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

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

Journal Article (2016)

Author(s)

Walid M. Abdelmoula (Leiden University Medical Center)

Benjamin Balluff (Maastricht University, Leiden University Medical Center)

Sonja Englert (German Research Center for Environmental Health)

Jouke Dijkstra (Leiden University Medical Center)

Marcel Reinders (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Axel Walch (German Research Center for Environmental Health)

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

Boudewijn Lelieveldt (TU Delft - Electrical Engineering, Mathematics and Computer Science, Leiden University Medical Center)

Research Group

Pattern Recognition and Bioinformatics

Cancer Biomarker T-SNE Mass spectrometry imaging Intratumor heterogeneity

DOI related publication

https://doi.org/10.1073/pnas.1510227113 Final published version

To reference this document use

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

More Info

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Publication Year

2016

Language

English

Research Group

Pattern Recognition and Bioinformatics

Journal title

Proceedings of the National Academy of Sciences of the United States of America

Issue number

43

Volume number

113

Pages (from-to)

12244-12249

Downloads counter

225

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.