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

doi:10.1016/j.isci.2022.105382

Genopathomic profiling identifies signatures for immunotherapy response of lung adenocarcinoma via confounder-aware representation learning

iScience. 2022 Oct 17;25(11):105382. doi: 10.1016/j.isci.2022.105382. eCollection 2022 Nov 18.

Authors

Jiajun Deng¹, Jiancheng Yang^{2

3

4}, Likun Hou⁵, Junqi Wu¹, Yi He⁴, Mengmeng Zhao¹, Bingbing Ni^{2

3

6}, Donglai Wei⁷, Hanspeter Pfister⁷, Caicun Zhou⁸, Tao Jiang⁸, Yunlang She¹, Chunyan Wu⁵, Chang Chen^{1

9

10}

Affiliations

¹ Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China.
² Shanghai Jiao Tong University, Shanghai, P.R. China.
³ MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, P.R. China.
⁴ Dianei Technology, Shanghai, P.R. China.
⁵ Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China.
⁶ Huawei Hisilicon, Shanghai, P.R. China.
⁷ Harvard University, Cambridge, MA, USA.
⁸ Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P.R. China.
⁹ The First Hospital of Lanzhou University, Gansu, P.R. China.
¹⁰ The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu, P.R. China.

Abstract

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.

Keywords: Artificial intelligence; Cancer; Immunology.