In iScience
Immunotherapy 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 < 0.05). PITER was significantly associated with pathways involved in active cell division and a more immune activating microenvironment, which indicated the biological basis in identifying patients with favorable outcome of immunotherapy. Thus, PITER may be a potential biomarker to identify patients of lung adenocarcinoma with a good response to immunotherapy, and potentially provide precise treatment.
Deng Jiajun, Yang Jiancheng, Hou Likun, Wu Junqi, He Yi, Zhao Mengmeng, Ni Bingbing, Wei Donglai, Pfister Hanspeter, Zhou Caicun, Jiang Tao, She Yunlang, Wu Chunyan, Chen Chang
2022-Nov-18
Artificial intelligence, Cancer, Immunology
