Computational Methods for Characterizing Cancer Mutational Heterogeneity

Abstract

Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.

Keywords: cancer heterogeneity; cancer pathways; clinical association; mutations; mutual exclusivity.

Figure 1

Inter-tumor heterogeneity and its causes. Driver mutations (in red) target genes which are members of different cancer pathways, sets of interacting genes which perform specific functions and are altered in cancer. Passenger mutations (in black) not related to the disease comprise the majority of mutations in a tumor. Different mutated genes in cancer pathways and different passenger mutations are observed in tumors of the same type, with two cancer genomes often having no mutation in common.

Figure 2

Intra-tumor heterogeneity and its causes. Cancer evolves from a normal cell that accumulates mutations (in red, yellow, and blue), leading to different clones, populations of cells of different genotypes, coexisting in the same tumor. Bulk sequencing measures mutations from a sample of the resulting cell mixture, that also comprises normal cells. The fraction of reads supporting a mutation (VAF) is proportional to the number of cells with the mutation in the sample.

Figure 3

Computational analyses to characterize inter-tumor heterogeneity. Starting from somatic mutations measured in many patients, different types of analyses are possible: annotation and enrichment analysis for known pathways; network analyses to discover significantly mutated subnetworks of a large interaction networks; the de novo identification of pathways, based for example on the identification of mutual exclusivity patterns.

Figure 4

Computational analyses to characterize intra-tumor heterogeneity. Starting from measurements (e.g.,VAFs) obtained from bulk sequencing of one or more tumor samples, one can infer the clonal composition of the sample and also the evolutionary relationships among clones. Single-cell sequencing can be use to infer the evolutionary relationships among the individual cells for which mutations have been assayed.

Figure 5

Network of Mutations Associated with Survival (NoMAS). NoMAS combines mutations measured in many patients and the corresponding survival time with a large interaction network to identify subnetworks of genes with significant association to survival. NoMAS is based on an efficient graph color-coding algorithm, and uses a permutation test to correct for multiple hypothesis testing.