Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

TLDR

Digitized H&E images of TCGA samples are underutilized beyond basic pathology. The study presents TIL maps derived from H&E images of 13 TCGA tumor types to showcase the resource. TIL maps were generated by a CNN that classifies image patches, and their structural patterns were grouped with standard histopathological parameters. Affinity propagation identified spatial TIL structures that correlate with overall survival, are enriched for specific T cell subpopulations, and vary across tumor types, immune subtypes, and molecular subtypes, linking spatial infiltrate patterns to TCGA genomic data.

Abstract

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.