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.

The SMC5/6 complex prevents genotoxicity upon APOBEC3A-mediated replication stress.

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.

Topography of mutational signatures in non-small cell lung cancer: emerging concepts, clinical applications, and limitations.

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.

Cancer mutational signatures identification in clinical assays using neural embedding-based representations.

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.

The Association between Mutational Signatures and Clinical Outcomes among Patients with Early-Onset Breast Cancer.

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.

KMT2A Mutations and High Prevalence of dMMR-associated Mutational Signatures as Prognostic Indicators in Metastatic Colorectal Cancer.

The conventional treatment strategies for patients with metastatic colorectal cancer (mCRC) are predominantly guided by the status of RAS and BRAF mutations. Although patients may exhibit analogous pathological characteristics and undergo similar treatment regimens, notable disparities in their prognostic outcomes can be observed. Therefore, tissue and plasma samples from 40 mCRC patients underwent next-generation sequencing targeting 425 cancer-relevant genes. Genomic variations and canonical oncogenic pathways were investigated for their prognostic effects in association with progression-free survival (PFS) of these patients. We found that patients with BRCA2 and KMT2A mutations exhibited worse prognostic outcomes after chemotherapy-based treatment (univariate, P < 0.01). Further pathway analysis indicated that alterations in the homologous recombination pathway and in the KMT2A signaling network were also significantly associated with shortened PFS (univariate, P < 0.01). Additionally, mutation signature analysis showed that patients with higher proportions of defective mismatch repair (dMMR)-related mutational signatures. Had a worse prognosis (univariate, P = 0.02). KMT2A mutations (hazard ratio [HR], 4.47; 95% confidence interval [CI], 1-19.93; P =0.050) and dMMR signature proportions (HR, 3.57; 95% CI, 1.42-8.96; P = 0.007) remained independently associated with PFS after multivariate analysis and the results were further externally validated. These findings may enhance our understanding of this disease and may potentially facilitate the optimization of its treatment approaches.

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.

Insight into NSCLC through novel analysis of gene interactions and characteristics.

Around 80 to 85% of all lung cancers are non-small cell lung cancer (NSCLC). Previous research has aimed at exploring the genetic basis of NSCLC through individual approaches, but studies have yet to investigate the results of combining them. Here we show that analyzing NSCLC genetics through three approaches simultaneously creates unique insights into our understanding of the disease. Through a combination of previous research and bioinformatics tools, we determined 35 NSCLC candidate genes. We analyzed these genes in 3 different approaches. First, we found the gene fusions between these candidate genes. Second, we found the common superfamilies between genes. Finally, we identified mutational signatures that are possibly associated with NSCLC. Each approach has its individual, unique results. Fusion relationships identify specific gene fusion targets, common superfamilies identify possible avenues to determine novel target genes, and identifying NSCLC associated mutational signatures has diagnostic and prognostic benefits. Combining the approaches, we found that gene CD74 has significant fusion relationships, but it has no association with the other two approaches, suggesting that CD74 is associated with NSCLC mainly because of its fusion relationships. Targeting the gene fusions of CD74 may be an alternative NSCLC treatment. This genetic analysis has indeed created unique insight into NSCLC genes. Both the results from each of the approaches separately and combined allow pursuit of more effective treatment strategies for this cancer. The methodology presented can also apply to other cancers, creating insights that current analytical methods could not find.

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.

Quantitative and systematic NMR measurements of sequence-dependent A-T Hoogsteen dynamics uncovers unique conformational specificity in the DNA double helix.

The propensities to form lowly-populated short-lived conformations of DNA could vary with sequence, providing an important source of sequence-specificity in biochemical reactions. However, comprehensively measuring how these dynamics vary with sequence is challenging. Using 1H CEST and 13C R1ρ NMR, we measured Watson-Crick to Hoogsteen dynamics for an A-T base pair in thirteen trinucleotide sequence contexts. The Hoogsteen population and exchange rate varied 4-fold and 16-fold, respectively, and were dependent on both the 3'- and 5'-neighbors but only weakly dependent on monovalent ion concentration (25 versus 100 mM NaCl) and pH (6.8 versus 8.0). Flexible TA and CA dinucleotide steps exhibited the highest Hoogsteen populations, and their kinetics rates strongly depended on the 3'-neighbor. In contrast, the stiffer AA and GA steps had the lowest Hoogsteen population, and their kinetics were weakly dependent on the 3'-neighbor. The Hoogsteen lifetime was especially short when G-C neighbors flanked the A-T base pair. The Hoogsteen dynamics had a distinct sequence-dependence compared to duplex stability and minor groove width. Thus, our results uncover a unique source of sequence-specificity hidden within the DNA double helix in the form of A-T Hoogsteen dynamics and establish the utility of 1H CEST to quantitively measure sequence-dependent DNA dynamics.

Flexible model-based non-negative matrix factorization with application to mutational signatures.

Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized using either simple mono-nucleotide interaction models or general tri-nucleotide interaction models. We describe a flexible and novel framework for identifying biologically plausible parametrizations of mutational signatures, and in particular for estimating di-nucleotide interaction models. Our novel estimation procedure is based on the expectation-maximization (EM) algorithm and regression in the log-linear quasi-Poisson model. We show that di-nucleotide interaction signatures are statistically stable and sufficiently complex to fit the mutational patterns. Di-nucleotide interaction signatures often strike the right balance between appropriately fitting the data and avoiding over-fitting. They provide a better fit to data and are biologically more plausible than mono-nucleotide interaction signatures, and the parametrization is more stable than the parameter-rich tri-nucleotide interaction signatures. We illustrate our framework in a large simulation study where we compare to state of the art methods, and show results for three data sets of somatic mutation counts from patients with cancer in the breast, Liver and urinary tract.

Molecular profiling of primary endometrioid endometrial cancer and matched lung metastases: CTNNB1 mutation as a potential driver.

•Both primary endometrial cancers (ECs) and matched lung metastases shared a common ancestor with independent evolution at each site.•The two endometrioid ECs studied acquired additional mutations during the distant metastatic process.•Subclonal CTNNB1 hotspot mutations in the two primary ECs studied became clonal in the distant metastases.

Genomic landscape of diploid and aneuploid microsatellite stable early onset colorectal cancer.

Although colorectal cancer (CRC) remains the second leading cause of cancer-related death in the United States, the overall incidence and mortality from the disease have declined in recent decades. In contrast, there has been a steady increase in the incidence of CRC in individuals under 50 years of age. Hereditary syndromes contribute disproportionately to early onset CRC (EOCRC). These include microsatellite instability high (MSI+) tumors arising in patients with Lynch Syndrome. However, most EOCRCs are not associated with familial syndromes or MSI+ genotypes. Comprehensive genomic profiling has provided the basis of improved more personalized treatments for older CRC patients. However, less is known about the basis of sporadic EOCRC. To define the genomic landscape of EOCRC we used DNA content flow sorting to isolate diploid and aneuploid tumor fractions from 21 non-hereditary cases. We then generated whole exome mutational profiles for each case and whole genome copy number, telomere length, and EGFR immunohistochemistry (IHC) analyses on subsets of samples. These results discriminate the molecular features of diploid and aneuploid EOCRC and provide a basis for larger population-based studies and the development of effective strategies to monitor and treat this emerging disease.

Improved detection of colibactin-induced mutations by genotoxic E. coli in organoids and colorectal cancer.

Co-culture of intestinal organoids with a colibactin-producing pks+E. coli strain (EcC) revealed mutational signatures also found in colorectal cancer (CRC). E. coli Nissle 1917 (EcN) remains a commonly used probiotic, despite harboring the pks operon and inducing double strand DNA breaks. We determine the mutagenicity of EcN and three CRC-derived pks+E. coli strains with an analytical framework based on sequence characteristic of colibactin-induced mutations. All strains, including EcN, display varying levels of mutagenic activity. Furthermore, a machine learning approach attributing individual mutations to colibactin reveals that patients with colibactin-induced mutations are diagnosed at a younger age and that colibactin can induce a specific APC mutation. These approaches allow the sensitive detection of colibactin-induced mutations in ∼12% of CRC genomes and even in whole exome sequencing data, representing a crucial step toward pinpointing the mutagenic activity of distinct pks+E. coli strains.

Somatic mutations in aging and disease.

Time always leaves its mark, and our genome is no exception. Mutations in the genome of somatic cells were first hypothesized to be the cause of aging in the 1950s, shortly after the molecular structure of DNA had been described. Somatic mutation theories of aging are based on the fact that mutations in DNA as the ultimate template for all cellular functions are irreversible. However, it took until the 1990s to develop the methods to test if DNA mutations accumulate with age in different organs and tissues and estimate the severity of the problem. By now, numerous studies have documented the accumulation of somatic mutations with age in normal cells and tissues of mice, humans, and other animals, showing clock-like mutational signatures that provide information on the underlying causes of the mutations. In this review, we will first briefly discuss the recent advances in next-generation sequencing that now allow quantitative analysis of somatic mutations. Second, we will provide evidence that the mutation rate differs between cell types, with a focus on differences between germline and somatic mutation rate. Third, we will discuss somatic mutational signatures as measures of aging, environmental exposure, and activities of DNA repair processes. Fourth, we will explain the concept of clonally amplified somatic mutations, with a focus on clonal hematopoiesis. Fifth, we will briefly discuss somatic mutations in the transcriptome and in our other genome, i.e., the genome of mitochondria. We will end with a brief discussion of a possible causal contribution of somatic mutations to the aging process.

Mutational Signatures in Wild Type Escherichia coli Strains Reveal Predominance of DNA Polymerase Errors.

While mutational processes operating in the Escherichia coli genome have been revealed by multiple laboratory experiments, the contribution of these processes to accumulation of bacterial polymorphism and evolution in natural environments is unknown. To address this question, we reconstruct signatures of distinct mutational processes from experimental data on E. coli hypermutators, and ask how these processes contribute to differences between naturally occurring E. coli strains. We show that both mutations accumulated in the course of evolution of wild-type strains in nature and in the lab-grown nonmutator laboratory strains are explained predominantly by the low fidelity of DNA polymerases II and III. By contrast, contributions specific to disruption of DNA repair systems cannot be detected, suggesting that temporary accelerations of mutagenesis associated with such disruptions are unimportant for within-species evolution. These observations demonstrate that accumulation of diversity in bacterial strains in nature is predominantly associated with errors of DNA polymerases.

Monitoring APOBEC3A protein levels in human cancer cells.

The APOBEC3 family of cytosine deaminases, which target single-stranded DNA and RNA of viruses and retroelements as part of the innate immune defense, generate mutations in many human cancers. Although the APOBEC3A paralog is a major endogenous source of these mutations, low APOBEC3A mRNA levels and protein abundance have hampered functional characterization. Extensive homology across APOBEC3 paralogs have further challenged the development of specific detection reagents. Here, we describe the isolation and use of monoclonal antibodies with specificity for APOBEC3A and the APOBEC3A/APOBEC3B/APOBEC3G proteins. We provide protocols and technical advice for detection and measurement of APOBEC3A protein across human cancer cell lines using standard immunoblotting and immunofluorescence protocols.

A review on trends in development and translation of omics signatures in cancer.

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Current and emerging sequencing-based tools for precision cancer medicine.

Current precision cancer medicine is dependent on the analyses of a plethora of clinically relevant genomic aberrations. During the last decade, next-generation sequencing (NGS) has gradually replaced most other methods for precision cancer diagnostics, spanning from targeted tumor-informed assays and gene panel sequencing to global whole-genome and whole-transcriptome sequencing analyses. The shift has been impelled by a clinical need to assess an increasing number of genomic alterations with diagnostic, prognostic and predictive impact, including more complex biomarkers (e.g. microsatellite instability, MSI, and homologous recombination deficiency, HRD), driven by the parallel development of novel targeted therapies and enabled by the rapid reduction in sequencing costs. This review focuses on these sequencing-based methods, puts their emergence in a historic perspective, highlights their clinical utility in diagnostics and decision-making in pediatric and adult cancer, as well as raises challenges for their clinical implementation. Finally, the importance of applying sensitive tools for longitudinal monitoring of treatment response and detection of measurable residual disease, as well as future avenues in the rapidly evolving field of sequencing-based methods are discussed.

Comprehensive genomic and clinical analyses identify APOBEC mutational signatures as a brain metastasis risk factor in lung adenocarcinoma patients.

BACKGROUND: Lung adenocarcinoma is the most common source of brain metastasis (BM), resulting in significant morbidity and mortality. We aimed to identify patients with high BM risk who possibly benefit from brain-penetrant drugs, prophylactic cranial irradiation, or close brain magnetic resonance imaging surveillance. METHODS: Metastatic lung adenocarcinoma patients with extracranial tumor samples profiled by a next-generation sequencing panel targeting 425 tumor-related genes were retrospectively enrolled between February 2008 and July 2021. We compared BM and non-BM patients' genomic and clinical features and studied their associations with BM risk. Two external cohorts were used for result validation and molecular mechanisms investigation, respectively. RESULTS: We included 174 eligible patients, including 90 having developed BM by the end of follow-up. Age≤60, EGFR activating mutations, and high-level apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC) mutational signatures were associated with elevated BM risk. Similar findings in BM-free survival were obtained by fitting Fine-Gray subdistribution hazard models addressing competing risks. Increased BM risk related to APOBEC mutational signatures was validated in an external cohort (N = 440). RNA sequencing data analyses performed in another external cohort (N = 230) revealed that expressions of metastasis-related pathways such as transforming growth factor (TGF)ß and epithelial-mesenchymal transition (EMT) were upregulated in the patients with high-level APOBEC mutational signatures. CONCLUSION: APOBEC mutational signatures related to upregulated TGFß and EMT, could serve as an independent risk factor for BM and BM-free survival in metastatic lung adenocarcinoma patients. Further investigations are warranted to tailor personalized treatments to improve the susceptible patient's outcomes.