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

iScience (Nov 2022)

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

Journal volume & issue: Vol. 25, no. 11
p. 105382

Abstract

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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.

Published in iScience

ISSN: 2589-0042 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science
Website: http://www.cell.com/iscience/home

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