Abstract
Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database (TSNAdb v1.0), based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen (HLA) allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas (TCGA) and The Cancer Immunome Atlas (TCIA). We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients, which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy. TSNAdb is freely available at http://biopharm.zju.edu.cn/tsnadb.
Highlights
Cancer somatic mutations and viral oncogenes can generate tumor-specific protein sequences that are entirely absent from normal human cells
We developed a comprehensive database named TSNAdb for tumor-specific neoantigens based on 7748 tumor samples of 16 tumor types from The Cancer Genome Atlas (TCGA)
This database provides detailed affinity information between mutant/wild-type peptides and human leukocyte antigen (HLA) alleles, and the frequencies of neoantigens shared by tumor samples of each tumor type and pancancer
Summary
Cancer somatic mutations and viral oncogenes can generate tumor-specific protein sequences that are entirely absent from normal human cells. We collected somatic mutations and HLA alleles of 7748 tumor samples across 16 tumor types from TCGA (Release7.0, https://portal.gdc.cancer.gov) and TCIA (https://tcia.at/ home), respectively. We selected the top 100 HLA alleles (frequency >0.5%) of 7748 tumor samples and combined them with the recurrent missense mutations to predict potential shared neoantigens. We extracted 13,459 recurrent missense mutations from 9155 samples derived from the International Cancer Genome Consortium (ICGC) (Release, https://icgc.org/) and 16 highly frequent HLA alleles (frequencies >5%) from the 1000 Genome Project [23] for the prediction of potential shared neoantigens. We took the information on somatic mutations and HLA alleles of each tumor sample and employed NetMHCpan v2.8 [13] and NetMHCpan v4.0 [22] for neoantigen prediction, using the filtering tools embedded in our previously-developed software TSNAD [10]. We exemplify the usage of TSNAdb with the results predicted by NetMHCpan v2.8
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