A Computational Framework To Infer The Order Of Accumulating Mutations In Individual Tumors

Phylogenetic methods are routinely used to quantify intra tumor heterogeneity (ITH) from multi-sample sequencing of individual tumors. These methods can deconvolve clonal or mutational structures, but sometimes require several complex technical assumptions. Here, we present a simple computational framework (Temporal oRder of Individual Tumors, TRaIT) to infer the qualitative ordering of mutations that accumulate during tumor growth from single-cell and multi-region data. TRaIT provides several off-the-shelf algorithms that can model confounding factors, tumors with multiple cells of origin and a generalized form of parallel (convergent) evolution. Our methods efficiently deal with technical errors in the data and have state-of-the-art performance, lower computational cost and better scalability than tools for phylogenetic inference. We show with real breast and colorectal cancer data that the joint application of TRaIT and complementary phylogenetic methods allows to better quantify the extent of ITH, and generate novel experimental hypotheses