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We presented a novel neoantigen intrinsic feature-based deep learning model (neoDL) to stratify IDH wild-type GBMs into subgroups with different survivals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We first derived intrinsic features for each neoantigen associated with survival, followed by applying neoDL in TCGA data cohort(AUC\u2009=\u20090.988,\n                      <jats:italic>p<\/jats:italic>\n                      value\u2009&lt;\u20090.0001). Leave one out cross validation (LOOCV) in TCGA demonstrated that neoDL successfully classified IDH wild-type GBMs into different prognostic subgroups, which was further validated in an independent data cohort from Asian population. Long-term survival IDH wild-type GBMs identified by neoDL were found characterized by 12 protective neoantigen intrinsic features and enriched in development and cell cycle.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The model can be therapeutically exploited to identify IDH wild-type GBM with good prognosis who will most likely benefit from neoantigen based personalized immunetherapy. Furthermore, the prognostic intrinsic features of the neoantigens inferred from this study can be used for identifying neoantigens with high potentials of immunogenicity.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-021-04301-6","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T01:02:41Z","timestamp":1627002161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival"],"prefix":"10.1186","volume":"22","author":[{"given":"Ting","family":"Sun","sequence":"first","affiliation":[]},{"given":"Yufei","family":"He","sequence":"additional","affiliation":[]},{"given":"Wendong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zixuan","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Xiaohan","family":"Han","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haoyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yubo","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"issue":"2","key":"4301_CR1","doi-asserted-by":"publisher","first-page":"174","DOI":"10.3390\/cancers11020174","volume":"11","author":"D Fabian","year":"2019","unstructured":"Fabian D, Guillermo Prieto Eibl MDP, Alnahhas I, Sebastian N, Giglio P, Puduvalli V, Gonzalez J, Palmer JD. 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