Gritstone Oncology Announces Oral Presentation on MHC Class II Antigen Prediction at the AACR Annual Meeting in April 2019
--MHC Class II Antigen Prediction by Gritstone’s Proprietary Artificial Intelligence Platform EDGETM Shows
Significant Improvement Over Standard Prediction Tools --
--EDGE Enables Identification of Neoantigen Reactive T cells and T cell Receptors --
“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
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 |
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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 |
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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 |
About EDGE™ (Epitope Discovery in cancer GEnomes) Platform
The EDGE platform is designed to be a best-in-class machine-learning tool for the identification of tumor neoantigens presented on the surface of tumor cells. EDGE’s prediction model was initially trained using a large dataset of human tumor and normal tissue samples with paired class I HLA-presented peptide sequences, HLA types and transcriptome RNA sequencing. The training dataset for EDGE includes hundreds of tumor and normal tissue samples, yielding over one million peptides, from patients of various ancestries with diverse HLA types. EDGE leverages a novel integrated neural network model architecture to model key features that are essential for accurate prediction of true tumor-specific neoantigens. Data demonstrating the neoantigen identification capabilities of EDGE were published in Nature Biotechnology in
About
Gritstone Forward-Looking Statements
This press release contains forward-looking statements, including, but not limited to, statements related to the predictive capabilities of the EDGE Platform, its T cell and T cell receptor discovery program, and its investigational immunotherapies. Such forward-looking statements involve substantial risks and uncertainties that could cause Gritstone’s research and clinical development programs, future results, performance or achievements to differ significantly from those expressed or implied by the forward-looking statements. Such risks and uncertainties include, among others, the uncertainties inherent in the drug development process, including Gritstone’s programs’ early stage of development, the process of designing and conducting preclinical and clinical trials, the regulatory approval processes, the timing of regulatory filings, the challenges associated with manufacturing drug products, Gritstone’s ability to successfully establish, protect and defend its intellectual property and other matters that could affect the sufficiency of existing cash to fund operations. Gritstone undertakes no obligation to update or revise any forward-looking statements. For a further description of the risks and uncertainties that could cause actual results to differ from those expressed in these forward-looking statements, as well as risks relating to the business of the company in general, see Gritstone’s most recent Quarterly Report on Form 10-Q filed on
Contacts
Media:
1AB
(973) 271-6085
dan@1abmedia.com
Investors:
Wheelhouse Life Science Advisors
(510) 871-6161
asantos@wheelhouselsa.com
Source: Gritstone Oncology, Inc