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Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data.
Proc. Natl. Acad. Sci. U.S.A. 113, 12244-12249 (2016)
The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
intratumor heterogeneity; mass spectrometry imaging; t-SNE; biomarker; cancer; Intratumor Heterogeneity; Clonal Evolution; Cancer; Visualization; Challenges; Expression; Brain
ISSN (print) / ISBN
0027-8424
e-ISSN
1091-6490
Quellenangaben
Volume: 113,
Issue: 43,
Pages: 12244-12249
Publisher
National Academy of Sciences
Publishing Place
Washington
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Research Unit Analytical Pathology (AAP)