文章基本信息

标题：Genopathomic profiling identifies signatures for immunotherapy response of lung adenocarcinoma via confounder-aware representation learning
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作者：Jiajun Deng ; Jiancheng Yang ; Likun Hou 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：11
页码：1-14
DOI：10.1016/j.isci.2022.105382
语种：English
出版社：Elsevier
摘要：SummaryImmunotherapy shows durable response but only in a subset of patients, and test for predictive biomarkers requires procedures in addition to routine workflow. We proposed a confounder-aware representation learning-based system, genopathomic biomarker for immunotherapy response (PITER), that uses only diagnosis-acquired hematoxylin-eosin (H&E)-stained pathological slides by leveraging histopathological and genetic characteristics to identify candidates for immunotherapy. PITER was generated and tested with three datasets containing 1944 slides of 1239 patients. PITER was found to be a useful biomarker to identify patients of lung adenocarcinoma with both favorable progression-free and overall survival in the immunotherapy cohort (p Graphical abstractDisplay OmittedHighlights•Genetic profile could be extracted by deep learning from digital histologic slides•Extracted pathomic signature could identify patients’ response to immunotherapy•The extracted pathomic signature was involved in an active immune microenvironmentImmunology; Cancer; Artificial intelligence.