文章基本信息

标题：Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients
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作者：Jie Ju ; Leonoor V. Wismans ; Dana A.M. Mustafa 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：12
页码：1-19
DOI：10.1016/j.isci.2021.103415
语种：English
出版社：Elsevier
摘要：SummaryA major challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is the unpredictability of their prognoses due to high heterogeneity. We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify PDAC subtypes and to predict prognoses of new patients. MODEL-P was trained on autoencoder integrated multi-omics of 146 patients with PDAC together with their survival outcome. Using MODEL-P, we identified two PDAC subtypes with distinct survival outcomes (median survival 10.1 and 22.7 months, respectively, log rank p = 1 × 10−6), which correspond to DNA damage repair and immune response. We rigorously validated MODEL-P by stratifying patients in five independent datasets into these two survival groups and achieved significant survival difference, which is superior to current practice and other subtyping schemas. We believe the subtype-specific signatures would facilitate PDAC pathogenesis discovery, and MODEL-P can provide clinicians the prognoses information in the treatment decision-making to better gauge the benefits versus the risks.Graphical abstractDisplay OmittedHighlights•We developed DL-based MODEL-P to identify prognosis-correlated PDAC subtypes•The identified subtypes related to DNA damage repair and immune response processes•MODEL-P stratified patients from independent datasets into distinct survival groups•MODEL-P could be used in clinics to aid treatment decision-makingBiocomputational method; Cancer systems biology; Cancer