1299 Predicting CD8+ cell density and tumor-immune phenotypes in non-small-cell lung cancer (NSCLC) from standard H&E slides using deep learning (DL)

Abstract

Background Tumor infiltrated lymphocytes (TIL), namely CD8 + TILs play a major role in antitumor immunity and tumor cell eradication. High-density infiltration of CD8+ cells in the tumor, in contrast to CD8+ cell excluded regions, is associ-ated with improved prognosis and response to immunotherapy in multiple cancer types, however, CD8 evaluations require IHC staining, often not performed routinely in clinical practice. Here, we used DL to predict CD8+ cell density and immune phenotypes from standard H&E slides. Methods 188 pairs of H&E slide and a sequential CD8 stained slide from 103 patients with metastatic NSCLC were procured. DL models were trained to classify tumor cells, lymphocytes, fibroblasts, and tumor versus stromal areas on H&E, as well as positivity of CD8 per cell by IHC. 354 spatial features were calculated from the H&E slides and CD8+ density in the whole tumor region from IHC slides. A training (n=143) and test (n=45) cohort was created and linear regression modeling predicted CD8 density from H&E features. Two board certified pathologists classified IHC slides into immune phenotypes: inflamed, desert and excluded, based on CD8 density (table 1) and a multinomial logistic regression model was train to predicet these phenotypes from the H&E images.