Gritstone Oncology Presents Data at AACR Demonstrating MHC Class II Neoantigen Prediction with EDGE™ Significantly Outperforms Current Prediction Methods
EDGE is an artificial intelligence platform that identifies tumor-specific neoantigens (TSNA) for the development of antigen-directed immunotherapies that may drive highly specific tumor cell destruction by T cells. TSNA can be presented by either MHC class I, which are recognized by CD8 T cells, or MHC class II, which are recognized by CD4 T cells. The public tools available to predict tumor-specific antigens presented by MHC class I are more advanced; historically, the characterization of MHC class II presented antigens has been challenging for the field due to greater variability in their binding properties.
“We believe, based in large part on the observed mechanism of action of checkpoint inhibitors such as anti-PD-(L)1 antibodies, that immunotherapies targeting tumor-specific neoantigens are potentially one of the most powerful approaches to achieve durable anti-cancer responses with minimal to no impact on healthy cells,” said
The MHC class II dataset for the AACR analyses was derived from 73 human tumor and cell-line samples, including non-small cell lung cancer, lymphoma, and ovarian cancer, and comprised over forty-five thousand tumor-presented peptides. Building on the progress with class I EDGE, Gritstone’s class II model overcame a key challenge with HLA class II prediction, which is the longer and more variable presented peptide lengths. Gritstone addressed this challenge with the new comprehensive training dataset and an innovative neural network architecture, leading to an approximately 20-fold increase in performance when using EDGE versus standard methods.
This dataset complements the previously reported EDGE data demonstrating that it is approximately nine-fold better than publicly available tools at predicting tumor-specific antigens presented by MHC class I. Gritstone’s groundbreaking data were published in Nature Biotechnology in
About Gritstone EDGETM (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
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 Annual Report on Form 10-K filed on
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Source: Gritstone Oncology, Inc