Computational Prediction of Tumor Neoantigen for Precision Oncology

Advances in immune checkpoint blockade have elicited adaptive immune responses with promising clinical responses to treatments against human malignancies. Emerging data suggest that recognition of patient-specific mutation-associated cancer antigens may allow scientists to dissect the immune response in the activity of clinical immunotherapies. On the other hand, studies indicate that more than 90% of human genes are alternatively spliced. The advent of high-throughput sequencing technology has provided a comprehensive view of both splicing aberrations and somatic mutations across a range of human malignancies. We introduced a computational method that works on both short-read and long-read sequencing data, which allows us to significantly improve the detection of cancer antigens resulting from alternative splicing variants, insertions, deletions and point mutations. Subsequent analysis of these cancer antigen candidates with widely used tools such as netMHC allows for the accurate in silico prediction of neoantigens. These altered peptide sequences may elicit immune responses such as T-cell recognition and tumor cell clearance if they are properly presented by the immune system and have a far-reaching impact on the prediction of clinical benefits to immunotherapy.