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

Publication ,  Journal Article
Saltz, J; Gupta, R; Hou, L; Kurc, T; Singh, P; Nguyen, V; Samaras, D; Shroyer, KR; Zhao, T; Batiste, R; Van Arnam, J; Shmulevich, I; Rao, AUK ...
Published in: Cell Rep
April 3, 2018

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.

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Published In

Cell Rep

DOI

EISSN

2211-1247

Publication Date

April 3, 2018

Volume

23

Issue

1

Start / End Page

181 / 193.e7

Location

United States

Related Subject Headings

  • Neoplasms
  • Lymphocytes, Tumor-Infiltrating
  • Image Interpretation, Computer-Assisted
  • Humans
  • Deep Learning
  • 31 Biological sciences
  • 1116 Medical Physiology
  • 0601 Biochemistry and Cell Biology
 

Citation

APA
Chicago
ICMJE
MLA
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Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., … Thorsson, V. (2018). Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep, 23(1), 181-193.e7. https://doi.org/10.1016/j.celrep.2018.03.086
Saltz, Joel, Rajarsi Gupta, Le Hou, Tahsin Kurc, Pankaj Singh, Vu Nguyen, Dimitris Samaras, et al. “Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.Cell Rep 23, no. 1 (April 3, 2018): 181-193.e7. https://doi.org/10.1016/j.celrep.2018.03.086.
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep. 2018 Apr 3;23(1):181-193.e7.
Saltz, Joel, et al. “Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.Cell Rep, vol. 23, no. 1, Apr. 2018, pp. 181-193.e7. Pubmed, doi:10.1016/j.celrep.2018.03.086.
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. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep. 2018 Apr 3;23(1):181-193.e7.

Published In

Cell Rep

DOI

EISSN

2211-1247

Publication Date

April 3, 2018

Volume

23

Issue

1

Start / End Page

181 / 193.e7

Location

United States

Related Subject Headings

  • Neoplasms
  • Lymphocytes, Tumor-Infiltrating
  • Image Interpretation, Computer-Assisted
  • Humans
  • Deep Learning
  • 31 Biological sciences
  • 1116 Medical Physiology
  • 0601 Biochemistry and Cell Biology