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Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images

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Title: Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
Authors: Saltz, J
Gupta, R
Hou, L
Kurc, T
Singh, P
Vu, N
Samaras, D
Shroyer, KR
Zhao, T
Batiste, R
Van Arnam, J
Shmulevich, I
Rao, AUK
Lazar, AJ
Sharma, A
Thorsson, V
Item Type: Journal Article
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.
Issue Date: 3-Apr-2018
Date of Acceptance: 20-Mar-2018
URI: http://hdl.handle.net/10044/1/71242
DOI: https://doi.org/10.1016/j.celrep.2018.03.086
ISSN: 2211-1247
Publisher: Elsevier
Start Page: 181
End Page: 193.e7
Journal / Book Title: Cell Reports
Volume: 23
Issue: 1
Copyright Statement: © 2018 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Sponsor/Funder: SAIC-F-Frederick, Inc
Leidos Biomedical Research, Inc.
Funder's Grant Number: TCGA Pilot Program
15Y011ST
Keywords: Science & Technology
Life Sciences & Biomedicine
Cell Biology
SQUAMOUS-CELL CARCINOMA
STANDARDIZED METHOD
IMMUNE CONTEXTURE
GENOMIC ANALYSES
SOLID TUMORS
CANCER
MELANOMA
SUBTYPES
PROPOSAL
BREAST
artificial intelligence
bioinformatics
computer vision
deep learning
digital pathology
immuno-oncology
lymphocytes
machine learning
tumor microenvironment
tumor-infiltrating lymphocytes
Cancer Genome Atlas Research Network
Science & Technology
Life Sciences & Biomedicine
Cell Biology
SQUAMOUS-CELL CARCINOMA
STANDARDIZED METHOD
IMMUNE CONTEXTURE
GENOMIC ANALYSES
SOLID TUMORS
CANCER
MELANOMA
SUBTYPES
PROPOSAL
BREAST
Publication Status: Published
Open Access location: https://www.cell.com/cell-reports/fulltext/S2211-1247(18)30447-9?_returnURL=https:%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2211124718304479%3Fshowall%3Dtrue
Online Publication Date: 2018-04-05
Appears in Collections:Department of Surgery and Cancer