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Abdelmoula, W.M.* ; Balluff, B.* ; Englert, S. ; Dijkstra, J.* ; Reinders, M.J.T.* ; Walch, A.K. ; McDonnell, L.A.* ; Lelieveldt, B.P.F.*

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)
Verlagsversion Forschungsdaten DOI
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter 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 Band: 113, Heft: 43, Seiten: 12244-12249 Artikelnummer: , Supplement: ,
Verlag National Academy of Sciences
Verlagsort Washington
Begutachtungsstatus Peer reviewed