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

Abstract

2607 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.

Artificial intelligence‐powered spatial analysis of tumor‐infiltrating lymphocytes as a predictive biomarker for axitinib in adenoid cystic carcinoma

“…12,13 It has been reported that tumor-infiltrating lymphocytes (TIL) evaluated by Lunit SCOPE IO, an artificial intelligence (AI)-powered analyzer of TIL, can predict prognosis according to the use of immune checkpoint inhibitors in multiple cancer types. [14][15][16] Given that antiangiogenic agents have an immunomodulatory ability, 17,18 we hypothesized that TIL might be a good predictive biomarker of axitinib. We conducted this study to analyze the predictive value of AI-powered TIL analysis in patients with R/M ACC treated with axitinib.…”

“…To date, several studies have been conducted on the predictive genomic markers of TKI, but the results are inconsistent 12,13 . It has been reported that tumor‐infiltrating lymphocytes (TIL) evaluated by Lunit SCOPE IO, an artificial intelligence (AI)‐powered analyzer of TIL, can predict prognosis according to the use of immune checkpoint inhibitors in multiple cancer types 14–16 . Given that antiangiogenic agents have an immunomodulatory ability, 17,18 we hypothesized that TIL might be a good predictive biomarker of axitinib.…”