Abstract LB384: Artificial intelligence measures of tumor infiltrating lymphocytes predict colorectal cancer-specific and overall survival

Abstract

Abstract Tumor infiltrating lymphocytes (TILs) per high-power field (HPF) as measured by expert pathologists is a useful, independent, validated prognostic marker in colorectal cancer (CRC). Artificial intelligence (AI) techniques of deep learning (DL) can predict TILs directly from routinely collected, digitized hematoxylin and eosin (H&E) pathology slides, but AI-predicted TILs have not yet been thoroughly validated as clinically relevant biomarker. Here we test whether AI-predicted TILs per HPF can be validated as an independent prognostic biomarker of CRC-specific survival in patients. We constructed an AI model to predict TILS per HPF using a weakly-supervised, transformer-based deep regression model, trained using 5-fold cross validation in a large dataset (n=1,738) and validated in a second independent dataset (n=223) of CRC cases. The model predicted TILs per HPF as a continuous variable, which was carried forward for survival analyses both as a continuous variable and dichotomized at a previously established a priori threshold of <2 vs. ≥2 TILs per HPF. Using longitudinal data from the population-based Molecular Epidemiology of Colorectal Cancer Study (median follow-up = 95 months), survival analyses were performed using non-parametric and Cox-proportional hazards models, with and without adjustment for age, sex, ethnicity, stage, and molecularly measured microsatellite instability (MSI). Two or more AI-predicted TILs per HPF were significantly associated with 5-year CRC-specific survival (p=0.0000037), 5-year overall survival (p=0.00021), and overall survival (p=0.000067). In a Cox proportional hazards model adjusting for age, sex, ethnicity, stage, and MSI, ≥2 AI-predicted TILs per HPF was significantly associated with improved 5-year CRC-specific survival, with a hazard ratio (HR) = 0.62, (95% confidence interval; 0.43, 0.91), p=0.01. Our new AI-driven deep learning model, which we call HopeSTIL, provides a highly efficient algorithm for analyzing digital pathology images of H&E sections of CRC to quantify TILs per HPF. HopeSTIL is a validated prognostic marker for 5-year CRC-specific survival that does not require a pathologist to manually count and score TILs per HPF. Citation Format: Stephen B. Gruber, Omar S. El Nahhas, Joseph D. Bonner, Joel K. Greenson, Daniel Schmolze, Lawrence Shaktah, Jonathan Salazar, Lorena Reynaga, Sidney Lindsey, Jenny Lu, Allen Mao, Victor Moreno, Stephani L. Schmit, Ya-Yu Tsai, Stanley R. Hamilton, Gad Rennert, Jakob N. Kather. Artificial intelligence measures of tumor infiltrating lymphocytes predict colorectal cancer-specific and overall survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB384.