BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun in which the relations between modules are enriched with mutual exclusivity while genes within each module are functionally related; (ii) BeME-WithCo which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun which ensures co-occurrence between modules while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to the importance of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergetic effects, including a potential synergy between mutations in TP53 and metastasis related DCC gene

Spatial intratumoral heterogeneity and temporal clonal evolution in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is among the most common malignancies, but little is known about its spatial intratumoral heterogeneity (ITH) and temporal clonal evolutionary processes. To address this, we performed multiregion whole-exome sequencing on 51 tumor regions from 13 ESCC cases and multiregion global methylation profiling for 3 of these 13 cases. We found an average of 35.8% heterogeneous somatic mutations with strong evidence of ITH. Half of the driver mutations located on the branches of tumor phylogenetic trees targeted oncogenes, including PIK3CA, NFE2L2 and MTOR, among others. By contrast, the majority of truncal and clonal driver mutations occurred in tumor-suppressor genes, including TP53, KMT2D and ZNF750, among others. Interestingly, phyloepigenetic trees robustly recapitulated the topological structures of the phylogenetic trees, indicating a possible relationship between genetic and epigenetic alterations. Our integrated investigations of spatial ITH and clonal evolution provide an important molecular foundation for enhanced understanding of tumorigenesis and progression in ESCC

Identifying colorectal cancer caused by biallelic MUTYH pathogenic variants using tumor mutational signatures

Carriers of germline biallelic pathogenic variants in the MUTYH gene have a high risk of colorectal cancer. We test 5649 colorectal cancers to evaluate the discriminatory potential of a tumor mutational signature specific to MUTYH for identifying biallelic carriers and classifying variants of uncertain clinical significance (VUS). Using a tumor and matched germline targeted multi-gene panel approach, our classifier identifies all biallelic MUTYH carriers and all known non-carriers in an independent test set of 3019 colorectal cancers (accuracy = 100% (95% confidence interval 99.87-100%)). All monoallelic MUTYH carriers are classified with the non-MUTYH carriers. The classifier provides evidence for a pathogenic classification for two VUS and a benign classification for five VUS. Somatic hotspot mutations KRAS p.G12C and PIK3CA p.Q546K are associated with colorectal cancers from biallelic MUTYH carriers compared with non-carriers (p = 2 x 10(-23) and p = 6 x 10(-11), respectively). Here, we demonstrate the potential application of mutational signatures to tumor sequencing workflows to improve the identification of biallelic MUTYH carriers. Germline biallelic pathogenic MUTYH variants predispose patients to colorectal cancer (CRC); however, approaches to identify MUTYH variant carriers are lacking. Here, the authors evaluated mutational signatures that could distinguish MUTYH carriers in large CRC cohorts, and found MUTYH-associated somatic mutations

Quantifying the pro- and antimutagenic roles of DNA damage and repair

Genome integrity is essential to the survival of any living organism. The genome is constantly challenged by a multitude of endogenous and exogenous mutagenic factors such as environmental exposures or replication errors. Therefore, evolution has supplied cells with a number of repair mechanisms to protect their genetic information; however, excessive exposures or defects in the repair machinery can lead to the accumulation of deleterious mutations which may cause a range of diseases including cancer. Different mutational processes often leave behind characteristic patterns of mutations, so-called mutational signatures. Mutational signature analysis of tumours has gained a lot of attention recently, because it may reveal carcinogenic exposures and also therapeutic vulnerabilities. So far, over 50 mutational signatures have been identified using pattern recognition in large cancer cohorts, reflecting the action of a range of known mutagenic processes, such as UV light, tobacco smoke or mismatch repair deficiency, but for many mutational signatures an underlying generative process is still unknown. The search for the causes behind a given mutational signature is further complicated by the fact that every alteration in the DNA results from failed or incorrect repair of a DNA lesion, hence there are two factors which jointly shape the mutational spectrum of any mutagenic process. In this thesis, I quantify the variability of mutational signatures in model organisms and in human cancer and explore the diversity of DNA damage-repair interactions. Using data from a large mutagenesis screen in C. elegans, including over 50 DNA repair deficient genetic backgrounds, 12 genotoxins and nearly 200 combinations thereof, I characterise the mutational spectra and genomic features of a range of DNA repair deficiencies, and describe the mutational signatures of genotoxins across multiple genetic backgrounds. Importantly, the mutagenic contributions of genetic and mutagenic factors can vary dev pending on the DNA repair components available: over 35% of genotoxin-knockout combinations demonstrated a measurable effect on the mutation rate compared to expected values, and about 10% also presented a new mutational spectrum. Analysis of mutational signatures in cancer exomes demonstrates the relevance of C. elegans results to cancer investigation. Mismatch repair deficiency patterns extracted from C. elegans are comparable to those in gastrointestinal tumours, and help to dissect convoluted mutational processes. The antagonism between DNA damage and repair drives variability in cancer genomes as well: the observed interaction effects were low in magnitude, but evolutionary considerations suggest that cancer risk may be substantially elevated even by small increases in mutagenicity. In summary, this thesis presents the first comprehensive analysis of mutagenic DNA damage-repair interactions using experimental and cancer data. The results show that mutations result from the opposing pro- and anti-mutagenic forces of DNA damage and repair, which shape mutational signatures in highly variable ways. This variation has to be acknowledged and integrated into mutational signature analysis to ensure reliable interpretation and applicability in clinical oncology. Lastly, the cross-species comparison shows that the fundamental laws of mutagenesis are acting similarly across eukaryotic organisms reminding that many mutational processes fuelling tumorigenesis are not exclusive to cancer, but also drive variation and the evolution of species.My PhD studies were funded by the EMBL International PhD Programme

Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes

Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with significant heterogeneity in disease progression. Existing clinical models of progression risk do not fully capture this heterogeneity. Here we integrate 42 genetic alterations from 214 SMM patients using unsupervised binary matrix factorization (BMF) clustering and identify six distinct genetic subtypes. These subtypes are differentially associated with established MM-related RNA signatures, oncogenic and immune transcriptional profiles, and evolving clinical biomarkers. Three genetic subtypes are associated with increased risk of progression to active MM in both the primary and validation cohorts, indicating they can be used to better predict high and low-risk patients within the currently used clinical risk stratification models

SigsPack, a package for cancer mutational signatures

BACKGROUND: Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology. RESULTS: We present here SigsPack, a Bioconductor package to estimate a sample's exposure to mutational processes described by a set of mutational signatures. The package also provides functions to estimate stability of these exposures, using bootstrapping. The performance of exposure and exposure stability estimations have been validated using synthetic and real data. Finally, the package provides tools to normalize the mutation frequencies with respect to the tri-nucleotide contents of the regions probed in the experiment. The importance of this effect is illustrated in an example. CONCLUSION: SigsPack provides a complete set of tools for individual sample exposure estimation, and for mutation catalogue & mutational signatures normalization