late Phase Diagnostic Testing Drug Screening Portfolio & Strategy Planning Technology Solutions Diagnostic Products Informatics Clinical Trial Execution Patient and Provider Healthcare IT Engagement Wellness and Risk Management LaboratoriesLaboratories Product Marketing and Sales Employer Health ClinicalClinical Trials Trials Central Labs Central Labs Anatomic Pathology Anatomic Pathology (CT) BioAnalytical (Advion) Vaccine Testing (Focus Diagnostics Clinical Trials) Genomics (EA) Biorepository COMPANY CONFIDENTIAL 2 Q2 Solutions Footprint & Scientific Distribution Edinburgh Oss Indianapolis Beijing Teterboro Valencia Ithaca Tokyo Mumbai SJC EA Atlanta Singapore Pretoria Central Lab Hubs Supporting more than 180,000 AP labs investigative sites worldwide Molecular testing/Genetics Assay Development ADME-BioA-Immunogenicity COMPANY CONFIDENTIAL 3 The Opportunity: Immuno-oncology As The Future Of Cancer Treatment • Over 45 immuno-oncology drugs approved (US)* • 57 immuno-oncology drugs in development* • Over 250 studies registered/ongoing* • Increasing number of relevant publications annually** Brahmer et al, N Engl J Med 2015; 373:123-135 COMPANY CONFIDENTIAL 4 Expansion of PD-1/PL-L1 drugs into multiple indications and combinations adds complexity and opportunity Pembrolizumab (NSCLC) Advanced Solid Tumors MSB0010718C MEDI4736 BMS-936559 MPDL3280A + RO7009789 MEDI4736 + tremelimumab Nivolumab + MPDL3280A + Interferon alfa-2b / ipilimumab Solid Tumors iliolumbar AMP-514 + MEDI4736 MPDL3280A + cobimetinib Nivolumab MPDL3280A + MPDL3280A + vemurafenib (Phase Ib) MPDL3280A + bevacizumab AMP-554 RO6895882 MPDL3280A + Nivolumab + ipilimumab and/or chemotherapy Nivolumab + interleukin-21 MEDI4736 (I/II) RO5509554 Pembrolizumab + dabrafenib (I/II) + trametinib Anti-LAG3 (BMS-986016) ± nivolumab Nivolumab + multiple class 1 peptides & montanide ISA 51 VG Prostate Cancer Pembrolizumab (I/II) MEDI4736 + dabrafenib + trametinib or trametinib alone (I/II) Pembrolizumab Pembrolizumab (I/II) Melanoma Pidilizumab + sipuleucel-T + Nivolumab + ipilimumab Nivolumab Pancreatic cyclophosphamide Nivolumab ± ipilimumab Nivolumab sequentially with ipilimumab Tremelimumab and / or MEDI4736 + radiation Nivolumab Pidilizumab + gemcitabine Melanoma, NSCLC Nivolumab ± ipilimumab (I/II) Pembrolizumab Pembrolizumab MPDL3280A + carboplatin + paclitaxel ± bevacizumab Pembrolizumab (II/III) MPDL3280A + erlotinib (Phase Ib) MPDL3280A NSCLC Malignant Gliomas Pidilizumab (I/II) MPDL3280A Nivolumab ± gemcitabine/cisplatin, Pembrolizumab (I/II)2 MPDL3280A + carboplatin pemetrexed/cisplatin, carboplatin/paclitaxel, 4 MPDL3280A + obinutuzumab + nab-paclitaxel Nivolumab bevacizumab, erlotinib, ipilimumab MPDL3280A + carboplatin + Pembrolizumab MPDL3280A Hodgkin Lymphoma, paclitaxel / nab-paclitaxel MPDL3280A + radiation therapy MPDL3280A ± lenalidomide Avelumab Myeloma, MDS, NHL Nivolumab ± ipilimumab Nivolumab Pembrolizumab Nivolumab MPDL3280A + MEDI4736 + tremelimumab (I/II) Pembrolizumab bevacizumab Hepatocellular ± MPDL3280A + INCB024360 Pembrolizumab Pidilizumab dendritic cell/RCC fusion cell vaccine Glioblastoma Nivolumab ± Pembrolizumab + pazopanib MPDL3280A ± bevacizumab vs sunitinib ipilimumab (I/II) Biomarker driven therapy Nivolumab + sunitinib, pazopanib, or ipilimumab MPDL3280A Pembrolizumab + cisplatin + 5-FU3 Nivolumab Avelumab MPDL3280A ± bevacizumab vs sunitinib Pembrolizumab Gastric, SCLC, TNBC, HNC, Urothelial Pembrolizumab MPDL3280A Renal Cell Carcinoma Mono therapy Combination Solid or Hematological Malignancies Colon Cancer Skin Cancer Phase I Phase II Phase III Source: Quintiles Internal Analysis, Clinicaltrials.gov, BioPharm Clinical & ADIS Database, Dolan, 2014, Cancer Control. 2014:231-237 COMPANY CONFIDENTIAL 5 May 2015 The Challenge: Identifying & Implementing Biomarkers in I-O Drug Development • Defining I-O biomarker intended use • Testing in the context of clinical trials – Exploratory/decision enabler − Availability of appropriate samples – Who will respond? − Sufficient quality and quantity – Who is responding (typical criteria − Best technology/approach do not always apply for I-O)? − Robust data analysis and decision – How to match the biomarker with cutoffs the I-O therapy class? − Technical or analytical variability – How to anticipate/monitor risk of AEs? − Companion diagnostic path − ROI for biomarker deployment and implementation of CDx COMPANY CONFIDENTIAL 6 Immuno-Oncology is Genomics Solutions Oriented Molecular methods provide an advantage for complex biomarker analysis Checkpoint Inhibitor Expression Beyond IHC: Incorporation of genomics due to limitations of other methodologies (e.g. subjectivity, quantitative ability, low capacity for multiplex, sensitivity, etc.) Gene Mutational Immune Expression Load Repertoire Signature COMPANY CONFIDENTIAL 7 Immuno-Oncology Innovation New opportunities for molecular characterization Samples Analysis Potential Application Routinely collected Data can be mined for molecular How these characterizations can clinical samples immune-oncology characterization be used in drug studies Exploitation of Innate and Adaptive Immune Response to Self-recognition Tumors HLA and KIR genotyping Slides Immune activation B and T-cell repertoire, Optimized Patient Selection Immune gene signature Refinement of Immuno- modulatory Therapies Saliva Biopsy Tumor characterization Blood DNA and RNA Cancer Vaccines and Tumor- Specific Immune Responses COMPANY CONFIDENTIAL 8 Application of Genomics in I-O Somatic mutation frequency • Observed higher ‘mutation burden’ in • Increased immunogenic mutation tumor types correlated to clinical frequency associated with longer survival responses to checkpoint inhibitors Vomehr et al., Brown et al., Curr Opinion Genome Res. Imm. 2016. 39: 2014. 24: 743- 14-22 750 Quantitative Predictive Charactization (e.g. # mutations) Tumor Specimen of assay for RNA, DNA Mutation response to Burden Qualitative I-O therapies (e.g. immunogenicity) Whole Exome Deep Sequencing COMPANY CONFIDENTIAL 9 Application of Genomics in I-O Gene Expression Profiling • An inflammatory gene signature is associated Commercial with increased response to immunotherapy I-O and positive clinical outcomes Assays RNAseq CD8 expression HLA-A expression qPCR Immune activation Checkpoint inhibitor expression Immunosuppressive cytokines Immune polarization (e.g. M2, Th1) Brown et al., Genome Res. 2014 Tumor Gene ‘Signature” Predictive assay Pharmacodynamic response Novel drugs Herbst et al, Nature 2014 Combination therapies COMPANY CONFIDENTIAL 10 Application of Genomics in I-O Immune Repertoire and Antigen Presentation • TCR and BCR clonality shows associations with disease severity and prognosis as well as the tumor microenvironment and anti-tumor immune response Ruggiero et al, Nat Commun. 2015 • Predictive ‘immunogenicity’ algorithms using HLA genotyping data + exome sequences show association with survival rate Brown et al., Genome Res. 2014. 24: 743-750 COMPANY CONFIDENTIAL 11 Immuno-Oncology Innovation New opportunities for molecular characterization Assessment Genomics Application Somatic and Germline Mutation Analysis Low frequency SNP detection, Ploidy, Breakpoint Analysis/Fusion Detection, Phasing and Associations, LOH, Tumor mutation burden Gene Expression Profiling RNA-Seq, RNA panels, Nanostring HLA Characterization HLA Calling (DNA, RNA, Arrays) Check-point inhibitor expression RNA-Seq, RNA panels KIR genotyping and expression KIR genotyping and expression VDJ rearrangement (RNA/DNA) clonal diversity and enumeration B-Cell T-Cell (α/β, γ/δ) Receptor repertoire Monitoring expansion of CAR systemic T-cells and TIL expansion Cancer Vaccines (neoantigen) and Whole exome sequencing (WES), RNA-Seq Tumor-Specific Immune Responses Through Application of Genomics to Immuno-oncology: • Gain a deeper understanding of drug safety and PD markers • Achieve higher-quality, more precise data on tumor characteristics COMPANY CONFIDENTIAL 12 • Develop more effective, personalized therapies 12 Typical Specimen Collection is Already Sufficient Genomic assays are often feasible and more economical in terms of sample usage Different technologies can vary nucleic acid inputs below ASSAY INPUT NEEDS* HLA Allele Calling 150 RNA or 1100DNA Genotyping Risk Alleles 500 DNA IGVH Testing 500 RNA Targeted (Capture) Sequencing 150 DNA Whole Exome Sequencing 150 DNA PCR-enriched Sequencing 40 DNA Gene Expresion by qRT-PCR 30 RNA ctDNA Allele Calling 10 DNA 0 200 400 600 800 1000 Input Amount (ng) * Varies by matrix COMPANY CONFIDENTIAL 13 The Overlooked Fundamentals: Sample Quality Methods and best practices to maximize integrity of clinical specimens Pre-Analytics Standardized collection Established guidelines (fixatives, fixation time, methods etc.), collection device, biopsies versus blood, etc. Proper shipping/handling Logistics infrastructure, insulated shippers Stabilization/Preservation PAXgene tubes, cfDNA tubes, ethanol fixation Sample Processing Cell Enrichment Cell purification, PBMC isolation, CTC isolation, FFPET macrodissection Ex vivo assays Cell stimulation, drug treatment, co-culture Sample QC Pathology review (% tumor), tissue quantity, viability assessment Nucleic Acid
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