neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival

BMC Bioinformatics

Wei Zhang¹  Hao Wen²  Jing Li²  Lu Wang²  Haoyu Wang²  Jing Zhang²  Ting Sun²  Lin Li²  Yufei He²  Zixuan Xiao²  Yong Liu²  Wendong Li²  Yifan Chen²  Guang Liu²  Yubo Fan²  Xiaohan Han² 

[1] Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, 100070, Beijing, People’s Republic of China;Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring Road West, Fengtai District, 100070, Beijing, People’s Republic of China;Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine, School of Biological Science and Medical Engineering, Beihang University, No.37 Xueyuan Road, Haidian District, 100083, Beijing, People’s Republic of China;

关键词: IDH wild-type glioblastoma;    Peptide-features;    Prognosis;    Deep learning;    Immunology;   

DOI  :  10.1186/s12859-021-04301-6

来源: Springer

【 摘 要 】

BackgroundNeoantigen based personalized immune therapies achieve promising results in melanoma and lung cancer, but few neoantigen based models perform well in IDH wild-type GBM, and the association between neoantigen intrinsic features and prognosis remain unclear in IDH wild-type GBM. We presented a novel neoantigen intrinsic feature-based deep learning model (neoDL) to stratify IDH wild-type GBMs into subgroups with different survivals.ResultsWe first derived intrinsic features for each neoantigen associated with survival, followed by applying neoDL in TCGA data cohort(AUC = 0.988, p value < 0.0001). 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.ConclusionsThe 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.

【 授权许可】

CC BY