연구성과 홍보

J Immunother Cancer. 2025 Oct 31;13(10):e012374.

Title : Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor

Authors : Yeong Hak Bang1,2,3, Geun-Ho Park1,4, Jin Woo Oh5, Soohyun Hwang5,6, Jun-Gi Jeong1,2, Boram Lee6, Cheol Yong Joe1,4, Hyemin Kim1, Jinyong Kim1, Sehhoon Park1,4, Hyun Ae Jung1, Jong-Mu Sun1, Jin Seok Ahn1, Myung-Ju Ahn1, Yoon-La Choi2,4,6,7, Chang Ho Ahn5, Siraj M Ali5, Chan-Young Ock5, Se-Hoon Lee8,2,4*

Affiliations :

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

2Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.

3Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

4Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.

5Lunit Inc, Seoul, Republic of Korea.

6Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

7Department of Clinical Research and Evaluation, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea.

8Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

DOI: 10.1136/jitc-2025-012374.

Abstract :

Purpose: This study evaluated the dynamic changes in the tumor microenvironment (TME) in patients with non-small cell lung cancer (NSCLC) and acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) using an artificial intelligence (AI)-powered spatial TME analyzer. We then assessed the predictive efficacy of immune-checkpoint inhibitors (ICIs)-based treatment.

Experimental design: An AI-powered whole-slide image analyzer was used to segment cancer areas (CAs) and cancer stroma and to identify tumor-infiltrating lymphocytes (TILs), tertiary lymphoid structures, fibroblasts, and endothelial cells (ECs) in the tumor tissue. We analyzed 143 NSCLC samples after resistance to EGFR-TKIs from two cohorts: (1) 89 patients treated with ICI monotherapy and (2) 54 patients from the ATTLAS phase III trial comparing atezolizumab plus bevacizumab, paclitaxel, and carboplatin (ABCP) versus pemetrexed plus carboplatin.

Results: Post-TKI samples showed reduced TILs in the CA (p=0.045) and increased ECs in the CA (p=0.005) compared with pre-TKI samples. These changes differed according to EGFR mutation subtype. Higher TILs in CA were associated with a better overall response rate (ORR) and progression-free survival (PFS). Similarly, higher EC levels in CA correlated with improved ORR and PFS. In the ATTLAS cohort, these factors were associated with clinical benefits from ABCP, with a significant association with TILs and a marginal association with ECs.

Conclusion: Our findings suggest that EGFR-TKIs affect the immune landscape of patients with EGFR-mutated NSCLC. Higher TILs or ECs in the CA were significantly associated with a favorable response to subsequent ICI-based treatment.