한빛사논문
- 조회319
Jeanne Shen1,2, Yoon-La Choi3,4, Taebum Lee5, Hyojin Kim6, Young Kwang Chae7, Ben W Dulken1, Stephanie Bogdan2, Maggie Huang8, George A Fisher9, Sehhoon Park10, Se-Hoon Lee10, Jun-Eul Hwang11, Jin-Haeng Chung6, Leeseul Kim12, Heon Song13, Sergio Pereira13, Seunghwan Shin13, Yoojoo Lim13, Chang Ho Ahn13, Seulki Kim13, Chiyoon Oum13, Sukjun Kim13, Gahee Park13, Sanghoon Song13, Wonkyung Jung13, Seokhwi Kim14, Yung-Jue Bang15, Tony S K Mok16, Siraj M. Ali13 and Chan-Young Ock13
1Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
2Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, California, USA
3Department of Pathology and Translational Genomics, Sungkyunkwan University School of Medicine, Suwon, Korea (the Republic of)
4Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea (the Republic of)
5Department of Pathology, Chonnam National University Medical School, Gwangju, Korea (the Republic of)
6Department of Pathology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea (the Republic of)
7Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
8UCLA Health, University of California, Los Angeles, Los Angeles, California, USA
9Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
10Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
11Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Korea (the Republic of)
12AMITA Health Saint Francis Hospital Evanston, Evanston, Illinois, USA
13Lunit, Seoul, Korea (the Republic of)
14Department of Pathology, Ajou University School of Medicine, Suwon, Korea (the Republic of)
15Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
16Department of Clinical Oncology, The Chinese University of Hong Kong, New Territories, Hong Kong
JS, Y-LC, TL, HK and YKC contributed equally.
Correspondence to Dr Jeanne Shen; Dr Chan-Young Ock
Abstract
Background: The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types.
Methods: Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions.
Results: We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup.
Conclusion: The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.
논문정보
- Research article
- 2024년 02월 (BRIC 등록일 2024-02-19)
- 국내(교신)+국외 연구진
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