CaMuS: simultaneous fitting and de novo imputation of cancer mutational signature

Diffsig: Associating Risk Factors with Mutational Signatures

BACKGROUND

Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors.

METHODS

Here, we develop a statistical model, Diffsig, for estimating the association of one or more continuous or categorical risk factors with DNA mutational signatures. Diffsig takes into account the uncertainty associated with assigning signatures to samples as well as multiple risk factors' simultaneous effect on observed DNA mutations.

RESULTS

We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. In simulation, our model was capable of accurately estimating expected associations in a variety of contexts.

CONCLUSIONS

Diffsig allows researchers to quantify and perform inference on the associations of risk factors with mutational signatures.

IMPACT

We expect Diffsig to provide more robust associations of risk factors with signatures to lead to better understanding of the tumor development process and improved models of tumorigenesis.

Evolutionary trajectories of small cell lung cancer under therapy

The evolutionary processes that underlie the marked sensitivity of small cell lung cancer (SCLC) to chemotherapy and rapid relapse are unknown1-3. Here we determined tumour phylogenies at diagnosis and throughout chemotherapy and immunotherapy by multiregion sequencing of 160 tumours from 65 patients. Treatment-naive SCLC exhibited clonal homogeneity at distinct tumour sites, whereas first-line platinum-based chemotherapy led to a burst in genomic intratumour heterogeneity and spatial clonal diversity. We observed branched evolution and a shift to ancestral clones underlying tumour relapse. Effective radio- or immunotherapy induced a re-expansion of founder clones with acquired genomic damage from first-line chemotherapy. Whereas TP53 and RB1 alterations were exclusively part of the common ancestor, MYC family amplifications were frequently not constituents of the founder clone. At relapse, emerging subclonal mutations affected key genes associated with SCLC biology, and tumours harbouring clonal CREBBP/EP300 alterations underwent genome duplications. Gene-damaging TP53 alterations and co-alterations of TP53 missense mutations with TP73, CREBBP/EP300 or FMN2 were significantly associated with shorter disease relapse following chemotherapy. In summary, we uncover key processes of the genomic evolution of SCLC under therapy, identify the common ancestor as the source of clonal diversity at relapse and show central genomic patterns associated with sensitivity and resistance to chemotherapy.

Diffsig: Associating Risk Factors With Mutational Signatures

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present Diffsig, a model and R package for estimating the association of risk factors with mutational signatures, suggesting etiologies for the pre-defined mutational signatures. Diffsig is a Bayesian Dirichlet-multinomial hierarchical model that allows testing of any type of risk factor while taking into account the uncertainty associated with samples with a low number of observations. In simulation, we found that our method can accurately estimate risk factor-mutational signal associations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. Diffsig is implemented as an R package available at: https://github.com/jennprk/diffsig.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Variant profiling of colorectal adenomas from three patients of two families with MSH3-related adenomatous polyposis

The spectrum of somatic genetic variation in colorectal adenomas caused by biallelic pathogenic germline variants in the MSH3 gene, was comprehensively analysed to characterise mutational signatures and identify potential driver genes and pathways of MSH3-related tumourigenesis. Three patients from two families with MSH3-associated polyposis were included. Whole exome sequencing of nine adenomas and matched normal tissue was performed. The amount of somatic variants in the MSH3-deficient adenomas and the pattern of single nucleotide variants (SNVs) was similar to sporadic adenomas, whereas the fraction of small insertions/deletions (indels) (21-42% of all small variants) was significantly higher. Interestingly, pathogenic somatic APC variants were found in all but one adenoma. The vast majority (12/13) of these were di-, tetra-, or penta-base pair (bp) deletions. The fraction of APC indels was significantly higher than that reported in patients with familial adenomatous polyposis (FAP) (p < 0.01) or in sporadic adenomas (p < 0.0001). In MSH3-deficient adenomas, the occurrence of APC indels in a repetitive sequence context was significantly higher than in FAP patients (p < 0.01). In addition, the MSH3-deficient adenomas harboured one to five (recurrent) somatic variants in 13 established or candidate driver genes for early colorectal carcinogenesis, including ACVR2A and ARID genes. Our data suggest that MSH3-related colorectal carcinogenesis seems to follow the classical APC-driven pathway. In line with the specific function of MSH3 in the mismatch repair (MMR) system, we identified a characteristic APC mutational pattern in MSH3-deficient adenomas, and confirmed further driver genes for colorectal tumourigenesis.

Mutational signatures: emerging concepts, caveats and clinical applications

Whole-genome sequencing has brought the cancer genomics community into new territory. Thanks to the sheer power provided by the thousands of mutations present in each patient's cancer, we have been able to discern generic patterns of mutations, termed 'mutational signatures', that arise during tumorigenesis. These mutational signatures provide new insights into the causes of individual cancers, revealing both endogenous and exogenous factors that have influenced cancer development. This Review brings readers up to date in a field that is expanding in computational, experimental and clinical directions. We focus on recent conceptual advances, underscoring some of the caveats associated with using the mutational signature frameworks and highlighting the latest experimental insights. We conclude by bringing attention to areas that are likely to see advancements in clinical applications.