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RESEARCH PRODUCT
Bioinformatic methods for cancer neoantigen prediction
Timothy O'donnellJulia KodyshSebastian BoegelJohn C. CastleAlex Rubinsteynsubject
0301 basic medicineintegumentary systembiologyComputer sciencemedicine.medical_treatmentTumor cellsImmunotherapyComputational biologyMajor histocompatibility complex03 medical and health sciences030104 developmental biology0302 clinical medicineImmune systemCancer immunotherapy030220 oncology & carcinogenesismedicinebiology.proteindescription
Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.
year | journal | country | edition | language |
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2019-01-01 |