Saltz, Joel; Gupta, Rajarsi; Hou, Le; Kurc, Tahsin; Singh, Pankaj; Nguyen, Vu; Samaras, Dimitris; Shroyer, Kenneth R; Zhao, Tianhao; Batiste, Rebecca; Van Arnam, John (2018). Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell reports, 23(1), 181-193.e7. Cell Press 10.1016/j.celrep.2018.03.086
![[img]](https://boris.unibe.ch/style/images/fileicons/text.png) |
Text
nihms958989.pdf - Accepted Version Restricted to registered users only Available under License Publisher holds Copyright. Download (1MB) |
|
|
Text
1-s2.0-S2211124718304479-main.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (4MB) | Preview |
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.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Präzisionsonkologie 04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Präzisionsonkologie |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2211-1247 |
Publisher: |
Cell Press |
Language: |
English |
Submitter: |
Marla Rittiner |
Date Deposited: |
01 Oct 2019 13:46 |
Last Modified: |
24 Oct 2019 11:00 |
Publisher DOI: |
10.1016/j.celrep.2018.03.086 |
PubMed ID: |
29617659 |
Additional Information: |
Mark Rubin (Direktor DBMR) ist Collaborator in dieser Publikation. |
Uncontrolled Keywords: |
artificial intelligence bioinformatics computer vision deep learning digital pathology immuno-oncology lymphocytes machine learning tumor microenvironment tumor-infiltrating lymphocytes |
BORIS DOI: |
10.7892/boris.126388 |
URI: |
https://boris.unibe.ch/id/eprint/126388 |
Actions (login required)
 |
Edit item |