UNASSIGNED: Constitutional mismatch repair deficiency (CMMRD) is a cancer predisposition due to biallelic mutations in one of the mismatch repair (MMR) genes associated with early onset of cancers, especially high-grade gliomas. Our aim was to decipher the molecular specificities of these gliomas.
UNASSIGNED: Clinical, histopathological, and whole exome sequencing data were analyzed in 12 children with genetically proven CMMRD and a high-grade glioma.
UNASSIGNED: PDL1 expression was present in immunohistochemistry in 50% of the samples. In 9 patients, the glioma harbored an ultra-hypermutated phenotype (104-635 coding single nucleotide variants (SNV) per Mb, median 204). Driver mutations in POLE and POLD1 exonuclease domains were described for 8 and 1 patients respectively and were always present in the mutation burst with the highest variant allele frequency (VAF). The mutational signatures were dominated by MMR-related ones and similar in the different mutation bursts of a same patient without subsequent enrichment of the mutation signatures with POL-driven ones. Median number of coding SNV with VAF above one of the driving polymerase mutation per Mb was 57 (17-191). Our findings suggest that somatic polymerase alterations does not entirely explain the ultra-hypermutant phenotype. SETD2, TP53, NF1, EPHB2, PRKDC, and DICER1 genes were frequently mutated with higher VAF than the deleterious somatic polymerase mutation.
UNASSIGNED: CMMRD-associated gliomas have a specific oncogenesis that does not involve usual pathways and mutations seen in sporadic pediatric or adult glioblastomas. Frequent alterations in other pathways such as MAPK may suggest the use of other targeted therapies along with PD1 inhibitors.

By replicating damaged nucleotides, error-prone DNA translesion synthesis (TLS) enables the completion of replication, albeit at the expense of fidelity. TLS of helix-distorting DNA lesions, that usually have reduced capacity of basepairing, comprises insertion opposite the lesion followed by extension, the latter in particular by polymerase ζ (Pol ζ). However, little is known about involvement of Pol ζ in TLS of non- or poorly-distorting, but miscoding, lesions such as O6-methyldeoxyguanosine (O6-medG). Using purified Pol ζ we describe that the enzyme can misincorporate thymidine opposite O6-medG and efficiently extend from terminal mismatches, suggesting its involvement in the mutagenicity of O6-medG. Surprisingly, O6-medG lesions induced by the methylating agent N-methyl-N\'-nitro-N-nitrosoguanidine (MNNG) appeared more, rather than less, mutagenic in Pol ζ-deficient mouse embryonic fibroblasts (MEFs) than in wild type MEFs. This suggested that in vivo Pol ζ participates in non-mutagenic TLS of O6-medG. However, we found that the Pol ζ-dependent misinsertions at O6-medG lesions are efficiently corrected by DNA mismatch repair (MMR), which masks the error-proneness of Pol ζ. We also found that the MNNG-induced mutational signature is determined by the adduct spectrum, and modulated by MMR. The signature mimicked single base substitution signature 11 in the catalogue of somatic mutations in cancer, associated with treatment with the methylating drug temozolomide. Our results unravel the individual roles of the major contributors to methylating drug-induced mutagenesis. Moreover, these results warrant caution as to the classification of TLS as mutagenic or error-free based on in vitro data or on the analysis of mutations induced in MMR-proficient cells.

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.

The genome of a cell is continuously battered by a plethora of exogenous and endogenous processes that can lead to damaged DNA. Repair mechanisms correct this damage most of the time, but failure to do so leaves mutations. Mutations do not occur in random manner, but rather typically follow a more or less specific pattern due to known or imputed mutational processes. Mutational signature analysis is the process by which the predominant mutational process can be inferred for a cancer and can be used in several contexts to study both the genesis of cancer and its response to therapy. Recent pan-cancer genomic efforts such as \"The Cancer Genome Atlas\" have identified numerous mutational signatures that can be categorized into single base substitutions, doublet base substitutions, or small insertions/deletions. Understanding these mutational signatures as they occur in non-small lung cancer could improve efforts at prevention, predict treatment response to personalized treatments, and guide the development of therapies targeting tumor evolution. For non-small cell lung cancer, several mutational signatures have been identified that correlate with exposures such as tobacco smoking and radon and can also reflect endogenous processes such as aging, APOBEC activity, and loss of mismatch repair. Herein, we provide an overview of the current knowledge of mutational signatures in non-small lung cancer.

Mutational patterns caused by APOBEC3 cytidine deaminase activity are evident throughout human cancer genomes. In particular, the APOBEC3A family member is a potent genotoxin that causes substantial DNA damage in experimental systems and human tumors. However, the mechanisms that ensure genome stability in cells with active APOBEC3A are unknown. Through an unbiased genome-wide screen, we define the Structural Maintenance of Chromosomes 5/6 (SMC5/6) complex as essential for cell viability when APOBEC3A is active. We observe an absence of APOBEC3A mutagenesis in human tumors with SMC5/6 dysfunction, consistent with synthetic lethality. Cancer cells depleted of SMC5/6 incur substantial genome damage from APOBEC3A activity during DNA replication. Further, APOBEC3A activity results in replication tract lengthening which is dependent on PrimPol, consistent with re-initiation of DNA synthesis downstream of APOBEC3A-induced lesions. Loss of SMC5/6 abrogates elongated replication tracts and increases DNA breaks upon APOBEC3A activity. Our findings indicate that replication fork lengthening reflects a DNA damage response to APOBEC3A activity that promotes genome stability in an SMC5/6-dependent manner. Therefore, SMC5/6 presents a potential therapeutic vulnerability in tumors with active APOBEC3A.

While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3S249C. In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients.

Cancers are the product of evolutionary events, where molecular variation occurs and accumulates in tissues and tumors. Sequencing of this molecular variation informs not only which variants are driving tumorigenesis, but also the mechanisms behind what is fueling mutagenesis. Both of these details are crucial for preventing premature deaths due to cancer, whether it is by targeting the variants driving the cancer phenotype or by measures to prevent exogenous mutations from contributing to somatic evolution. Here, we review tools to determine both molecular signatures and cancer drivers, and avenues by which these metrics may be linked.

Multiple myeloma (MM) is the second most common hematological malignancy, which remains incurable despite recent advances in treatment strategies. Like other forms of cancer, MM is characterized by genomic instability, caused by defects in DNA repair. Along with mutations in DNA repair genes and genotoxic drugs used to treat MM, non-canonical secondary DNA structures (four-stranded G-quadruplex structures) can affect accumulation of somatic mutations and chromosomal abnormalities in the tumor cells of MM patients. Here, we tested the hypothesis that G-quadruplex structures may influence the distribution of somatic mutations in the tumor cells of MM patients. We sequenced exomes of normal and tumor cells of 11 MM patients and analyzed the data for the presence of G4 context around points of somatic mutations. To identify molecular mechanisms that could affect mutational profile of tumors, we also analyzed mutational signatures in tumor cells as well as germline mutations for the presence of specific SNPs in DNA repair genes or in genes regulating G-quadruplex unwinding. In several patients, we found that sites of somatic mutations are frequently located in regions with G4 context. This pattern correlated with specific germline variants found in these patients. We discuss the possible implications of these variants for mutation accumulation and specificity in MM and propose that the extent of G4 context enrichment around somatic mutation sites may be a novel metric characterizing mutational processes in tumors.

Early-onset breast cancer (EoBC), defined by a diagnosis <40 years of age, is associated with poor prognosis. This study investigated the mutational landscape of non-metastatic EoBC and the prognostic relevance of mutational signatures using 100 tumour samples from Alberta, Canada. The MutationalPatterns package in R/Bioconductor was used to extract de novo single-base substitution (SBS) and insertion-deletion (indel) mutational signatures and to fit COSMIC SBS and indel signatures. We assessed associations between these signatures and clinical characteristics of disease, in addition to recurrence-free (RFS) and overall survival (OS). Five SBS and two indel signatures were extracted. The SBS13-like signature had higher relative contributions in the HER2-enriched subtype. Patients with higher than median contribution tended to have better RFS after adjustment for other prognostic factors (HR = 0.29; 95% CI: 0.08-1.06). An unsupervised clustering algorithm based on absolute contribution revealed three clusters of fitted COSMIC SBS signatures, but cluster membership was not associated with clinical variables or survival outcomes. The results of this exploratory study reveal various SBS and indel signatures may be associated with clinical features of disease and prognosis. Future studies with larger samples are required to better understand the mechanistic underpinnings of disease progression and treatment response in EoBC.

Tumor mutational signatures have gained prominence in cancer research, yet the lack of standardized methods hinders reproducibility and robustness. Leveraging colorectal cancer (CRC) as a model, we explored the influence of computational parameters on mutational signature analyses across 230 CRC cell lines and 152 CRC patients. Results were validated in three independent datasets: 483 endometrial cancer patients stratified by mismatch repair (MMR) status, 35 lung cancer patients by smoking status and 12 patient-derived organoids (PDOs) annotated for colibactin exposure. Assessing various bioinformatic tools, reference datasets and input data sizes including whole genome sequencing, whole exome sequencing and a pan-cancer gene panel, we demonstrated significant variability in the results. We report that the use of distinct algorithms and references led to statistically different results, highlighting how arbitrary choices may induce variability in the mutational signature contributions. Furthermore, we found a differential contribution of mutational signatures between coding and intergenic regions and defined the minimum number of somatic variants required for reliable mutational signature assignment. To facilitate the identification of the most suitable workflows, we developed Comparative Mutational Signature analysis on Coding and Extragenic Regions (CoMSCER), a bioinformatic tool which allows researchers to easily perform comparative mutational signature analysis by coupling the results from several tools and public reference datasets and to assess mutational signature contributions in coding and non-coding genomic regions. In conclusion, our study provides a comparative framework to elucidate the impact of distinct computational workflows on mutational signatures.