Neoantigen Identification using ATLAS Across Multiple Tumor Types Highlights Limitations of Prediction Algorithms

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1 Neoantigen Identification using ATLAS Across Multiple Tumor Types Highlights Limitations of Prediction Algorithms Wendy Broom, Ph.D. Keystone: Translational Systems Immunology, Snowbird February 1st 2018

2 Genocea offers a compelling solution to antigen discovery ATLAS TM : foundational antigen selection technology platform Operationalized for rapid antigen selection Discovers the true antigens for CD4 + and CD8 + T cells Demonstrates the futility of in silico approaches IND-ready neoantigen vaccine Only vaccine to use a patient s T cells for true neoantigen discovery Multiple strategic collaboration opportunities: Neoantigen vaccine (IND in early 2018) Tumor-associated antigens for Vaccines Diagnostics Bi-specific therapeutics Novel viral cancer antigens (EBV) 2

3 Genocea addresses the most important cancer vaccine problem: antigen selection How Do We NeoA choose among thousands of candidate neoantigens? TAA move past the same few tumor-associated antigens that consistently fail? Viral select antigens from a large, complex viral proteome? 3

4 ATLAS platform uses patients own T cells to identify true antigens Input: Every candidate antigen Patientspecific ATLAS cytokine readout: - Antigen or not? - Stimulatory or inhibitory? Patient s own T cells * HLA agnostic CD4 + & CD8 + antigens Hot and cold tumors Rapid and high throughput *For CD4 + T cell screening the cllo is not co-expressed, resulting in conventional endosomal processing and presentation by MHC cii 4

5 ATLAS optimized as a rapid, industrialized platform for antigen discovery Neoantigen vaccine TAA/viral vaccine Dendritic Cell T cell 5

6 GEN-009: Neoantigen vaccine program 6

7 in silico antigen selection approaches yield limited vaccine immunogenicity Immunogenicity from FIH Neoantigen Vaccine Trials Patients % Response to Neoantigens* via ex vivo ELISpot** CD4 + CD8 + Using ATLAS platform for neoantigen selection may significantly improve outcomes Ott et al. 1 Peptide + adjuvant Sahin et al. 2 RNA Melanoma (stage IIIB/C & IVM1a/b) 20% 0% Melanoma (stage III & IV) 20% *** Vaccine delivery method does not impact response rates *Cohort level; number of SLP with responses in all patients/total SLP immunized across all patients **ex vivo ELISpot represents industry-standard approach to measuring T cell responses in blood. Papers also disclosed higher neoantigen response rate, primarily CD4 +, after multiple rounds of artificial in vitro stimulation over 10-21d. ***Presumed CD4 + (total PBMC). Text states majority of responses were CD4 + but not disclosed in figure 1 Ott et al., 2017 Nature 2 Sahin et al., 2017 Nature 7

8 N o rm a liz e d C y to k in e N o rm a liz e d C y to k in e C o n c e n tra tio n (IF N ) Neoantigens of pre-existing T cell responses increase probability of effective vaccination ATLAS identifies neoantigens of preexisting responses Pre-existing immunity associated with effective vaccination T cell response readout, per candidate antigen CD Neoantigens TAA vaccines Malignant glioma 1 C o n c e n tra tio n (IF N ) 5 0 NG Her-2/neu in prostate cancer 2 Sarcoma NY-ESO-1 vaccine 3 Neoantigen vaccine 4-5 CD4 + Mutation NG Inhibitory Antigen Mutation 1 Yajima et al., 2005 Clin Cancer Res 2 Voutsas et al., 2016 JITC 3 Pollack et al., 2017 ASCO poster 4 Carreno et al., 2015 Science 8

9 ATLAS provides greater information than in silico tools Biology too complex for algorithms Algorithm predictions confirmed by ATLAS 3% Data challenge common assumptions ATLAS-identified antigens show no enrichment for key algorithm inputs Inhibitory antigens cannot be predicted Limited overlap between CD4 + and CD8 + neoantigens (12%) Inhibitory antigens mischaracterized by algorithms 12% Antigens identified by ATLAS but not algorithms 65% Shared neoantigens not found to date (20 patients analyzed) Summary data from 20 patients screened across seven different tumor types 1785 total mutations profiled, 870 epitopes predicted (49%) 1 Melssen and Slingluff, 2017 Curr Opin Immunol 9

10 ATLAS-identified antigens challenges assumptions: no correlations yet identified with common prioritization criteria Mutant DNA allele frequency Gene expression levels ATLAS class Neoantigen Inhibitory Non-responsive Binding predictions Detection of mutation in RNA Somatic mutation type 10

11 Neoantigen vaccines - ATLAS: Don t Guess. Know. T cell T cell antigen ATLAS T cells showing, not predicting, neoantigens: For any patient Of pre-existing responses for easier vaccination For CD8 + and CD4 + T cells for broader immune response Inhibitory antigens to be avoided Tumor Cell The right neoantigens for better vaccines 11

12 Novel Tumor-Associated (Common) Antigens Therapeutic Applications 12

13 ATLAS optimized as a rapid, industrialized platform for antigen discovery Neoantigen vaccine TAA/viral vaccine Dendritic Cell T cell 13

14 ATLAS identifies unexpected TAA profiles in patients candidates for vaccine formulation Three of 26 TAAs elicited responses in nearly all individuals with colorectal cancer (n=25) but not healthy donors (n=12) 14

15 Well known TAAs in clinical development were not frequent antigens compared to ATLAS-prioritized antigens Novel TAA Known TAA Novel TAA Known TAA 15

16 Novel Tumor-Associated Antigens Biomarkers of Response and Early Diagnostics 16

17 Mean # of TAA recognized M e a n N u m b e r o f T c e l l A n t i g e n s M e a n N u m b e r o f T c e l l A n t i g e n s Mean # of TAA recognized M e a n N u m b e r o f T c e l l A n t i g e n s ATLAS minimally-invasive, blood-based TAA immunoprofiling has the potential to improve cancer management outcomes T cells from checkpoint blockade patient responders recognize TAAs Non-responder patient s T cells do not T cells from patients with pre-cancerous and cancerous lesions recognize TAAs Healthy individuals T cells do not 6 CD8 + Novel antigen Non- Responder Responder e s p o n d e r N o n - r e s p o n d e r R e s p o n d e r 4 CD4 + 6 Healthy Pre- Canc. Canc. Healthy Known antigen Pre- Canc. Canc N o n - r e s p o n d e r Non- Responder R e s p o n d e r Responder 0 Healthy N o n - r e s p o n d e r Pre- Canc. R e s p o n d e r Canc. Healthy Pre- Canc. Canc. Response Biomarker: A test using the ATLAS TAA response signature could be used to improve patient selection for checkpoint blockade therapy Early Disease Diagnostic: A test using the ATLAS TAA response signature could be used to identify subclinical disease in patients at-risk for recurrence 17

18 Advantages of ATLAS-derived assays as a diagnostic tool 1 Blood Only 2 Comprehensive No tumor sample needed Test T cell responses to broad TAA library 3 Direct 4 Accurate MHC I/II Measures T cell activation (not the tumor) Identifies antigens that other tests may miss TCR T cell antigen 18

19 Genocea: advancing programs through the power of ATLAS Neoantigen Vaccines GEN-009 IND early 2018 Tumor Antigen Vaccines and Diagnostics TAA response profiling in multiple indications Virus-Associated Antigen Vaccines T cell response signature to EBV Target EBV-associated cancers 19

20 Acknowledgements Research and Clinical Teams at The patients and their families who provided samples for these studies Memorial Sloan Kettering Cancer Center Timothy Chan Jonathan Havel Matthew Hellman Vladimir Makarov Jedd Wolchok Mayo Clinic Khashayarsha Khazaie Abdulmohammad Pezeshi Dana-Farber Cancer Institute F. Stephen Hodi Funding: The Ludwig Trust (melanoma TAA study) Contact: 20