RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer

MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification

MOTIVATION

Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients.

RESULTS

We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics.

AVAILABILITY AND IMPLEMENTATION

MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE.

Accurate and sensitive mutational signature analysis with MuSiCal

Mutational signature analysis is a recent computational approach for interpreting somatic mutations in the genome. Its application to cancer data has enhanced our understanding of mutational forces driving tumorigenesis and demonstrated its potential to inform prognosis and treatment decisions. However, methodological challenges remain for discovering new signatures and assigning proper weights to existing signatures, thereby hindering broader clinical applications. Here we present Mutational Signature Calculator (MuSiCal), a rigorous analytical framework with algorithms that solve major problems in the standard workflow. Our simulation studies demonstrate that MuSiCal outperforms state-of-the-art algorithms for both signature discovery and assignment. By reanalyzing more than 2,700 cancer genomes, we provide an improved catalog of signatures and their assignments, discover nine indel signatures absent in the current catalog, resolve long-standing issues with the ambiguous 'flat' signatures and give insights into signatures with unknown etiologies. We expect MuSiCal and the improved catalog to be a step towards establishing best practices for mutational signature analysis.

Cancer driver mutations: predictions and reality

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.

Spectrum of DNA mismatch repair failures viewed through the lens of cancer genomics and implications for therapy

Genome sequencing can be used to detect DNA repair failures in tumors and learn about underlying mechanisms. Here, we synthesize findings from genomic studies that examined deficiencies of the DNA mismatch repair (MMR) pathway. The impairment of MMR results in genome-wide hypermutation and in the 'microsatellite instability' (MSI) phenotype-occurrence of indel mutations at short tandem repeat (microsatellite) loci. The MSI status of tumors was traditionally assessed by molecular testing of a selected set of MS loci or by measuring MMR protein expression levels. Today, genomic data can provide a more complete picture of the consequences on genomic instability. Multiple computational studies examined somatic mutation distributions that result from failed DNA repair pathways in tumors. These include analyzing the commonly studied trinucleotide mutational spectra of single-nucleotide variants (SNVs), as well as of other features such as indels, structural variants, mutation clusters and regional mutation rate redistribution. The identified mutation patterns can be used to rigorously measure prevalence of MMR failures across cancer types, and potentially to subcategorize the MMR deficiencies. Diverse data sources, genomic and pre-genomic, from human and from experimental models, suggest there are different ways in which MMR can fail, and/or that the cell-type or genetic background may result in different types of MMR mutational patterns. The spectrum of MMR failures may direct cancer evolution, generating particular sets of driver mutations. Moreover, MMR affects outcomes of therapy by DNA damaging drugs, antimetabolites, nonsense-mediated mRNA decay (NMD) inhibitors, and immunotherapy by promoting either resistance or sensitivity, depending on the type of therapy.

Decomposing the mutational landscape of cancer genomes with RepairSig

Mutational signatures are the outcomes of mutagenic processes that occur prior to, and during, tumorigenesis as a result of DNA damage, DNA repair, and DNA replication. In this issue of Cell Systems, Wojtowicz et al. introduce a new computational model aimed at deconstructing the mutational processes that shape cancer genomes.