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Artificial Intelligence–Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non–Small-Cell Lung Cancer

Sehhoon Park et al. — Journal of Clinical Oncology (2022)



Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI).


We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC).


Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists (P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP.


The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

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Sehhoon Park , MD, PhD1; Chan-Young Ock , MD, PhD2; Hyojin Kim , MD, PhD3; Sergio Pereira, PhD2; Seonwook Park , PhD2; Minuk Ma , MS2; Sangjoon Choi , MD4; Seokhwi Kim , MD, PhD5; Seunghwan Shin , MD2; Brian Jaehong Aum , PhD2; Kyunghyun Paeng, MS2; Donggeun Yoo, PhD2; Hongui Cha , PhD1; Sunyoung Park, PhD1; Koung Jin Suh, MD6; Hyun Ae Jung , MD, PhD1; Se Hyun Kim , MD, PhD6; Yu Jung Kim , MD, PhD6; Jong-Mu Sun , MD, PhD1; Jin-Haeng Chung , MD, PhD3; Jin Seok Ahn, MD, PhD1; Myung-Ju Ahn , MD, PhD1; Jong Seok Lee, MD, PhD6; Keunchil Park , MD, PhD1; Sang Yong Song, MD, PhD4; Yung-Jue Bang , MD, PhD7; Yoon-La Choi , MD, PhD4; Tony S. Mok , MD8; and Se-Hoon Lee , MD, PhD1,9

1Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea

2Lunit, Seoul, Republic of Korea

3Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

4Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

5Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea

6Division of Hematology-Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

7Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea

8State Key Laboratory of Translational Oncology, Department of Clinical Oncology, Chinese University of Hong Kong, Hong Kong, China

9Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea

Journal of Clinical Oncology (2022)

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