Gritstone Presents on MHC Class II Antigen Prediction at AACR

–MHC Class II Antigen Prediction by Gritstone’s Proprietary Artificial Intelligence Platform EDGE^TM Shows
Significant Improvement Over Standard Prediction Tools —

            — EDGE Enables Identification of Neoantigen Reactive T cells and T cell Receptors —

Gritstone Oncology, Inc. (Nasdaq: GRTS), a clinical-stage biotechnology company developing the next generation of cancer immunotherapies to fight multiple cancer types, today announced two abstracts have been accepted for presentation at the upcoming American Association for Cancer Research (AACR) Annual Meeting 2019 in Atlanta, Georgia.

“In order to drive an effective anti-cancer immune response, T cells must recognize tumor-specific antigens (peptides) presented by either class I or class II major histocompatibility complex (MHC) molecules,” said Andrew Allen, M.D., Ph.D., co-founder, president and chief executive officer of Gritstone Oncology. “Our artificial intelligence platform for neoantigen identification, EDGE, is designed to be a best-in-class tool for identifying these tumor antigens for use in immunotherapies. We have already demonstrated that EDGE is approximately nine-fold better than publicly available tools at predicting class I MHC-presented tumor-specific antigens. At AACR, we look forward to presenting data showing the significant progress we have made in predicting MHC class II-presented tumor-specific antigens, which has historically been a challenge for the field. Identification of MHC class II antigens expands our repertoire of tumor-specific targets and may increase the potency of our neoantigen-based therapies.”

Gritstone’s work on class II antigen prediction will be presented at AACR in an oral session. Additionally, Gritstone has leveraged its capabilities in neoantigen identification to efficiently identify neoantigen reactive T cells and T cell receptors, which have potential applications in cell therapy. As the field of engineered T cell therapies begins to evaluate solid tumor targets, accurate prediction of neoantigens makes the process of identifying relevant T cell receptors and T cells much more efficient – a potential key benefit of utilizing a powerful prediction tool such as EDGE. These data will be presented in a poster session.

Oral Presentation
Title:			MHC class II antigen identification for cancer immunotherapy by deep learning on tumor HLA peptides
Session Date and Time:			Tuesday, April 2, 2019 3:00 p.m. – 5:00 p.m. EST
Poster Presentation
Title:			Identification of pre-existing neoantigen-specific T cells in patients receiving checkpoint inhibitor therapy using a deep learning antigen prediction model
Session Date and Time:			Tuesday, April 2, 2019 1:00 p.m. – 5:00 p.m. EST

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