Inference of mutability landscapes of tumors from single cell sequencing data

One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https:// github.com/compbel/MULAN.. Author summary Cancer is a dynamical evolutionary process that unfolds in populations of tumor cells. Combinations of genomic alterations of these cells affect their replication and survival. In particular, intra-tumor rates of mutation and genetic instability are often significantly higher than the normal rate. The impact of combinations of gene alterations on the genetic instability of cancer cells could be highly non-linear. In this paper, we present a computational approach called MULAN, that allows for estimation of instability rates inside heterogeneous intra-tumor populations shaped by such non-linear genetic interactions. To achieve this, we make use of single-cell sequencing, that allows to capture exact cancer clones rather than just individual mutations. We demonstrate the accuracy of our approach and show how it could be applied to experimental tumor data to study the evolution of genetic instability and infer evolutionary history. The proposed method can be used to provide new insight into the evolutionary dynamics of cancer.