Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data

Abdelmoula, Walid M.; Balluff, Benjamin; Englert, Sonja; Dijkstra, J.; Reinders, M.J.T.; Walch, Axel; McDonnell, Liam A.; Lelieveldt, Boudewijn

Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data

Journal article (2016)

Authors

Walid M. Abdelmoula Leiden University Medical Center

Benjamin Balluff Leiden University Medical Center, Universiteit Maastricht

Sonja Englert German Research Center for Environmental Health

J. Dijkstra Leiden University Medical Center

M.J.T. Reinders Pattern Recognition and Bioinformatics -

Axel Walch German Research Center for Environmental Health

Liam A. McDonnell Fondazione Pisana per la Scienza ONLUS, Leiden University Medical Center

Boudewijn Lelieveldt Pattern Recognition and Bioinformatics - , Leiden University Medical Center

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

Cancer Biomarker T-SNE Mass spectrometry imaging Intratumor heterogeneity

To reference this document use:

http://resolver.tudelft.nl/uuid:d47e69d0-f953-433b-8698-dd3c767942f7

More Info

expand_more

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

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.