Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model.

Abstract:

:One of the obstacles hindering a better understanding of cancer is its heterogeneity. However, computational approaches to model cancer heterogeneity have lagged behind. To bridge this gap, we have developed a new probabilistic approach that models individual cancer cases as mixtures of subtypes. Our approach can be seen as a meta-model that summarizes the results of a large number of alternative models. It does not assume predefined subtypes nor does it assume that such subtypes have to be sharply defined. Instead given a measure of phenotypic similarity between patients and a list of potential explanatory features, such as mutations, copy number variation, microRNA levels, etc., it explains phenotypic similarities with the help of these features. We applied our approach to Glioblastoma Multiforme (GBM). The resulting model Prob_GBM, not only correctly inferred known relationships but also identified new properties underlining phenotypic similarities. The proposed probabilistic framework can be applied to model relations between similarity of gene expression and a broad spectrum of potential genetic causes.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

Cho DY,Przytycka TM

doi

10.1093/nar/gkt577

subject

Has Abstract

pub_date

2013-09-01 00:00:00

pages

8011-20

issue

17

eissn

0305-1048

issn

1362-4962

pii

gkt577

journal_volume

41

pub_type

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