Data-driven Identification of Prognostic Tumor Subpopulations Using Spatially Mapped T-SNE of Mass Spectrometry Imaging Data

Overview

Journal Proc Natl Acad Sci U S A

Specialty Science

Date 2016 Oct 30

PMID 27791011

Citations 58

Authors

Walid M Abdelmoula

Benjamin Balluff

Sonja Englert

Jouke Dijkstra

Marcel J T Reinders

Axel Walch

Liam A McDonnell

Boudewijn P F Lelieveldt

Affiliations

Soon will be listed here.

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.

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