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- W3217248064 endingPage "100052" @default.
- W3217248064 startingPage "100052" @default.
- W3217248064 abstract "Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them." @default.
- W3217248064 created "2021-12-06" @default.
- W3217248064 creator A5066001126 @default.
- W3217248064 creator A5066477976 @default.
- W3217248064 creator A5084751312 @default.
- W3217248064 date "2021-12-01" @default.
- W3217248064 modified "2023-09-30" @default.
- W3217248064 title "Computational cancer neoantigen prediction: current status and recent advances" @default.
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