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

Saltz J, Gupta R, Hou L, Kurc T, Singh P, Vu Nguyen , Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao AUK, Lazar AJ, Sharma A, Thorsson V (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Cell Reports Elsevier (Cell Press)

Book Volume: 23

Pages Range: 181-193.e7

Journal Issue: 1

DOI: 10.1016/j.celrep.2018.03.086

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.

Involved external institutions

Stony Brook Medicine US United States (USA) (US) State University of New York at Albany (UNY Albany / UAlbany) US United States (USA) (US) University of Texas MD Anderson Cancer Center US United States (USA) (US) University of Pennsylvania US United States (USA) (US) Institute for Systems Biology US United States (USA) (US) Emory University US United States (USA) (US)

How to cite

APA:

Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Vu Nguyen, .,... Thorsson, V. (2018). Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports, 23(1), 181-193.e7. https://doi.org/10.1016/j.celrep.2018.03.086

MLA:

Saltz, Joel, et al. "Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images." Cell Reports 23.1 (2018): 181-193.e7.

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