Genopathomic profiling identifies signatures for immunotherapy response of lung adenocarcinoma via confounder-aware representation learning
Jiajun Deng,
Jiancheng Yang,
Likun Hou,
Junqi Wu,
Yi He,
Mengmeng Zhao,
Bingbing Ni,
Donglai Wei,
Hanspeter Pfister,
Caicun Zhou,
Tao Jiang,
Yunlang She,
Chunyan Wu,
Chang Chen
Affiliations
Jiajun Deng
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China
Jiancheng Yang
Shanghai Jiao Tong University, Shanghai, P.R. China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, P.R. China; Dianei Technology, Shanghai, P.R. China
Likun Hou
Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China
Junqi Wu
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China
Yi He
Dianei Technology, Shanghai, P.R. China
Mengmeng Zhao
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China
Bingbing Ni
Shanghai Jiao Tong University, Shanghai, P.R. China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, P.R. China; Huawei Hisilicon, Shanghai, P.R. China
Donglai Wei
Harvard University, Cambridge, MA, USA
Hanspeter Pfister
Harvard University, Cambridge, MA, USA
Caicun Zhou
Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China
Tao Jiang
Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China
Yunlang She
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China; Corresponding author
Chunyan Wu
Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China; Corresponding author
Chang Chen
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China; The First Hospital of Lanzhou University, Gansu, P.R. China; The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu, P.R. China; Corresponding author
Summary: 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.
