Spatial Organization And Molecular Correlation Of Tumor-Infiltrating Lymphocytes Using Deep Learning On Pathology Images

Cell Reports. Volume 23 Issue 1: p181-193.e7, 3 April 2018 10.1016/j.celrep.2018.03.086

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.

Data in the GDC

• GDC Manifests
  • Open-Access Data - Download Manifest (5 Files)

Supplemental Data

• Data Files
  • Manuscript Table S1 - TILMap_TableS1.xlsx
  • Manuscript Table S2 - TILMap_TableS2.xlsx
  • Affinity Propagation Cluster Summary Values - TILmap_Summary_20171013.tsv
  • Adjusted Cluster Indices - Adjusted_indices_October_23_2017.tsv
  • Global Structural Patterns - TIL-Pattern-Labels.csv

Additional Resources

• The Cancer Imaging Archive (link is external)
• ISB Cancer Genomics Cloud (link is external) Institute for Systems Biology
• PanCanAtlas Additional Files