AUTHORS
Jeanne Shen, Yoon-La Choi, Taebum Lee, Hyojin Kim, Young Kwang Chae, Ben W Dulken, Stephanie Bogdan, Maggie Huang, George A Fisher, Sehhoon Park, Se-Hoon Lee, Jun-Eul Hwang, Jin-Haeng Chung, Leeseul Kim, Heon Song, Sergio Pereira, Seunghwan Shin, Yoojoo Lim, Chang Ho Ahn, Seulki Kim, Chiyoon Oum, Sukjun Kim, Gahee Park, Sanghoon Song, Wonkyung Jung, Seokhwi Kim, Yung-Jue Bang, Tony S K Mok, Siraj M. Ali, Chan-Young Ock
Department of Pathology, Stanford University School of Medicine, Stanford, California, USA; Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, California, USA; Department of Pathology and Translational Genomics, Sungkyunkwan University School of Medicine, Suwon, Korea (the Republic of); Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea (the Republic of); Department of Pathology, Chonnam National University Medical School, Gwangju, Korea (the Republic of); Department of Pathology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea (the Republic of); Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; UCLA Health, University of California, Los Angeles, Los Angeles, California, USA; Department of Medicine, Stanford University School of Medicine, Stanford, California, USA; Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of); Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Korea (the Republic of); AMITA Health Saint Francis Hospital Evanston, Evanston, Illinois, USA; Lunit, Seoul, Korea (the Republic of); Department of Pathology, Ajou University School of Medicine, Suwon, Korea (the Republic of); Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea (the Republic of); Department of Clinical Oncology, The Chinese University of Hong Kong, New Territories, Hong Kong
PUBLISHED
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.
Conclusions
The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.