Population-based statistical inference for temporal sequence of somatic mutations in cancer genomes

Tae-Min Kim,Je-Keun Rhee

doi:10.1186/s12920-018-0352-z

Abstract

BackgroundIt is well recognized that accumulation of somatic mutations in cancer genomes plays a role in carcinogenesis; however, the temporal sequence and evolutionary relationship of somatic mutations remain largely unknown.MethodsIn this study, we built a population-based statistical framework to infer the temporal sequence of acquisition of somatic mutations. Using the model, we analyzed the mutation profiles of 1954 tumor specimens across eight tumor types.ResultsAs a result, we identified tumor type-specific directed networks composed of 2-15 cancer-related genes (nodes) and their mutational orders (edges). The most common ancestors identified in pairwise comparison of somatic mutations were TP53 mutations in breast, head/neck, and lung cancers. The known relationship of KRAS to TP53 mutations in colorectal cancers was identified, as well as potential ancestors of TP53 mutation such as NOTCH1, EGFR, and PTEN mutations in head/neck, lung and endometrial cancers, respectively. We also identified apoptosis-related genes enriched with ancestor mutations in lung cancers and a relationship between APC hotspot mutations and TP53 mutations in colorectal cancers.ConclusionWhile evolutionary analysis of cancers has focused on clonal versus subclonal mutations identified in individual genomes, our analysis aims to further discriminate ancestor versus descendant mutations in population-scale mutation profiles that may help select cancer drivers with clinical relevance.

Highlights

It is well recognized that accumulation of somatic mutations in cancer genomes plays a role in carcinogenesis; the temporal sequence and evolutionary relationship of somatic mutations remain largely unknown
The co-occurring mutation gene pairs with high SCORE-CO were tumor type-specific, e.g., gene pairs of TP53 and PIK3CA were highly ranked in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), COADREAD, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC) (SCORE-CO = 0.089 for 8 cases with the co-occurrence / total 90 patients, 0.055 for 40 cases, 0.085 for cases, 0.083 for cases, 0.085 for 10 cases, 0.106 for 18 cases, respectively) and to a lesser extent in LUAD (SCORE-CO = 0.024 for 7 cases)
Similar approaches have been previously proposed in which genomic data of multiple tumors at their fully transformed stages may be used to deduce the temporal sequence of genomic events (RESIC [4]); we extended this idea by exploiting the distinction of clonal versus subclonal mutations based on cancer cell fraction (CCF) estimates of individual mutations

Summary

Introduction

It is well recognized that accumulation of somatic mutations in cancer genomes plays a role in carcinogenesis; the temporal sequence and evolutionary relationship of somatic mutations remain largely unknown. It has long been recognized that cancer genomes undergo a stepwise progression in which they acquire somatic mutations in a sequential order during their evolution Attolini et al proposed a mathematical approach to determine the sequential order of APC, KRAS, and TP53 mutations in 70 colorectal cancer samples [4] They estimated the mutation rate per allele and predicted the temporal sequences for mutations acquired in these genes. Foo et al investigated driver mutations in the evolutionary processes of mutation accumulation using healthy and tumor tissues [7] Most of these previous reports used binary genomic data (e.g., calls for presence or absence of mutations or copy number alterations) and did not exploit information regarding the clonality of mutations (e.g., clonal vs subclonal mutations)

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Results

Discussion

Conclusion