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Activity Number: 40 - Statistical Methods for Microbiome and Tumor Data
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306802
Title: Predicting Cancer Immunotherapy Treatment Response with Neoantigen Burden
Author(s): Laura Zhou* and Fei Zou and Wei Sun
Companies: University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill and Fred Hutchinson Cancer Research Center
Keywords: Cancer; Immunotherapy; Neontigen; Mutation Burden; Multinomial Model

Cancer immunotherapy, treatment prompted by the immune system to identify and kill cancer cells, has reported phenomenal successes. However, durable responses are only observed in a subset of patients. Identifying patients who can benefit from immunotherapy is among the most pressing questions in precision medicine. Mutation burden, the total number of non-silent mutations, has been shown to be an informative predictor. Since not all somatic mutations will induce an immune response, some researchers have considered the burden of human leukocyte antigen class I protein (HLA-I) neoantigens, defined as the somatic mutations that can be presented on cell surface by the HLA-I. However, the human leukocyte antigen class II protein (HLA-II) also plays an important role in the immune response system. We developed a new definition for neoantigen burden using both HLA-I and HLA-II binding predictions. We compare and examine the prediction of patient specific drug response on existing datasets with and without the inclusion of this definition of neoantigen burden. Our definition of neoantigen burden helps predict the patient’s response, even after adjusting for the effect of mutation burden.

Authors who are presenting talks have a * after their name.

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