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

Walid M. Abdelmoula, Benjamin Balluff, Sonja Englert, Jouke Dijkstra, Marcel Reinders, Axel Walch, Liam A. McDonnell, Boudewijn Lelieveldt

Research output: Contribution to journalArticleScientificpeer-review

68 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)12244-12249
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume113
Issue number43
DOIs
Publication statusPublished - 2016

Keywords

  • intratumor heterogeneity
  • mass spectrometry imaging
  • t-SNE
  • biomarker
  • cancer

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