Enterprise Interest Nothing to declare

Transcription

1 Enterprise Interest Nothing to declare

2 Immunotherapy Biomarker Identification with NGS Technology Andrew Felton VP Marketing and Product Management, Ion Torrent Business Monday, September 4, 2017 The world leader in serving science

3 Immunotherapy Biomarker Identification with NGS Technology Challenges with immuno-oncology clinical trials Ion NGS platform offers integrated solution for multidimensional approach Characterizing gene expression in tumor microenvironment for 36 immune response pathways Sequencing of T cell receptors to characterize immune status Characterizing tumor mutation burden to stratify clinical trial research samples Integrating biomarker research assays can improve clinical trial success Example 1: Can TRBV allele polymorphisms be potential future biomarkers for immune-mediated adverse events? Example 2: How do immune cell co-infiltrates influence response to immunotherapy? Immunotherapy biomarker identification with NGS technology 3

4 Immunotherapy Biomarker Identification with NGS Technology Summary: pipeline radar chart Recent setbacks in immunooncology clinical trials Source: clinicaltrials.gov 4

5 Opens Door to Important Questions for Any Trial Going Forward Can characterizing the tumor microenvironment (TME) predict immune response? Can we improve a selection strategy for immune therapy clinical research trials? Can we identify population subsets that are predisposed to immune-mediated adverse events? 5

6 No Single Biomarker for Mono and Combination Therapies Is Fully Predictive Positive predictive value for checkpoint inhibitor response 100% Comprehensive immune profiling Microsatellite Instability (MSI) PD-L1/2 Copy Number Variation PD-L1 IHC TPS >50% Mutational Burden (MuB) High PD-L1 IHC TPS >1% Prevalence Patients positive by biomarker Ideal biomarker(s) CD3+/CD8+ Tumor-Infiltrating Lymphocytes (TILs) 100% Isolated use of individual markers does not provide confidence that all the bases are being covered for a given patient None of current markers give any indication of adverse events. Source: OmniSeq 6

7 Tumor-Immune Environment Is Complex and Warrants Multi-marker Approach 3 4 Gene expression of >350 immune-related genes for tumor-infiltrating lymphocytes and T cell receptor signaling Up to 400 genes for mutational burden Microsatellite instability (MSI) for DNA mismatch repair deficiency 1. Release and capture tumor-associated antigens 2. Cancer antigen presentation 3. Priming and activation of effector T cells 4. Trafficking of cytotoxic T cells 5. Infiltration of cytotoxic T cells into tumor 6. T cell recognition / binding to T cell receptors (TCRs) 7. Killing of cancer cells Sequencing of T cell receptors to characterize immune repertoire of the sample 7

8 Ion NGS Platform Offers Integrated Solution for Multidimensional Approach Can characterizing the tumor micro- environment (TME) predict immune response? Can we identify population subsets that are predisposed to immunemediated adverse events? Can we improve a selection strategy for immune therapy clinical research trials? Sample prep Sequencing Analysis Characterizing gene expression in TME for immune response pathways Sequencing of T cell receptors to characterize immune repertoire of the sample + + Characterizing somatic mutations to assess tumor mutation burden RNA-Seq TCR-Seq DNA-Seq For Research Use Only. Not for use in diagnostic procedures. 8

9 Log2 reads/million Characterizing Gene Expression in TME for 36 Immune Response Pathways RNA-Seq Ion Torrent Oncomine Immune Response Research Assay Profile 395 genes associated with immune response Accurately quantify gene expression with wide dynamic range Capture low-expressing genes (INFγ) with greater sensitivity *Genes associated with T cell activation in tumor-infiltrating lymphocytes (TIL) The box-and-whisker plot depicts the log 2 ratios of the reads per million for the genes shown on the x-axis in 8 NSCLC samples. The box represents the interquartile range, the line within the box represents the median value, whiskers represent the 10 values, and outliers are represented by dots. 9

10 Sequencing of T Cell Receptors to Characterize Immune Status Sequence information on variable gene segments covering CDR1, 2, and 3 regions Flexible input requirements covering samples with low, medium, and high clonal diversity Clonality assessment with high accuracy without interference from primer bias TCR-Seq Coming soon: Ion AmpliSeq Immune Repertoire Assay Plus, TCR β RNA/cDNA CDR1 Leader FR1 FR2 CDR2 FR3 CDR3 Variable (V) Diversity (D) Joining (J) Constant Ion AmpliSeq primers ~ bp T-cell receptor 10

11 Rich Repertoire Analyses on Ion Reporter Read count QC metrics V-gene and allele identification Not full length Quality trimmed Perfect read Representation of different alleles Expanded clones In color lonotype identification Variable Joining CDR3 AA CDR3 NT Counts Frequency Rank Clone sizes per variable gene TRBV3-1 TRBJ2-3 ASSQDGGQNTDTQY GCCAGCAGCCAAGATGGGGGA CAGAACACAGATACGCAGTAT TRBV3-1 TRBJ2-1 ASSQQLGEQF GCCAGCAGCCAACAATTAGGT GAGCAGTTC TRBV11-2 TRBJ2-3 ASSLTALGRSPDTQY GCCAGCAGCTTAACCGCCCTA GGCAGGAGTCCAGATACGCAG TAT TRBV28 TRBJ1-2 ASSLHHKSNYGYT GCCAGCAGTTTACATCACAAAT CTAACTATGGCTACACC TRBV29-1 TRBJ2-2 SIIIQNTGELF AGCATCATAATTCAGAACACCG GGGAGCTGTTT

12 Characterizing Tumor Mutation Burden to Stratify Clinical Trial Research Samples Somatic mutations Single-sample analysis Mutations/Mb Bladder Brain Colon Head & neck Lung Ovary Stomach Uterus NCI-H647 Accurate quantification of somatic mutations to assess tumor mutation burden in research samples Single-sample workflow (tumor only) with low input requirement Targeted NGS panel with high multiplexing ability DNA-Seq Tumor mutation burden analysis* (in development) Mutation burden in MSI/MSS research samples Correlation of somatic mutations Reproducible mutation counts r = P-value = x 10 9 MSI MSS Tumor-normal analysis For Research Use Only. Not for use in diagnostic procedures. *The content provided herein may relate to products that have not been officially released and is subject to change without notice. 12

13 Integrating Biomarker Research Assays Can Improve Clinical Trial Success A more informative perspective Oncomine Immune Response Research Assay Introducing Ion AmpliSeq Immune Repertoire Assay Plus, TCR β In development Tumor Mutation Burden Analysis* Example 1: Can TRBV allele polymorphisms be potential future biomarkers for immune-mediated adverse events? Example 2: How do immune cell co-infiltrates influence response to immunotherapy? For Research Use Only. Not for use in diagnostic procedures. *The content provided herein may relate to products that have not been officially released and is subject to change without notice. 13

14 Example 1 Can TRBV allele polymorphisms be potential future biomarkers for immune-mediated adverse events? Certain TRBV alleles may increase TCR recognition of auto-antigens TCR CDR loops Rheumatoid arthritis (McDermott 1995, Maskmowych 1992) Multiple sclerosis (Hockertz 1998, Hibbard 1992, Seboun 1989) Narcolepsy (Han 2013, Hallmayer 2009) Type 1 diabetes (Hughes, 2015, Pierce 2013) Asthma (Cho 2001, Moffatt 1997) September 6, p.m. CDR Source: Allelic variants alter interaction of CDR1 and 2 with HLA HLA Room: Elicium 1 Session: OFP-13 Molecular Pathology Evaluating overlap of circulating and tumorinfiltrating T cells using AmpliSeq-based Ion Torrent TCR β immune repertoire sequencing Dr. Tim Looney, Sr. Scientist, Thermo Fisher Scientific, USA 14

15 Example 2 Identifying immune cell co-infiltrates that influence T cell function in the tumor microenvironment Myeloid suppressor? Repressive tumor microenvironment Permissive tumor microenvironment Inhibits T cell responses to tumor Permits T cell expansion and anti-tumor activity High T cell evenness (Normalized Shannon entropy) Low T cell evenness 15

16 Example 2 Myeloid-derived suppressor cell signature correlates with T cell evenness T cell evenness from Ion AmpliSeq Immune Repertoire Plus, TCR β 0.6 Gene function correlation with clone evenness Correlation with Oncomine Immune Response Research Assay NSCLC biopsies 1 = most even sizes 0 = least even sizes For Research Use Only. Not for use in diagnostic procedures. 1 Correlation Gene categories Proliferation Drug_target Checkpoint_pathway NK_cell_marker T_cell_differentiation Housekeeping Leukocyte_inhibition Antigen_processing Macrophage Type_I_interferon_signaling Neutrophil Lymphocyte_infiltrate NK_activation Dendridic_cell Interferon_signaling Tumor_marker,stemness Antigen_presentation Cytokine_signaling Lymphocyte_development T_cell_regulation Chemokine_signaling Tumor_antigen Type_II_interferon_signaling TCR_coexpression Helper_T_cells Tumor_marker PD-1_signaling,tumor_marker Myeloid_marker,MDSC Leukocyte_migration Lymphocyte_activation Adhesion,migration Dendridic_cell,macrophage Apoptosis Myeloid_marker PD-1_signaling T_cell_receptor_signaling T_cell_regulation,trafficking B_cell_marker Innate_immune_response B_cell_receptor_signaling Myeloid_marker,stem_cell 16

17 s, Normal Tissue (AUC) of >95%. Ion NGS Platform Offers Integrated Solution for Multidimensional Approach 17 Variable Correlation p.value Percent Necrosis Percent Neoplastic Primary Metastatic Recurrent Tissue Amount Tissue site Oncomine Immune Response Specimen Year Research Assay Specimen Type Normal Tissue Tumor Type Specimen Architecture Characteristics Ion AmpliSeq Immune Stroma Quality Repertoire Assay Plus, Predictive model Stroma Quantity TCR β Sample prep Specimen Characteristics Tumor Mutation Burden Analysis Sequencing For Research Use Only. Not for use in diagnostic procedures. Figure courtesy of OmniSeq. Analysis p = 0.39 p = 0.97 Figure 3: ROC curve for Immune Advance shows an area under the curve 100% 100% 7.0% (3/43) NPV = 93.0% (40/43) Low 0-49 Immune Score Immune score Responders and Non-Responders by Immune Activation Score Percentage of Samples, n = 88 Responder and non-responder research samples by immune activation score 45.5% (10/22) 54.5% (12/22) Indeterminate PPV = 100% 91.3% (21/23) 8.7% (2/23) High % Responders Non- Responders Figure 4: Predictive value of the Immune Score for response to Check Point Inhibitors: The clinical utility study included 88 patients with either melanoma, kidney cancer, bladder cancer, head and neck cancer, or NSCLC treated with one or more FDA-approved CPIs. o Immune Score >75 representing 26% of the tested population has a 95% predictive value for response to CPIs. o Immune Score <50 representing 49% of the tested population has a 95% predictive value for no response to CPIs. Predictive value of the immune score for response to checkpoint inhibitors: The clinical utility study included 88 tumor research samples with either melanoma, kidney cancer, bladder cancer, head and neck cancer, or NSCLC, treated with one or more FDA-approved CPIs.

18 Summary: Immunotherapy Biomarker Identification with NGS Technology Immunotherapies have the potential to positively impact patient response in the future There is much work to do to understand the variability in observed response among various types of cancer research samples, to improve clinical research Progress in the field of immuno-oncology will rely on a multidisciplinary and multiparametric approach to unlock a deeper understanding of the immune system in the context of a complex and heterogeneous disease To address the complexity of cancer and the immune system, Thermo Fisher Scientific offers multiple cancer research assays to provide comprehensive coverage of targets associated with key genes in immune response biomarker research, in order to: Better understand the tumor microenvironment Better stratify clinical trial research samples More accurately and rapidly determine response to therapy, and reduce adverse drug reactions For Research Use Only. Not for use in diagnostic procedures. 18

19 Thank you For Research Use Only. Not for use in diagnostic procedures Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the property of Thermo Fisher Scientific and its subsidiaries unless otherwise specified. COL The world leader in serving science

20 Appendix The world leader in serving science

21 Long-Read Capability Offers Advantages of Full V-Gene Characterization High throughput: >50,000 clones/sample generated from up to 16 samples with hands-on time < 30 min, TAT under 48 hr Unbiased output: V-gene primers are optimized to minimize bias and maximize coverage of possible rearrangements of the VDJ region CDR1 CDR2 CDR3 Comprehensive: up to 400 bp read length offers complete characterization/sequencing of all 3 CDR domains (1, 2, and 3) Clonotype assignment confidence score Highly accurate: sequencing and amplification error correction using proprietary statistical models leveraging unique insights about TCR and BCR mrna For Research Use Only. Not for use in diagnostic procedures. 21

22 Example 1 Can TRBV allele polymorphisms be potential future biomarkers for immune-mediated adverse events? CDR Allele name Location of AA variant Number of research samples having allele In Lym1k? In NCBI NR database? Potential genetic mechanism Allelic variants alter interaction of CDR1 and 2 with HLA Certain TRBV alleles may increase TCR recognition of auto-antigens, leading to immune-mediated adverse events Rheumatoid arthritis (McDermott 1995, Maskmowych 1992) Multiple sclerosis (Hockertz 1998, Hibbard 1992, Seboun 1989) Narcolepsy (Han 2013, Hallmayer 2009) Type 1 diabetes (Hughes, 2015, Pierce 2013) Asthma (Cho 2001, Moffatt 1997) TRBV11-2*32k FR3 1 No No TRBV11-3*1k FR2 18 No Yes TRBV12-4*46k FR2 1 No No TRBV12-5*4k FR2 1 No No TRBV19*17k FR2 1 No No TRBV23-1*2k FR3 1 No No TRBV24-1*1k FR2 43 No Yes TRBV5-3*1k FR2 1 No No TRBV5-8*1k FR1 17 No No TRBV6-2*156k FR1, CDR1, FR2, CDR2, FR3 1 No No TRBV6-5*106k CDR2 1 No No TRBV11-1*11p CDR1, FR2/CDR2 1 Yes No TRBV30*6p FR3 1 Yes No TRBV5-5*9p FR3 2 Yes No TRBV5-6*11p FR3 4 Yes No Gene conversion between TRBV11-3*01 and *02 Gene conversion between TRBV12-4*01 and *02 Gene conversion between TRBV6-1*01 and TRBV6-2*01 For Research Use Only. Not for use in diagnostic procedures. 16 of 85 research samples carry an uncommon, novel TRBV allele 22

23 Overview of Oncology Portfolio Routine research Oncology research specimen Translational Molecular profiling solutions + Liquid biopsy solutions + Immuno-oncology solutions Full characterization of oncology samples Immuno-oncology solutions Oncomine Comprehensive Assay Oncomine cfdna Assays for Lung, Breast, Colon Oncomine Immune Response Research Assay Liquid biopsy solutions Oncomine Focus Assay Oncomine Lung cfdna Research Assay Ion AmpliSeq Immune Repertoire Assay Plus, TCR Oncomine BRCA Research Assay Oncomine Breast cfdna Research Assay v2 Oncomine Mutation Load Research Assay Molecular profiling solutions Oncomine Solid Tumor Research Assay Oncomine Pan-Cancer Cell-Free Research Assay Commercial product Product in development Oncomine assays are Ion Torrent Oncomine assays For Research Use Only. Not for use in diagnostic procedures. 23