Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

Mutations, substitutions, and selection: Linking mutagenic processes to cancer using evolutionary theory

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.

Clinical Interest in Exome-Based Analysis of Somatic Mutational Signatures for Non-Small Cell Lung Cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality. This study investigates the clinical interest of whole exome sequencing (WES) for analyzing somatic mutational signatures in patients with advanced or metastatic NSCLC treated with the current standard of care.

METHODS

Exome sequencing data and clinical characteristics from 132 patients with advanced or metastatic NSCLC were analyzed. Somatic mutational signatures including single base substitutions (SBSs), double base substitutions (DBSs), and copy number signatures were evaluated. Structural variations including tumor mutational burden (TMB), the number of neoantigens, TCR clonality, homologous recombination deficiency (HRD), copy number alterations (CNAs), and microsatellite instability (MSI) score were determined. The association between these genomic features, NSCLC subtypes, and patient outcomes (progression-free and overall survival) was evaluated.

CONCLUSIONS

Exome sequencing offers valuable insights into somatic mutational signatures in NSCLC. This study identified specific signatures associated with a poor response to immune checkpoint inhibitor (ICI) therapy and chemotherapy, potentially aiding treatment selection and identifying patients unlikely to benefit from these approaches.

TEX264 drives selective autophagy of DNA lesions to promote DNA repair and cell survival

DNA repair and autophagy are distinct biological processes vital for cell survival. Although autophagy helps maintain genome stability, there is no evidence of its direct role in the repair of DNA lesions. We discovered that lysosomes process topoisomerase 1 cleavage complexes (TOP1cc) DNA lesions in vertebrates. Selective degradation of TOP1cc by autophagy directs DNA damage repair and cell survival at clinically relevant doses of topoisomerase 1 inhibitors. TOP1cc are exported from the nucleus to lysosomes through a transient alteration of the nuclear envelope and independent of the proteasome. Mechanistically, the autophagy receptor TEX264 acts as a TOP1cc sensor at DNA replication forks, triggering TOP1cc processing by the p97 ATPase and mediating the delivery of TOP1cc to lysosomes in an MRE11-nuclease- and ATR-kinase-dependent manner. We found an evolutionarily conserved role for selective autophagy in DNA repair that enables cell survival, protects genome stability, and is clinically relevant for colorectal cancer patients.

Genome-scale mutational signature analysis in fixed archived tissues

Mutation spectra and mutational signatures in cancerous and non-cancerous tissues can be identified by various established techniques of massively parallel sequencing (or next-generation sequencing) including whole-exome or whole-genome sequencing, and more recently by error-corrected/duplex sequencing. One rather underexplored area has been the genome-scale analysis of mutational signatures as markers of mutagenic exposures, and their impact on cancer driver events applied to formalin-fixed or alcohol-fixed paraffin embedded archived biospecimens. This review showcases successful applications of the next-generation sequencing methodologies in archived fixed tissues, including the delineation of the specific tissue fixation-related DNA damage manifesting as artifactual signatures, distinguishable from the true signatures that arise from biological mutagenic processes. Overall, we discuss and demonstrate how next-generation sequencing techniques applied to archived fixed biospecimens can enhance our understanding of cancer causes including mutagenic effects of extrinsic cancer risk agents, and the implications for prevention efforts aimed at reducing avoidable cancer-causing exposures.

CLEMENT: genomic decomposition and reconstruction of non-tumor subclones

Genome-level clonal decomposition of a single specimen has been widely studied; however, it is mostly limited to cancer research. In this study, we developed a new algorithm CLEMENT, which conducts accurate decomposition and reconstruction of multiple subclones in genome sequencing of non-tumor (normal) samples. CLEMENT employs the Expectation-Maximization (EM) algorithm with optimization strategies specific to non-tumor subclones, including false variant call identification, non-disparate clone fuzzy clustering, and clonal allele fraction confinement. In the simulation and in vitro cell line mixture data, CLEMENT outperformed current cancer decomposition algorithms in estimating the number of clones (root-mean-square-error = 0.58-0.78 versus 1.43-3.34) and in the variant-clone membership agreement (∼85.5% versus 70.1-76.7%). Additional testing on human multi-clonal normal tissue sequencing confirmed the accurate identification of subclones that originated from different cell types. Clone-level analysis, including mutational burden and signatures, provided a new understanding of normal-tissue composition. We expect that CLEMENT will serve as a crucial tool in the currently emerging field of non-tumor genome analysis.

Cell-intrinsic platinum response and associated genetic and gene expression signatures in ovarian cancer cell lines and isogenic models

Ovarian cancers are still largely treated with platinum-based chemotherapy as the standard of care, yet few biomarkers of clinical response have had an impact on clinical decision making as of yet. Two particular challenges faced in mechanistically deciphering platinum responsiveness in ovarian cancer have been the suitability of cell line models for ovarian cancer subtypes and the availability of information on comparatively how sensitive ovarian cancer cell lines are to platinum. We performed one of the most comprehensive profiles to date on 36 ovarian cancer cell lines across over seven subtypes and integrated drug response and multiomic data to improve on our understanding of the best cell line models for platinum responsiveness in ovarian cancer. RNA-seq analysis of the 36 cell lines in a single batch experiment largely conforms with the currently accepted subtyping of ovarian cancers, further supporting other studies that have reclassified cell lines and demonstrate that commonly used cell lines are poor models of high-grade serous ovarian carcinoma. We performed drug dose response assays in the 32 of these cell lines for cisplatin and carboplatin, providing a quantitative database of IC50s for these drugs. Our results demonstrate that cell lines largely fall either well above or below the equivalent dose of the clinical maximally achievable dose (Cmax) of each compound, allowing designation of cell lines as sensitive or resistant. We performed differential expression analysis for high-grade serous ovarian carcinoma cell lines to identify gene expression correlating with platinum-response. Further, we generated two platinum-resistant derivatives each for OVCAR3 and OVCAR4, as well as leveraged clinically-resistant PEO1/PEO4/PEO6 and PEA1/PEA2 isogenic models to perform differential expression analysis for seven total isogenic pairs of platinum resistant cell lines. While gene expression changes overall were heterogeneous and vast, common themes were innate immunity/STAT activation, epithelial to mesenchymal transition and stemness, and platinum influx/efflux regulators. In addition to gene expression analyses, we performed copy number signature analysis and orthogonal measures of homologous recombination deficiency (HRD) scar scores and copy number burden, which is the first report to our knowledge applying field-standard copy number signatures to ovarian cancer cell lines. We also examined markers and functional readouts of stemness that revealed that cell lines are poor models for examination of stemness contributions to platinum resistance, likely pointing to the fact that this is a transient state. Overall this study serves as a resource to determine the best cell lines to utilize for ovarian cancer research on certain subtypes and platinum response studies, as well as sparks new hypotheses for future study in ovarian cancer.

Detecting HRD in whole-genome and whole-exome sequenced breast and ovarian cancers

Breast and ovarian cancers harboring homologous recombination deficiency (HRD) are sensitive to PARP inhibitors and platinum chemotherapy. Conventionally, detecting HRD involves screening for defects in BRCA1, BRCA2, and other relevant genes. Recent analyses have shown that HRD cancers exhibit characteristic mutational patterns due to the activities of HRD-associated mutational signatures. At least three machine learning tools exist for detecting HRD based on mutational patterns. Here, using sequencing data from 1,043 breast and 182 ovarian cancers, we trained Homologous Recombination Proficiency Profiler (HRProfiler), a machine learning method for detecting HRD using six mutational features. HRProfiler's performance is assessed against prior approaches using additional independent datasets of 417 breast and 115 ovarian cancers, including retrospective data from a clinical trial involving patients treated with PARP inhibitors. Our results demonstrate that HRProfiler is the only tool that robustly and consistently predicts clinical response from whole-exome sequenced breast and ovarian cancers.

Protein and mRNA Expression in Uveal Melanoma Cell Lines Are Related to GNA and BAP1 Mutation Status

Purpose

Cell lines are being used in preclinical uveal melanoma (UM) research. Because not all cell lines harbor typical GNAQ or GNA11 hotspot mutations, we aimed at better classifying them and determining whether we could find genetic causes to explain the protein and mRNA expression profiles of the cell lines.

Methods

We studied protein and mRNA expression of 14 UM cell lines and determined the presence of single nucleotide variants and small insertions and deletions with next-generation sequencing and copy number alterations with a single nucleotide polymorphism array. The lists of differentially expressed proteins and genes were merged, and shared lists were created, keeping only terms with concordant mRNA and protein expression. Enrichment analyses were performed on the shared lists.

Results

Cell lines Mel285 and Mel290 are separate from GNA-mutated cell lines and show downregulation of melanosome-related markers. Both lack typical UM mutations but each harbors four putatively deleterious variants in CTNNB1, PPP1R10, LIMCH1, and APC in Mel285 and ARID1A, PPP1R10, SPG11, and RNF43 in Mel290. The upregulated terms in Mel285 and Mel290 did not point to a convincing alternative origin. Mel285 shows loss of chromosomes 1p, 3p, partial 3q, 6, and partial 8p, whereas Mel290 shows loss of 1p and 6. Expression in the other 12 cell lines was related to BAP1 expression.

Conclusions

Although Mel285 and Mel290 have copy number alterations that fit UM, multi-omics analyses show that they belong to a separate group compared to the other analyzed UM cell lines. Therefore, they may not be representative models to test potential therapeutic targets for UM.

Constitutional mismatch repair deficiency mimicking Lynch syndrome is associated with hypomorphic mismatch repair gene variants

Lynch syndrome (LS) and constitutional mismatch repair deficiency (CMMRD) are distinct cancer syndromes caused, respectively, by mono- and bi-allelic germline mismatch repair (MMR) variants. LS predisposes to mainly gastrointestinal and genitourinary cancers in adulthood. CMMRD predisposes to brain, haematological, and LS-spectrum cancers from childhood. Two suspected LS patients with first cancer diagnosis aged 27 or 38 years were found to be homozygous for an MMR (likely) pathogenic variant, MSH6 c.3226C>T (p.(Arg1076Cys)), or variant of uncertain significance (VUS), MLH1 c.306G>A (p.(Glu102=)). MLH1 c.306G>A was shown to cause leaky exon 3 skipping. The apparent genotype-phenotype conflict was resolved by detection of constitutional microsatellite instability in both patients, a hallmark feature of CMMRD. A hypomorphic effect of these and other variants found in additional late onset CMMRD cases, identified by literature review, likely explains a LS-like phenotype. CMMRD testing in carriers of compound heterozygous or homozygous MMR VUS may find similar cases and novel hypomorphic variants. Individualised management of mono- and bi-allelic carriers of hypomorphic MMR variants is needed until we better characterise the associated phenotypes.

Mutational signatures of colorectal cancers according to distinct computational workflows

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.

The mutagenic forces shaping the genomic landscape of lung cancer in never smokers

Lung cancer in never smokers (LCINS) accounts for up to 25% of all lung cancers and has been associated with exposure to secondhand tobacco smoke and air pollution in observational studies. Here, we evaluate the mutagenic exposures in LCINS by examining deep whole-genome sequencing data from a large international cohort of 871 treatment-naïve LCINS recruited from 28 geographical locations within the Sherlock-Lung study. KRAS mutations were 3.8-fold more common in adenocarcinomas of never smokers from North America and Europe, while a 1.6-fold higher prevalence of EGFR and TP53 mutations was observed in adenocarcinomas from East Asia. Signature SBS40a, with unknown cause, was found in most samples and accounted for the largest proportion of single base substitutions in adenocarcinomas, being enriched in EGFR-mutated cases. Conversely, the aristolochic acid signature SBS22a was almost exclusively observed in patients from Taipei. Even though LCINS exposed to secondhand smoke had an 8.3% higher mutational burden and 5.4% shorter telomeres, passive smoking was not associated with driver mutations in cancer driver genes or the activities of individual mutational signatures. In contrast, patients from regions with high levels of air pollution were more likely to have TP53 mutations while exhibiting shorter telomeres and an increase in most types of somatic mutations, including a 3.9-fold elevation of signature SBS4 (q-value=3.1 × 10-5), previously linked mainly to tobacco smoking, and a 76% increase of clock-like signature SBS5 (q-value=5.0 × 10-5). A positive dose-response effect was observed with air pollution levels, which correlated with both a decrease in telomere length and an elevation in somatic mutations, notably attributed to signatures SBS4 and SBS5. Our results elucidate the diversity of mutational processes shaping the genomic landscape of lung cancer in never smokers.

G-Quadruplex Forming DNA Sequence Context Is Enriched around Points of Somatic Mutations in a Subset of Multiple Myeloma Patients

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.

Geographic variation of mutagenic exposures in kidney cancer genomes

International differences in the incidence of many cancer types indicate the existence of carcinogen exposures that have not yet been identified by conventional epidemiology make a substantial contribution to cancer burden1. In clear cell renal cell carcinoma, obesity, hypertension and tobacco smoking are risk factors, but they do not explain the geographical variation in its incidence2. Underlying causes can be inferred by sequencing the genomes of cancers from populations with different incidence rates and detecting differences in patterns of somatic mutations. Here we sequenced 962 clear cell renal cell carcinomas from 11 countries with varying incidence. The somatic mutation profiles differed between countries. In Romania, Serbia and Thailand, mutational signatures characteristic of aristolochic acid compounds were present in most cases, but these were rare elsewhere. In Japan, a mutational signature of unknown cause was found in more than 70% of cases but in less than 2% elsewhere. A further mutational signature of unknown cause was ubiquitous but exhibited higher mutation loads in countries with higher incidence rates of kidney cancer. Known signatures of tobacco smoking correlated with tobacco consumption, but no signature was associated with obesity or hypertension, suggesting that non-mutagenic mechanisms of action underlie these risk factors. The results of this study indicate the existence of multiple, geographically variable, mutagenic exposures that potentially affect tens of millions of people and illustrate the opportunities for new insights into cancer causation through large-scale global cancer genomics.

Evaluating topography of mutational signatures with SigProfilerTopography

The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genome's architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.