Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types
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...
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BMJ Publishing Group
2024-02-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/12/2/e008339.full |
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| author | Jeanne Shen Sergio Pereira Chan-Young Ock Yung-Jue Bang Seulki Kim Sehhoon Park Se-Hoon Lee George A Fisher Young Kwang Chae Yoon-La Choi Jin-Haeng Chung Tony S K Mok Leeseul Kim Jun-Eul Hwang Gahee Park Sanghoon Song Seunghwan Shin Yoojoo Lim Wonkyung Jung Heon Song Hyojin Kim Taebum Lee Sukjun Kim Chang Ho Ahn Seokhwi Kim Ben W Dulken Stephanie Bogdan Maggie Huang Chiyoon Oum Siraj M. Ali |
| author_facet | Jeanne Shen Sergio Pereira Chan-Young Ock Yung-Jue Bang Seulki Kim Sehhoon Park Se-Hoon Lee George A Fisher Young Kwang Chae Yoon-La Choi Jin-Haeng Chung Tony S K Mok Leeseul Kim Jun-Eul Hwang Gahee Park Sanghoon Song Seunghwan Shin Yoojoo Lim Wonkyung Jung Heon Song Hyojin Kim Taebum Lee Sukjun Kim Chang Ho Ahn Seokhwi Kim Ben W Dulken Stephanie Bogdan Maggie Huang Chiyoon Oum Siraj M. Ali |
| author_sort | Jeanne Shen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-99015999269c482d8756bf12afdf05fc |
| institution | Kabale University |
| issn | 2051-1426 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-99015999269c482d8756bf12afdf05fc2025-02-09T19:15:18ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262024-02-0112210.1136/jitc-2023-008339Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer typesJeanne Shen0Sergio Pereira1Chan-Young Ock2Yung-Jue Bang3Seulki Kim4Sehhoon Park5Se-Hoon Lee6George A Fisher7Young Kwang Chae8Yoon-La Choi9Jin-Haeng Chung10Tony S K Mok11Leeseul Kim12Jun-Eul Hwang13Gahee Park14Sanghoon Song15Seunghwan Shin16Yoojoo Lim17Wonkyung Jung18Heon Song19Hyojin Kim20Taebum Lee21Sukjun Kim22Chang Ho Ahn23Seokhwi Kim24Ben W Dulken25Stephanie Bogdan26Maggie Huang27Chiyoon Oum28Siraj M. Ali292 Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, California, USA13 Lunit, Seoul, Korea (the Republic of)3Lunit, Seoul, Republic of Korea15 Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea (the Republic of)13 Lunit, Seoul, Korea (the Republic of)10 Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)3Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea9 Department of Medicine, Stanford University School of Medicine, Stanford, California, USA9Northwestern University, Chicago, IL, USA3 Department of Pathology and Translational Genomics, Sungkyunkwan University School of Medicine, Suwon, Korea (the Republic of)6 Department of Pathology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea (the Republic of)16 Department of Clinical Oncology, The Chinese University of Hong Kong, New Territories, Hong Kong8Ascension Saint Francis, Evanston, IL, USA11 Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Korea (the Republic of)13 Lunit, Seoul, Korea (the Republic of)13 Lunit, Seoul, Korea (the Republic of)13 Lunit, Seoul, Korea (the Republic of)13 Lunit, Seoul, Korea (the Republic of)Boston College Connell School of Nursing, Chestnut Hill, Massachusetts, USA13 Lunit, Seoul, Korea (the Republic of)GC Cell, Yongin-si, Gyeonggi-do, Republic of Korea5 Department of Pathology, Chonnam National University Medical School, Gwangju, Korea (the Republic of)13 Lunit, Seoul, Korea (the Republic of)13 Lunit, Seoul, Korea (the Republic of)14 Department of Pathology, Ajou University School of Medicine, Suwon, Korea (the Republic of)1 Department of Pathology, Stanford University School of Medicine, Stanford, California, USA2 Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, California, USA8 UCLA Health, University of California, Los Angeles, Los Angeles, California, USA13 Lunit, Seoul, Korea (the Republic of)13 Lunit, Seoul, Korea (the Republic of)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.https://jitc.bmj.com/content/12/2/e008339.full |
| spellingShingle | Jeanne Shen Sergio Pereira Chan-Young Ock Yung-Jue Bang Seulki Kim Sehhoon Park Se-Hoon Lee George A Fisher Young Kwang Chae Yoon-La Choi Jin-Haeng Chung Tony S K Mok Leeseul Kim Jun-Eul Hwang Gahee Park Sanghoon Song Seunghwan Shin Yoojoo Lim Wonkyung Jung Heon Song Hyojin Kim Taebum Lee Sukjun Kim Chang Ho Ahn Seokhwi Kim Ben W Dulken Stephanie Bogdan Maggie Huang Chiyoon Oum Siraj M. Ali Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types Journal for ImmunoTherapy of Cancer |
| title | Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types |
| title_full | Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types |
| title_fullStr | Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types |
| title_full_unstemmed | Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types |
| title_short | Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types |
| title_sort | inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types |
| url | https://jitc.bmj.com/content/12/2/e008339.full |
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