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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Saltz, J; Gupta, R; Hou, L; Kurc, T; Singh, P; Nguyen, V; Samaras, D; Shroyer, KR; Zhao, T; Batiste, R; Van Arnam, J; Cancer Genome Atlas Research Network, ; Shmulevich, I; Rao, AUK; Lazar, AJ; Sharma, A; Thorsson, V

Published Date

  • April 3, 2018

Published In

Volume / Issue

  • 23 / 1

Start / End Page

  • 181 - 193.e7

PubMed ID

  • 29617659

Pubmed Central ID

  • PMC5943714

Electronic International Standard Serial Number (EISSN)

  • 2211-1247

Digital Object Identifier (DOI)

  • 10.1016/j.celrep.2018.03.086


  • eng

Conference Location

  • United States