Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

Yoo-Ah Kim,Rebecca Sarto Basso,Mark D M Leiserson,Welles Robinson,Itay Sason,Teresa M Przytycka,Fabio Vadin,Damian Wojtowicz,Dorit S Hochbaum,Roded Sharan

doi:10.1186/s13073-020-00745-2

Abstract

BackgroundStudies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells. However, cancer genomes accumulate a large number of passenger somatic mutations resulting from various endogenous and exogenous causes, including normal DNA damage and repair processes or cancer-related aberrations of DNA maintenance machinery as well as mutations triggered by carcinogenic exposures. Different mutagenic processes often produce characteristic mutational patterns called mutational signatures. Identifying mutagenic processes underlying mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis.MethodsTo investigate the genetic aberrations associated with mutational signatures, we took a network-based approach considering mutational signatures as cancer phenotypes. Specifically, our analysis aims to answer the following two complementary questions: (i) what are functional pathways whose gene expression activities correlate with the strengths of mutational signatures, and (ii) are there pathways whose genetic alterations might have led to specific mutational signatures? To identify mutated pathways, we adopted a recently developed optimization method based on integer linear programming.ResultsAnalyzing a breast cancer dataset, we identified pathways associated with mutational signatures on both expression and mutation levels. Our analysis captured important differences in the etiology of the APOBEC-related signatures and the two clock-like signatures. In particular, it revealed that clustered and dispersed APOBEC mutations may be caused by different mutagenic processes. In addition, our analysis elucidated differences between two age-related signatures—one of the signatures is correlated with the expression of cell cycle genes while the other has no such correlation but shows patterns consistent with the exposure to environmental/external processes.ConclusionsThis work investigated, for the first time, a network-level association of mutational signatures and dysregulated pathways. The identified pathways and subnetworks provide novel insights into mutagenic processes that the cancer genomes might have undergone and important clues for developing personalized drug therapies.

Highlights

Studies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells
Our study provides several new insights on the mutagenic processes in breast cancer including (i) association of the nucleotide excision repair (NER) pathway and oxidation processes with the strength of clock-like Signature 5, (ii) differences between the two clock-like signatures with respect to their associations with cell cycle, and (iii) differences in mutated subnetworks associated with different signatures including APOBEC-related signatures
To obtain a finer scale expression modules related to DNA repair, we zoomed in on genes involved in Gene Ontology DNA metabolic process (Fig. 2b)

Summary

Introduction

Studies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells. Cancer genomes accumulate a high number of mutations, only a small portion of which are cancer driving mutations Most of such mutations are passenger somatic mutations, not directly contributing to cancer development. Analyses of large-scale cancer genome data revealed that these passenger mutations often exhibit characteristic mutational patterns called “mutational signatures” [1]. These characteristic mutational signatures are often linked to specific mutagenic processes, making it possible to infer which mutagenic processes have been active in the given patient. This information often provides important clues about the nature of the diseases. With the increased interest in the information on mutagenic processes acting on cancer genomes, several computational approaches have been developed to define mutational signatures in cancer [1, 3,4,5,6,7], to identify patients whose genome contains given signatures [6,7,8], to map patient mutations to these signatures [9], and to identify superposition of several mutagenic processes [10]

Methods

Results

Discussion

Conclusion