Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.

Authors

null

Jeanne Shen

Department of Pathology, Stanford University School of Medicine, Stanford, CA

Jeanne Shen , Taebum Lee , Jun-Eul Hwang , Yoon-La Choi , Se-Hoon Lee , Hyojin Kim , Jin-haeng Chung , Stephanie Bogdan , Maggie Huang , Tyler Raclin , George A. Fisher Jr., Sergio Pereira , Seonwook Park , Minuk Ma , Donggeun Yoo , Seunghwan Shin , Kyunghyun Paeng , Chan-Young Ock , Tony S. K. Mok , Yung-Jue Bang

Organizations

Department of Pathology, Stanford University School of Medicine, Stanford, CA, Department of Pathology, Chonnam National University Hospital, Gwangju, South Korea, Division of Hematology-Oncology, Department of Internal Medicine, Chonnam National University Medical School, Gwangju, South Korea, Department of Pathology and Translational Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea, Division of Hematology and Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea, Department of Pathology, Seoul National University Bundang Hospital, Seongnam, South Korea, Center for Artificial Intelligence in Medicine & Imaging, Stanford University School of Medicine, Stanford, CA, UC Davis Health, Davis, CA, Stanford University School of Medicine, Stanford, CA, Department of Medicine, Stanford University, Stanford, CA, Lunit Inc., Seoul, South Korea, State Key Laboratory of Translational Oncology, Chinese University of Hong Kong, Hong Kong, China, Seoul National University College of Medicine, Seoul, South Korea

Research Funding

Other
Lunit Inc

Background: Tumor infiltrating lymphocytes (TIL) are a potential tumor-agnostic biomarker for immune checkpoint inhibitor (ICI) therapy. We previously reported the clinical application of an artificial intelligence-powered spatial TIL analyzer, Lunit SCOPE IO, for predicting ICI treatment outcomes in advanced non-small cell lung cancer (NSCLC). Here, we expand the clinical application of Lunit SCOPE IO as a tumor-agnostic ICI biomarker across multiple cancer types. Methods: Lunit SCOPE IO was trained and validated with a 2.8 x 109 micrometer2 area and 5.9 x 106 TILs from 3,166 H&E Whole-Slide Images (WSI) of multiple cancer types, annotated by 52 board-certified pathologists. The Inflamed Score (IS) was defined as the proportion of all tumor-containing 1 mm2-size tiles within a WSI classified as being of the inflamed immune phenotype (high TIL density within cancer epithelium). We first evaluated the correlation between the IS and TMB, MSI-H, and immune cytolytic activity (GZMA and PRF1) across 22 cancer types from The Cancer Genome Atlas (TCGA, n = 7,467). Subsequently, the correlation between the IS and overall survival after ICI treatment was evaluated in a real-world dataset of patients with 9 different tumor types (n = 1,013), retrospectively collected from Stanford University Medical Center, Chonnam National University Hospital, Samsung Medical Center, and Seoul National University Bundang Hospital. Results: Lunit SCOPE IO accurately detected CE, CS, and TILs with an area under the receiver-operating-characteristic curve of 0.970, 0.949, and 0.925, respectively. In the TCGA pan-cancer cohort, Lunit SCOPE IO’s IS correlated significantly with immune cytolytic activity (Spearman rho = 0.504, p< 0.001), TMB-high (≥ 10 mutations/Mb, fold change 1.39, p< 0.001) and MSI-H (fold change 1.45, p< 0.001). The IS-positive proportions of microsatellite-stable (MSS) and TMB-low cases were 42.5% and 17.1%, using the thresholds of IS ≥ 20% and ≥ 50% as presumptive clinical cutoffs. In the real-world ICI clinical dataset (n = 1,013), an IS ≥ 20% correlated significantly with favorable overall survival after ICI treatment (cancer type-adjusted hazard ratio [HR] 0.70, 95% confidence interval [CI] 0.59-0.83, p< 0.0001). Furthermore, this association remained significant after the exclusion of NSCLC patients (n = 519) (adjusted HR 0.68, 95% CI 0.53-0.86, p = 0.0016) indicating that the effect was not driven solely by one major tumor type. Conclusions: The Inflamed Score (IS), as evaluated by Lunit SCOPE IO, correlates with favorable overall survival after ICI treatment across multiple tumor types. AI-powered spatial TIL analysis of the tumor microenvironment may be able to detect a significant proportion of ICI responders, and offers promise as a new companion diagnostic, particularly in patients with MSS/TMB-low tumors.

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Abstract Details

Meeting

2021 ASCO Annual Meeting

Session Type

Poster Session

Session Title

Developmental Therapeutics—Immunotherapy

Track

Developmental Therapeutics—Immunotherapy

Sub Track

Tissue-Based Biomarkers

Citation

J Clin Oncol 39, 2021 (suppl 15; abstr 2607)

DOI

10.1200/JCO.2021.39.15_suppl.2607

Abstract #

2607

Poster Bd #

Online Only

Abstract Disclosures