2021-06-09 JUN

Journal Club

Mutational Signatures

The repertoire of mutational signatures in human cancer (Alexandrov et. al., 2020)

Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.

Bioinformatic Methods to Identify Mutational Signatures in Cancer (Islam & Alexandrov, 2021)

The genome of a cancer contains somatic mutations that reflect the activities of endogenous and exogenous mutational processes, with each mutational process imprinting a characteristic mutational signature. Computational analysis of somatic mutations derived from next-generation sequencing data allows revealing the mutational signatures operative in a set of cancer genomes. In this chapter, we briefly review the concept of mutational signatures and the tools available for deciphering mutational signatures. Further, we provide a quick guide as well as an in-depth protocol for deciphering mutational signatures using the tool SigProfilerExtractor and review the results generated from an example dataset of cancer genomes.

Mutational Signatures in Breast Cancer: The Problem at the DNA Level (Nik-Zainal & Morganella, 2017)

A breast cancer genome is a record of the historic mutagenic activity that has occurred throughout the development of the tumor. Indeed, every mutation may be informative. Although driver mutations were the main focus of cancer research for a long time, passenger mutational signatures, the imprints of DNA damage and DNA repair processes that have been operative during tumorigenesis, are also biologically illuminating. This review is a chronicle of how the concept of mutational signatures arose and brings the reader up-to-date on this field, particularly in breast cancer. Mutational signatures have now been advanced to include mutational processes that involve rearrangements, and novel cancer biological insights have been gained through studying these in great detail. Furthermore, there are efforts to take this field into the clinical sphere. If validated, mutational signatures could thus form an additional weapon in the arsenal of cancer precision diagnostics and therapeutic stratification in the modern war against cancer.

Detecting the mutational signature of homologous recombination deficiency in clinical samples (Gulhan et. al, 2019)

Mutations in BRCA1 and/or BRCA2 (BRCA1/2) are the most common indication of deficiency in the homologous recombination (HR) DNA repair pathway. However, recent genome-wide analyses have shown that the same pattern of mutations found in BRCA1/2-mutant tumors is also present in several other tumors. Here, we present a new computational tool called Signature Multivariate Analysis (SigMA), which can be used to accurately detect the mutational signature associated with HR deficiency from targeted gene panels. Whereas previous methods require whole-genome or whole-exome data, our method detects the HR-deficiency signature even from low mutation counts, by using a likelihood-based measure combined with machine-learning techniques. Cell lines that we identify as HR deficient show a significant response to poly (ADP-ribose) polymerase (PARP) inhibitors; patients with ovarian cancer whom we found to be HR deficient show a significantly longer overall survival with platinum regimens. By enabling panel-based identification of mutational signatures, our method substantially increases the number of patients that may be considered for treatments targeting HR deficiency.


Useful links:

  1. Wikipedia article: https://en.wikipedia.org/wiki/Mutational_signatures

  2. Catalog of mutational signatures: https://cancer.sanger.ac.uk/cosmic

  3. Non-negative matrix factorization (NMF) intuition: https://arxiv.org/abs/1401.5226

  4. Signature Multivariate Analysis (SigMA) tool: https://github.com/parklab/SigMA

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