Mathematical modelling of molecular heterogeneity identifies novel markers and subpopulations in complex tumors

Abstract

Intratumor heterogeneity, as both a major confounding factor and an underexploited information source, is widely implicated as a key driver of drug resistance. While a handful of reports have demonstrated the potential of supervised methods to deconvolute intratumor heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we developed convex analysis of mixtures (CAM), a fully unsupervised deconvolution method, for identifying marker genes and subpopulations directly from original mixed molecular expressions.