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COMPANY CONFIDENTIAL
Genomics enhances Clinical Immuno-oncology Trials
Patrice Hugo, Ph.D. Chief Scientific Officer Personalized Medicine World Congress, Mountain View, January 26, 2016
2 COMPANY CONFIDENTIAL
Introducing Q2 Solutions, a Quintiles-Quest Joint Venture Established 02 Jul 2015
Anatomic Pathology
BioAnalytical (Advion)
Genomics (EA)
Central Labs
Anatomic Pathology (CT)
Central Labs
Biorepository
Vaccine Testing (Focus Diagnostics Clinical Trials)
Consulting Portfolio & Strategy Planning Clinical Trial Execution Laboratories
Real World and Late Phase Technology Solutions Patient and Provider Engagement Product Marketing and Sales
Diagnostic Testing Diagnostic Products Healthcare IT Wellness and Risk Management Employer Health
Drug Screening Informatics Clinical Trials Laboratories Clinical Trials
3 COMPANY CONFIDENTIAL
Q2 Solutions Footprint & Scientific Distribution
Valencia
SJC
Singapore
Teterboro
Edinburgh
Beijing
Tokyo
Pretoria
EA Mumbai
Atlanta
Oss
Ithaca
Indianapolis
Supporting more than 180,000 investigative sites worldwide
Central Lab Hubs
AP labs Molecular testing/Genetics Assay Development ADME-BioA-Immunogenicity
4 COMPANY CONFIDENTIAL
The Opportunity: Immuno-oncology As The Future Of Cancer Treatment
Brahmer et al, N Engl J Med 2015; 373:123-135
• Over 45 immuno-oncology drugs approved (US)*
• 57 immuno-oncology drugs in development*
• Over 250 studies registered/ongoing*
• Increasing number of relevant publications annually**
5 COMPANY CONFIDENTIAL
Expansion of PD-1/PL-L1 drugs into multiple indications and combinations adds complexity and opportunity
Mono therapy Combination Phase I
Phase II Phase III
BMS-936559
MEDI4736 (I/II)
Nivolumab + iliolumbar
Advanced Solid Tumors
MPDL3280A + vemurafenib (Phase Ib)
MEDI4736 + dabrafenib + trametinib or trametinib alone (I/II)
Nivolumab ± ipilimumab Nivolumab + ipilimumab
Nivolumab + ipilimumab
Nivolumab sequentially with ipilimumab Nivolumab
Nivolumab + multiple class 1 peptides & montanide ISA 51 VG
Nivolumab
Pembrolizumab
Pembrolizumab
Melanoma
MPDL3280A + erlotinib (Phase Ib) MPDL3280A
MPDL3280A
MEDI4736 + tremelimumab (I/II)
Nivolumab ± gemcitabine/cisplatin, pemetrexed/cisplatin, carboplatin/paclitaxel, bevacizumab, erlotinib, ipilimumab Nivolumab
Pembrolizumab
Pembrolizumab (II/III) NSCLC
MPDL3280A ± bevacizumab vs sunitinib
Nivolumab + sunitinib, pazopanib, or ipilimumab
Nivolumab
Nivolumab
Pembrolizumab + pazopanib
Pidilizumab ± dendritic cell/RCC fusion cell vaccine
Renal Cell Carcinoma MPDL3280A
Solid or Hematological Malignancies
Pembrolizumab
Colon Cancer
Pembrolizumab
Nivolumab ± ipilimumab (I/II)
Gastric, SCLC, TNBC, HNC, Urothelial
Nivolumab ± ipilimumab
Glioblastoma
Nivolumab
Hepatocellular
Pembrolizumab Hodgkin Lymphoma, Myeloma, MDS, NHL
Pidilizumab (I/II) Malignant Gliomas
Pembrolizumab Melanoma, NSCLC
Pidilizumab + gemcitabine
Pancreatic Pidilizumab + sipuleucel-T + cyclophosphamide
Prostate Cancer
MPDL3280A + bevacizumab and/or chemotherapy
MPDL3280A + cobimetinib
MEDI4736 + tremelimumab MEDI4736
MSB0010718C
Anti-LAG3 (BMS-986016) ± nivolumab
Nivolumab
Nivolumab + interleukin-21 AMP-554
Solid Tumors
Pembrolizumab (NSCLC)
Nivolumab ± ipilimumab (I/II)
AMP-514 + MEDI4736
MPDL3280A + radiation therapy MPDL3280A + carboplatin + paclitaxel / nab-paclitaxel
Pembrolizumab
Pembrolizumab
MPDL3280A
Pembrolizumab (I/II)
Pembrolizumab (I/II)
Pembrolizumab (I/II)2
Pembrolizumab + dabrafenib (I/II) + trametinib
Pembrolizumab + cisplatin + 5-FU3
Tremelimumab and / or MEDI4736 + radiation
MPDL3280A ± lenalidomide
MPDL3280A ± bevacizumab vs sunitinib
MPDL3280A + bevacizumab
MPDL3280A + RO6895882
MPDL3280A + carboplatin + nab-paclitaxel
MPDL3280A + RO5509554
MPDL3280A + RO7009789
MPDL3280A + obinutuzumab4
MPDL3280A + Interferon alfa-2b / ipilimumab
MPDL3280A
Avelumab
Skin Cancer
Avelumab
MPDL3280A + INCB024360
MPDL3280A + carboplatin + paclitaxel ± bevacizumab
Biomarker driven therapy
May 2015
Source: Quintiles Internal Analysis, Clinicaltrials.gov, BioPharm Clinical & ADIS Database, Dolan, 2014, Cancer Control. 2014:231-237
6 COMPANY CONFIDENTIAL
• Defining I-O biomarker intended use – Exploratory/decision enabler
– Who will respond?
– Who is responding (typical criteria do not always apply for I-O)?
– How to match the biomarker with the I-O therapy class?
– How to anticipate/monitor risk of AEs?
The Challenge: Identifying & Implementing Biomarkers in I-O Drug Development
• Testing in the context of clinical trials − Availability of appropriate samples
− Sufficient quality and quantity
− Best technology/approach − Robust data analysis and decision
cutoffs
− Technical or analytical variability
− Companion diagnostic path − ROI for biomarker deployment and
implementation of CDx
7 COMPANY CONFIDENTIAL
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.)
Immuno-Oncology is Genomics Solutions Oriented Molecular methods provide an advantage for complex biomarker analysis
Mutational Load
Gene Expression Signature
Immune Repertoire
8 COMPANY CONFIDENTIAL
Routinely collected clinical samples
Data can be mined for molecular immune-oncology characterization
Immuno-Oncology Innovation New opportunities for molecular characterization
Samples Analysis Potential Application How these characterizations can be used in drug studies
Saliva
Blood
Biopsy
Slides
Self-recognition HLA and KIR genotyping
Immune activation B and T-cell repertoire, Immune gene signature
Tumor characterization DNA and RNA
Cancer Vaccines and Tumor-Specific Immune Responses
Optimized Patient Selection Refinement of Immuno-modulatory Therapies
Exploitation of Innate and Adaptive Immune Response to Tumors
9 COMPANY CONFIDENTIAL
• Observed higher ‘mutation burden’ in tumor types correlated to clinical responses to checkpoint inhibitors
Application of Genomics in I-O Somatic mutation frequency
Brown et al., Genome Res. 2014. 24: 743-750
• Increased immunogenic mutation frequency associated with longer survival
Tumor Specimen RNA, DNA
Whole Exome Deep Sequencing
Charactization of
Mutation Burden
Predictive assay for
response to I-O therapies
Quantitative (e.g. # mutations)
Qualitative (e.g. immunogenicity)
Vomehr et al., Curr Opinion Imm. 2016. 39: 14-22
10 COMPANY CONFIDENTIAL
• An inflammatory gene signature is associated with increased response to immunotherapy and positive clinical outcomes
Herbst et al, Nature 2014
CD8 expression HLA-A expression
Brown et al., Genome Res. 2014
RNAseq
Commercial I-O
Assays
qPCR
Immune activation Checkpoint inhibitor expression Immunosuppressive cytokines
Immune polarization (e.g. M2, Th1)
Tumor Gene ‘Signature”
Predictive assay Pharmacodynamic response
Novel drugs Combination therapies
Application of Genomics in I-O Gene Expression Profiling
11 COMPANY CONFIDENTIAL
• TCR and BCR clonality shows associations with disease severity and prognosis as well as the tumor microenvironment and anti-tumor immune response
• Predictive ‘immunogenicity’ algorithms using HLA genotyping data + exome sequences show association with survival rate
Application of Genomics in I-O Immune Repertoire and Antigen Presentation
Ruggiero et al, Nat Commun. 2015
Brown et al., Genome Res. 2014. 24: 743-750
12 COMPANY CONFIDENTIAL 12
Immuno-Oncology Innovation New opportunities for molecular characterization
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 • Develop more effective, personalized therapies
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
B-Cell T-Cell (α/β, γ/δ) Receptor repertoire
VDJ rearrangement (RNA/DNA) clonal diversity and enumeration Monitoring expansion of CAR systemic T-cells and TIL expansion
Cancer Vaccines (neoantigen) and Tumor-Specific Immune Responses Whole exome sequencing (WES), RNA-Seq
13 COMPANY CONFIDENTIAL
Different technologies can vary nucleic acid inputs below
Typical Specimen Collection is Already Sufficient Genomic assays are often feasible and more economical in terms of sample usage
* Varies by matrix
10
30
40
150
150
500
500
1100
0 200 400 600 800 1000
ASSAY INPUT NEEDS*
Input Amount (ng)
HLA Allele Calling
Genotyping Risk Alleles
IGVH Testing
Targeted (Capture) Sequencing
Gene Expresion by qRT-PCR
ctDNA Allele Calling
150
Whole Exome Sequencing
PCR-enriched Sequencing
RNA or DNA
DNA
RNA
DNA
DNA
DNA
RNA
DNA
14 COMPANY CONFIDENTIAL
The Overlooked Fundamentals: Sample Quality Methods and best practices to maximize integrity of clinical specimens
Pre-Analytics
Standardized collection methods
Established guidelines (fixatives, fixation time, 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 Preparation
FFPET-specific protocols (e.g. degraded material)
FFPET extraction and purification methods, optimized target capture protocols, increased depth of coverage, specialized bioinformatics
15 COMPANY CONFIDENTIAL
• PD-L1 IHC current dominates I-O CDx landscape but evidence is mounting that PD-L1 may not be the best marker nor IHC the optimal method
• Gene expression-based CDx studies are underway with more genomics-based assays in development
• Genomics-based CDx will require a validated method, platform, bioinformatics pipeline, and software
• CLIA-validated LDTs generating robust data can set the stage for successful CDx development
The Future of Immuno-Oncology Companion Diagnostics Will Incorporate Genomics CDx options for checkpoint inhibitors are advancing down different paths
I-O Target (e.g. inhibitory receptor/ligand) Detection of drug target • IHC likely to be on drug labels • Methods to assess gene
expression may be informative over IHC
Immune / Inflammatory Status • Strong biological rationale • Complex test with novel platforms • Multiple platform/assay developers
emerging
Genetic Load/Neo-Antigens • Promising clinical evidence
(mismatch repair defects) • Complex science and technology • Unclear path to CDx but NGS-
based CDx on horizon for targeted drugs
16 COMPANY CONFIDENTIAL
Thank You Questions?
