Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

Paired box proteins as diagnostic biomarkers for endocervical adenocarcinoma

In this editorial, we commented on the article by Akers et al published in the recent issue of the World Journal of Clinical Cases. We focused specifically on the role of the transcription factor paired box protein 8 (PAX8) belonging to the family PAX in the carcinogenesis of a gynecologic tumor, endocervical adenocarcinoma, arising from the tissue of mesonephric origin, and the potential diagnostic value for the same type of neoplasms. The global vaccination program of human papillomavirus (HPV) has dramatically reduced the incidence of cervical cancer, including cases of adenocarcinoma. The type of adenoid epithelial origin has a lower frequency of HPV detection but tends to be more aggressive and fatal. Cases of endocervical adenocarcinoma occurring in females of menopause age have been described in the 2023 volume of the World Journal of Clinical Cases and in our study recently published in Oncol Lett. The histopathological findings and immunohistochemical assays showed that the lesions had glandular morphology, and the specimens in these two reports were immunohistochemically positive for the transcription factor PAX8, albeit that they had opposing expression profiles of tumor suppressor p16 and estrogen receptor and the presence of the HPV genome. The presence of a mucin protein, MUC 5AC, as revealed in both studies suggested target molecules for the diagnosis of mucinous adenoid type of uterine tumor and other histological origins. The clinical outcome was unfavorable due to metastasis and recurrence. This prompted the improvement of the antitumor modality, with the introduction of precise targeting therapy. Mucin has now been reported to be the therapeutic target for adenocarcinomas.

MotifbreakR v2: extended capability and database integration

MotifbreakR is a software tool that scans genetic variants against position weight matrices of transcription factors (TF) to determine the potential for the disruption of TF binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to operate across a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single nucleotide variants (common and rare SNVs) on potential TF binding sites, in motifbreakR v2, we have updated the functionality. New features include the ability to query other types of more complex genetic variants, such as short insertions and deletions (indels). This function allows modeling a more extensive array of variants that may have more significant effects on TF binding. Additionally, while TF binding is based partly on sequence preference, predictions of TF binding based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR implements querying the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we have implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.

Implications of noncoding regulatory functions in the development of insulinomas

Insulinomas are rare neuroendocrine tumors arising from pancreatic β cells, characterized by aberrant proliferation and altered insulin secretion, leading to glucose homeostasis failure. With the aim of uncovering the role of noncoding regulatory regions and their aberrations in the development of these tumors, we coupled epigenetic and transcriptome profiling with whole-genome sequencing. As a result, we unraveled somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development, and epigenetic modifying genes, including polycomb complex components. Chromatin remodeling is apparent in insulinoma-selective domains shared across patients, containing a specific set of regulatory sequences dominated by the SOX17 binding motif. Moreover, many of these regions are H3K27me3 repressed in β cells, suggesting that tumoral transition involves derepression of polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements affecting the function and fate of β cells in their progression to insulinomas and a framework to identify coding and noncoding driver mutations.

Hidden secrets of the cancer genome: unlocking the impact of non-coding mutations in gene regulatory elements

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely "junk DNA", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

Targeting PAX8 sensitizes ovarian cancer cells to ferroptosis by inhibiting glutathione synthesis

Ovarian cancer is a malignant tumor originating from the ovary, characterized by its high mortality rate and propensity for recurrence. In some patients, especially those with recurrent cancer, conventional treatments such as surgical resection or standard chemotherapy yield suboptimal results. Consequently, there is an urgent need for novel anti-cancer therapeutic strategies. Ferroptosis is a distinct form of cell death separate from apoptosis. Ferroptosis inducers have demonstrated promising potential in the treatment of ovarian cancer, with evidence indicating their ability to enhance ovarian cancer cell sensitivity to cisplatin. However, resistance of cancer cells to ferroptosis still remains an inevitable challenge. Here, we analyzed genome-scale CRISPR-Cas9 loss-of function screens and identified PAX8 as a ferroptosis resistance protein in ovarian cancer. We identified PAX8 as a susceptibility gene in GPX4-dependent ovarian cancer. Depletion of PAX8 rendered GPX4-dependent ovarian cancer cells significantly more sensitive to GPX4 inhibitors. Additionally, we found that PAX8 inhibited ferroptosis in ovarian cancer cells. Combined treatment with a PAX8 inhibitor and RSL3 suppressed ovarian cancer cell growth, induced ferroptosis, and was validated in a xenograft mouse model. Further exploration of the molecular mechanisms underlying PAX8 inhibition of ferroptosis mutations revealed upregulation of glutamate-cysteine ligase catalytic subunit (GCLC) expression. GCLC mediated the ferroptosis resistance induced by PAX8 in ovarian cancer. In conclusion, our study underscores the pivotal role of PAX8 as a therapeutic target in GPX4-dependent ovarian cancer. The combination of PAX8 inhibitors such as losartan and captopril with ferroptosis inducers represents a promising new approach for ovarian cancer therapy.

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Detecting and understanding meaningful cancerous mutations based on computational models of mRNA splicing

Cancer research has long relied on non-silent mutations. Yet, it has become overwhelmingly clear that silent mutations can affect gene expression and cancer cell fitness. One fundamental mechanism that apparently silent mutations can severely disrupt is alternative splicing. Here we introduce Oncosplice, a tool that scores mutations based on models of proteomes generated using aberrant splicing predictions. Oncosplice leverages a highly accurate neural network that predicts splice sites within arbitrary mRNA sequences, a greedy transcript constructor that considers alternate arrangements of splicing blueprints, and an algorithm that grades the functional divergence between proteins based on evolutionary conservation. By applying this tool to 12M somatic mutations we identify 8K deleterious variants that are significantly depleted within the healthy population; we demonstrate the tool's ability to identify clinically validated pathogenic variants with a positive predictive value of 94%; we show strong enrichment of predicted deleterious mutations across pan-cancer drivers. We also achieve improved patient survival estimation using a proposed set of novel cancer-involved genes. Ultimately, this pipeline enables accelerated insight-gathering of sequence-specific consequences for a class of understudied mutations and provides an efficient way of filtering through massive variant datasets - functionalities with immediate experimental and clinical applications.

Leveraging Tissue-Specific Enhancer-Target Gene Regulatory Networks Identifies Enhancer Somatic Mutations That Functionally Impact Lung Cancer

Enhancers are noncoding regulatory DNA regions that modulate the transcription of target genes, often over large distances along with the genomic sequence. Enhancer alterations have been associated with various pathological conditions, including cancer. However, the identification and characterization of somatic mutations in noncoding regulatory regions with a functional effect on tumorigenesis and prognosis remain a major challenge. Here, we present a strategy for detecting and characterizing enhancer mutations in a genome-wide analysis of patient cohorts, across three lung cancer subtypes. Lung tissue-specific enhancers were defined by integrating experimental data and public epigenomic profiles, and the genome-wide enhancer-target gene regulatory network of lung cells was constructed by integrating chromatin three-dimensional architecture data. Lung cancers possessed a similar mutation burden at tissue-specific enhancers and exons but with differences in their mutation signatures. Functionally relevant alterations were prioritized on the basis of the pathway-level integration of the effect of a mutation and the frequency of mutations on individual enhancers. The genes enriched for mutated enhancers converged on the regulation of key biological processes and pathways relevant to tumor biology. Recurrent mutations in individual enhancers also affected the expression of target genes, with potential relevance for patient prognosis. Together, these findings show that noncoding regulatory mutations have a potential relevance for cancer pathogenesis and can be exploited for patient classification.

SIGNIFICANCE

Mapping enhancer-target gene regulatory interactions and analyzing enhancer mutations at the level of their target genes and pathways reveal convergence of recurrent enhancer mutations on biological processes involved in tumorigenesis and prognosis.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes

Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer. Here, we perform single-cell CRISPRi screens of 3,512 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of >500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of variant-associated enhancers disrupts breast cancer gene programs. We observe variant-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple variant-associated enhancers indirectly regulate TP53. Comparative studies illustrate sub-type specific functions between enhancers in ER+ and ER- cells. Finally, we developed the pySpade package to facilitate analysis of single-cell enhancer screens. Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age

BACKGROUND

Despite previous research findings on higher risks of stillbirth among pregnant individuals with SARS-CoV-2 infection, it is unclear whether the gestational timing of viral infection modulates this risk.

OBJECTIVE

This study aimed to examine the association between timing of SARS-CoV-2 infection during pregnancy and risk of stillbirth.

STUDY DESIGN

This retrospective cohort study used multilevel logistic regression analyses of nationwide electronic health records in the United States. Data were from 75 healthcare systems and institutes across 50 states. A total of 191,403 pregnancies of 190,738 individuals of reproductive age (15-49 years) who had childbirth between March 1, 2020 and May 31, 2021 were identified and included. The main outcome was stillbirth at ≥20 weeks of gestation. Exposures were the timing of SARS-CoV-2 infection: early pregnancy (<20 weeks), midpregnancy (21-27 weeks), the third trimester (28-43 weeks), any time before delivery, and never infected (reference).

RESULTS

We identified 2342 (1.3%) pregnancies with COVID-19 in early pregnancy, 2075 (1.2%) in midpregnancy, and 12,697 (6.9%) in the third trimester. After adjusting for maternal and clinical characteristics, increased odds of stillbirth were observed among pregnant individuals with SARS-CoV-2 infection only in early pregnancy (odds ratio, 1.75, 95% confidence interval, 1.25-2.46) and midpregnancy (odds ratio, 2.09; 95% confidence interval, 1.49-2.93), as opposed to pregnant individuals who were never infected. Older age, Black race, hypertension, acute respiratory distress syndrome or acute respiratory failure, and placental abruption were found to be consistently associated with stillbirth across different trimesters.

CONCLUSION

Increased risk of stillbirth was associated with COVID-19 only when pregnant individuals were infected during early and midpregnancy, and not at any time before the delivery or during the third trimester, suggesting the potential vulnerability of the fetus to SARS-CoV-2 infection in early pregnancy. Our findings underscore the importance of proactive COVID-19 prevention and timely medical intervention for individuals infected with SARS-CoV-2 during early and midpregnancy.

Tumour mutations in long noncoding RNAs enhance cell fitness

Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.

CenhANCER: a comprehensive cancer enhancer database for primary tissues and cell lines

Enhancers, which are key tumorigenic factors with wide applications for subtyping, diagnosis and treatment of cancer, are attracting increasing attention in the cancer research. However, systematic analysis of cancer enhancers poses a challenge due to the lack of integrative data resources, especially those from tumor primary tissues. To provide a comprehensive enhancer profile across cancer types, we developed a cancer enhancer database CenhANCER by curating public resources including all the public H3K27ac ChIP-Seq data from 805 primary tissue samples and 671 cell line samples across 41 cancer types. In total, 57 029 408 typical enhancers, 978 411 super-enhancers and 226 726 enriched transcription factors were identified. We annotated the super-enhancers with chromatin accessibility regions, cancer expression quantitative trait loci (eQTLs), genotype-tissue expression eQTLs and genome-wide association study risk single nucleotide polymorphisms (SNPs) for further functional analysis. The identified enhancers were highly consistent with accessible chromatin regions in the corresponding cancer types, and all the 10 super-enhancer regions identified from one colorectal cancer study were recapitulated in our CenhANCER, both of which testified the high quality of our data. CenhANCER with high-quality cancer enhancer candidates and transcription factors that are potential therapeutic targets across multiple cancer types provides a credible resource for single cancer analysis and for comparative studies of various cancer types. Database URL http://cenhancer.chenzxlab.cn/.

In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer

BACKGROUND

Epigenomic dysregulation has been linked to solid tumour malignancies, including ovarian cancers. Profiling of re-programmed enhancer locations associated with disease has the potential to improve stratification and thus therapeutic choices. Ovarian cancers are subdivided into histological subtypes that have significant molecular and clinical differences, with high-grade serous carcinoma representing the most common and aggressive subtype.

METHODS

We interrogated the enhancer landscape(s) of normal ovary and subtype-specific ovarian cancer states using publicly available data. With an initial focus on H3K27ac histone mark, we developed a computational pipeline to predict drug compound activity based on epigenomic stratification. Lastly, we substantiated our predictions in vitro using patient-derived clinical samples and cell lines.

RESULTS

Using our in silico approach, we highlighted recurrent and privative enhancer landscapes and identified the differential enrichment of a total of 164 transcription factors involved in 201 protein complexes across the subtypes. We pinpointed SNS-032 and EHMT2 inhibitors BIX-01294 and UNC0646 as therapeutic candidates in high-grade serous carcinoma, as well as probed the efficacy of specific inhibitors in vitro.

CONCLUSION

Here, we report the first attempt to exploit ovarian cancer epigenomic landscapes for drug discovery. This computational pipeline holds enormous potential for translating epigenomic profiling into therapeutic leads.

Rewiring of master transcription factor cistromes during high-grade serous ovarian cancer development

The transcription factors MECOM, PAX8, SOX17 and WT1 are candidate master regulators of high-grade serous 'ovarian' cancer (HGSC), yet their cooperative role in the hypothesized tissue of origin, the fallopian tube secretory epithelium (FTSEC) is unknown. We generated 26 epigenome (CUT&TAG, CUT&RUN, ATAC-seq and HiC) data sets and 24 profiles of RNA-seq transcription factor knock-down followed by RNA sequencing in FTSEC and HGSC models to define binding sites and gene sets regulated by these factors in cis and trans. This revealed that MECOM, PAX8, SOX17 and WT1 are lineage-enriched, super-enhancer associated master regulators whose cooperative DNA-binding patterns and target genes are re-wired during tumor development. All four TFs were indispensable for HGSC clonogenicity and survival but only depletion of PAX8 and WT1 impaired FTSEC cell survival. These four TFs were pharmacologically inhibited by transcriptional inhibitors only in HGSCs but not in FTSECs. Collectively, our data highlights that tumor-specific epigenetic remodeling is tightly related to MECOM, PAX8, SOX17 and WT1 activity and these transcription factors are targetable in a tumor-specific manner through transcriptional inhibitors.

Investigating the suitability of in vitro cell lines as models for the major subtypes of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the most fatal gynaecological malignancy, accounting for over 200,000 deaths worldwide per year. EOC is a highly heterogeneous disease, classified into five major histological subtypes-high-grade serous (HGSOC), clear cell (CCOC), endometrioid (ENOC), mucinous (MOC) and low-grade serous (LGSOC) ovarian carcinomas. Classification of EOCs is clinically beneficial, as the various subtypes respond differently to chemotherapy and have distinct prognoses. Cell lines are often used as in vitro models for cancer, allowing researchers to explore pathophysiology in a relatively cheap and easy to manipulate system. However, most studies that make use of EOC cell lines fail to recognize the importance of subtype. Furthermore, the similarity of cell lines to their cognate primary tumors is often ignored. Identification of cell lines with high molecular similarity to primary tumors is needed in order to better guide pre-clinical EOC research and to improve development of targeted therapeutics and diagnostics for each distinctive subtype. This study aims to generate a reference dataset of cell lines representative of the major EOC subtypes. We found that non-negative matrix factorization (NMF) optimally clustered fifty-six cell lines into five groups, putatively corresponding to each of the five EOC subtypes. These clusters validated previous histological groupings, while also classifying other previously unannotated cell lines. We analysed the mutational and copy number landscapes of these lines to investigate whether they harboured the characteristic genomic alterations of each subtype. Finally we compared the gene expression profiles of cell lines with 93 primary tumor samples stratified by subtype, to identify lines with the highest molecular similarity to HGSOC, CCOC, ENOC, and MOC. In summary, we examined the molecular features of both EOC cell lines and primary tumors of multiple subtypes. We recommend a reference set of cell lines most suited to represent four different subtypes of EOC for both in silico and in vitro studies. We also identify lines displaying poor overall molecular similarity to EOC tumors, which we argue should be avoided in pre-clinical studies. Ultimately, our work emphasizes the importance of choosing suitable cell line models to maximise clinical relevance of experiments.

A promising new cancer marker: Long noncoding RNA EGFR-AS1

Cancer consists of a group of diseases with the salient properties of an uncontrolled cell cycle, metastasis, and evasion of the immune response, mainly driven by the genomic instability of somatic cells and the physicochemical environment. Long noncoding RNAs (lncRNAs) are defined as noncoding RNAs with a length of more than 200 nucleotides. LncRNA dysregulation participates in diverse disease types and is tightly associated with patient clinical features, such as age, disease stage, and prognosis. In addition, an increasing number of lncRNAs have been confirmed to regulate a series of biological and pathological processes through numerous mechanisms. The lncRNA epidermal growth factor receptor antisense RNA 1 (EGFR-AS1) was recently discovered to be aberrantly expressed in many types of diseases, particularly in cancers. A high level of EGFR-AS1 was demonstrated to correlate with multiple patient clinical characteristics. More importantly, EGFR-AS1 was found to be involved in the mediation of various cellular activities, including cell proliferation, invasion, migration, chemosensitivity, and stemness. Therefore, EGFR-AS1 is a promising marker for cancer management. In this review, we introduce the expression profile, molecular mechanisms, biological functions, and clinical value of EGFR-AS1 in cancers.

Molecular Management of High-Grade Serous Ovarian Carcinoma

High-grade serous ovarian carcinoma (HGSOC) represents the most common form of epithelial ovarian carcinoma. The absence of specific symptoms leads to late-stage diagnosis, making HGSOC one of the gynecological cancers with the worst prognosis. The cellular origin of HGSOC and the role of reproductive hormones, genetic traits (such as alterations in P53 and DNA-repair mechanisms), chromosomal instability, or dysregulation of crucial signaling pathways have been considered when evaluating prognosis and response to therapy in HGSOC patients. However, the detection of HGSOC is still based on traditional methods such as carbohydrate antigen 125 (CA125) detection and ultrasound, and the combined use of these methods has yet to support significant reductions in overall mortality rates. The current paradigm for HGSOC management has moved towards early diagnosis via the non-invasive detection of molecular markers through liquid biopsies. This review presents an integrated view of the relevant cellular and molecular aspects involved in the etiopathogenesis of HGSOC and brings together studies that consider new horizons for the possible early detection of this gynecological cancer.

chromMAGMA: regulatory element-centric interrogation of risk variants

Candidate causal risk variants from genome-wide association studies reside almost exclusively in noncoding regions of the genome and innovative approaches are necessary to understand their biological function. Multi-marker analysis of genomic annotation (MAGMA) is a widely used program that nominates candidate risk genes by mapping single-nucleotide polymorphism summary statistics from genome-wide association studies to gene bodies. We augmented MAGMA to create chromatin-MAGMA (chromMAGMA), a method to nominate candidate risk genes based on the presence of risk variants within noncoding regulatory elements (REs). We applied chromMAGMA to a genetic susceptibility dataset for epithelial ovarian cancer (EOC), a rare gynecologic malignancy characterized by high mortality. This identified 155 unique candidate EOC risk genes across five EOC histotypes; 83% (105/127) of high-grade serous ovarian cancer risk genes had not previously been implicated in this EOC histotype. Risk genes nominated by chromMAGMA converged on mRNA splicing and transcriptional dysregulation pathways. chromMAGMA is a pipeline that nominates candidate risk genes through a gene regulation-focused approach and helps interpret the biological mechanism of noncoding risk variants for complex diseases.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Recurrent noncoding somatic and germline WT1 variants converge to disrupt MYB binding in acute promyelocytic leukemia

Genetic alternations can occur at noncoding regions, but how they contribute to cancer pathogenesis is poorly understood. Here, we established a mutational landscape of cis-regulatory regions (CREs) in acute promyelocytic leukemia (APL) based on whole-genome sequencing analysis of paired tumor and germline samples from 24 patients and epigenetic profiling of 16 patients. Mutations occurring in CREs occur preferentially in active enhancers bound by the complex of master transcription factors in APL. Among significantly enriched mutated CREs, we found a recurrently mutated region located within the third intron of WT1, an essential regulator of normal and malignant hematopoiesis. Focusing on noncoding mutations within this WT1 intron, an analysis on 169 APL patients revealed that somatic mutations were clustered into a focal hotspot region, including one site identified as a germline polymorphism contributing to APL risk. Significantly decreased WT1 expression was observed in APL patients bearing somatic and/or germline noncoding WT1 variants. Furthermore, biallelic WT1 inactivation was recurrently found in APL patients with noncoding WT1 variants, which resulted in the complete loss of WT1. The high incidence of biallelic inactivation suggested the tumor suppressor activity of WT1 in APL. Mechanistically, noncoding WT1 variants disrupted MYB binding on chromatin and suppressed the enhancer activity and WT1 expression through destroying the chromatin looping formation. Our study highlights the important role of noncoding variants in the leukemogenesis of APL.

A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we utilize an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We systematically probe the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we construct chromatin interaction maps that enable the annotation of direct target genes for these super-enhancers and confirm their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach examines a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.

Exome sequencing of families from Ghana reveals known and candidate hearing impairment genes

We investigated hearing impairment (HI) in 51 families from Ghana with at least two affected members that were negative for GJB2 pathogenic variants. DNA samples from 184 family members underwent whole-exome sequencing (WES). Variants were found in 14 known non-syndromic HI (NSHI) genes [26/51 (51.0%) families], five genes that can underlie either syndromic HI or NSHI [13/51 (25.5%)], and one syndromic HI gene [1/51 (2.0%)]. Variants in CDH23 and MYO15A contributed the most to HI [31.4% (16/51 families)]. For DSPP, an autosomal recessive mode of inheritance was detected. Post-lingual expression was observed for a family segregating a MARVELD2 variant. To our knowledge, seven novel candidate HI genes were identified (13.7%), with six associated with NSHI (INPP4B, CCDC141, MYO19, DNAH11, POTEI, and SOX9); and one (PAX8) with Waardenburg syndrome. MYO19 and DNAH11 were replicated in unrelated Ghanaian probands. Six of the novel genes were expressed in mouse inner ear. It is known that Pax8-/- mice do not respond to sound, and depletion of Sox9 resulted in defective vestibular structures and abnormal utricle development. Most variants (48/60; 80.0%) have not previously been associated with HI. Identifying seven candidate genes in this study emphasizes the potential of novel HI genes discovery in Africa.

The transcription factor PAX8 promotes angiogenesis in ovarian cancer through interaction with SOX17

PAX8 is a master transcription factor that is essential during embryogenesis and promotes neoplastic growth. It is expressed by the secretory cells lining the female reproductive tract, and its deletion during development results in atresia of reproductive tract organs. Nearly all ovarian carcinomas express PAX8, and its knockdown results in apoptosis of ovarian cancer cells. To explore the role of PAX8 in these tissues, we purified the PAX8 protein complex from nonmalignant fallopian tube cells and high-grade serous ovarian carcinoma cell lines. We found that PAX8 was a member of a large chromatin remodeling complex and preferentially interacted with SOX17, another developmental transcription factor. Depleting either PAX8 or SOX17 from cancer cells altered the expression of factors involved in angiogenesis and functionally disrupted tubule and capillary formation in cell culture and mouse models. PAX8 and SOX17 in ovarian cancer cells promoted the secretion of angiogenic factors by suppressing the expression of SERPINE1, which encodes a proteinase inhibitor with antiangiogenic effects. The findings reveal a non-cell-autonomous function of these transcription factors in regulating angiogenesis in ovarian cancer.

Blood Plasma Exosomes Contain Circulating DNA in Their Crown

It is known that circulating DNA (cirDNA) is protected from nuclease activity by proteins that form macromolecular complexes with DNA. In addition, it was previously shown that cirDNA can bind to the outer surface of exosomes. NTA analysis and real-time PCR show that exosomes from healthy females (HF) or breast cancer patients (BCP) plasma contain less than 1.4 × 10⁻⁸ pg of DNA. Thus, only a minor part of cirDNA is attached to the outer side of the exosome as part of the vesicle crown: the share of exosomal DNA does not exceed 0.025% HF plasma DNA and 0.004% BCP plasma DNA. Treatment of plasma exosomes with DNase I with subsequent dot immunoassay reveals that H2a, H2b, and H3 histones are not part of the exosomal membrane, but are part of the cirDNA–protein macromolecular complex associated with the surface of the exosome either through interaction with DNA-binding proteins or with histone-binding proteins. Using bioinformatics approaches after identification by MALDI-TOF mass spectrometry, 16 exosomal DNA-binding proteins were identified. It was shown that four proteins—AIFM1, IGHM, CHD5, and KCNIP3—are candidates for DNA binding on the outer membrane of exosomes; the crown of exosomes may include five DNA-binding proteins: H2a, H2b, H3, IGHM, and ALB. Of note, AIFM1, IGHM, and CHD5 proteins are found only in HF plasma exosomes; KCNIP3 protein is identified only in BCP plasma exosomes; and H2a, H2b, H3, and ALB are revealed in all samples of plasma exosomes. Two histone-binding proteins, CHD5 and KDM6B, have been found in exosomes from HF plasma. The data obtained indicate that cirDNA preferentially binds to the outer membrane of exosomes by association with DNA-binding proteins.

A multi-level investigation of the genetic relationship between endometriosis and ovarian cancer histotypes

Endometriosis is associated with increased risk of epithelial ovarian cancers (EOCs). Using data from large endometriosis and EOC genome-wide association meta-analyses, we estimate the genetic correlation and evaluate the causal relationship between genetic liability to endometriosis and EOC histotypes, and identify shared susceptibility loci. We estimate a significant genetic correlation (r_g) between endometriosis and clear cell (r_g = 0.71), endometrioid (r_g = 0.48), and high-grade serous (r_g = 0.19) ovarian cancer, associations supported by Mendelian randomization analyses. Bivariate meta-analysis identified 28 loci associated with both endometriosis and EOC, including 19 with evidence for a shared underlying association signal. Differences in the shared risk suggest different underlying pathways may contribute to the relationship between endometriosis and the different histotypes. Functional annotation using transcriptomic and epigenomic profiles of relevant tissues/cells highlights several target genes. This comprehensive analysis reveals profound genetic overlap between endometriosis and EOC histotypes with valuable genomic targets for understanding the biological mechanisms linking the diseases.

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Endometriosis is genetically correlated with CCOC, ENOC, and HGSOC

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Genetic liability to endometriosis confers risk of these EOC histotypes

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Profound colocalization of genetic associations at endometriosis and EOC risk loci

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Functional annotation highlights shared target genes elucidating the genetic link

Chromatin Mechanisms Driving Cancer

The change in cell state from normal to malignant is driven fundamentally by oncogenic mutations in cooperation with epigenetic alterations of chromatin. These alterations in chromatin can be a consequence of environmental stressors or germline and/or somatic mutations that directly alter the structure of chromatin machinery proteins, their levels, or their regulatory function. These changes can result in an inability of the cell to differentiate along a predefined lineage path, or drive a hyperactive, highly proliferative state with addiction to high levels of transcriptional output. We discuss how these genetic alterations hijack the chromatin machinery for the oncogenic process to reveal unique vulnerabilities and novel targets for cancer therapy.

Imaging through diffuse media using multi-mode vortex beams and deep learning

Optical imaging through diffuse media is a challenging issue and has attracted applications in many fields such as biomedical imaging, non-destructive testing, and computer-assisted surgery. However, light interaction with diffuse media leads to multiple scattering of the photons in the angular and spatial domain, severely degrading the image reconstruction process. In this article, a novel method to image through diffuse media using multiple modes of vortex beams and a new deep learning network named “LGDiffNet” is derived. A proof-of-concept numerical simulation is conducted using this method, and the results are experimentally verified. In this technique, the multiple modes of Gaussian and Laguerre-Gaussian beams illuminate the displayed digits dataset number, and the beams are then propagated through the diffuser before being captured on the beam profiler. Furthermore, we investigated whether imaging through diffuse media using multiple modes of vortex beams instead of Gaussian beams improves the imaging system's imaging capability and enhances the network's reconstruction ability. Our results show that illuminating the diffuser using vortex beams and employing the “LGDiffNet” network provides enhanced image reconstruction compared to existing modalities. When employing vortex beams for image reconstruction, the best NPCC is − 0.9850. However, when using Gaussian beams for imaging acquisition, the best NPCC is − 0.9837. An enhancement of 0.62 dB, in terms of PSNR, is achieved using this method when a highly scattering diffuser of grit 220 and width 2 mm (7.11 times the mean free path) is used. No additional optimizations or reference beams were used in the imaging system, revealing the robustness of the “LGDiffNet” network and the adaptability of the imaging system for practical applications in medical imaging.

Functional analysis of the 1p34.3 risk locus implicates GNL2 in high-grade serous ovarian cancer

The high-grade serous ovarian cancer (HGSOC) risk locus at chromosome 1p34.3 resides within a frequently amplified genomic region signifying the presence of an oncogene. Here, we integrate in silico variant-to-function analysis with functional studies to characterize the oncogenic potential of candidate genes in the 1p34.3 locus. Fine mapping of genome-wide association statistics identified candidate causal SNPs local to H3K27ac-demarcated enhancer regions that exhibit allele-specific binding for CTCF in HGSOC and normal fallopian tube secretory epithelium cells (FTSECs). SNP risk associations colocalized with eQTL for six genes (DNALI1, GNL2, RSPO1, SNIP1, MEAF6, and LINC01137) that are more highly expressed in carriers of the risk allele, and three (DNALI1, GNL2, and RSPO1) were upregulated in HGSOC compared to normal ovarian surface epithelium cells and/or FTSECs. Increased expression of GNL2 and MEAF6 was associated with shorter survival in HGSOC with 1p34.3 amplifications. Despite its activation of β-catenin signaling, RSPO1 overexpression exerted no effects on proliferation or colony formation in our study of ovarian cancer and FTSECs. Instead, GNL2, MEAF6, and SNIP1 silencing impaired in vitro ovarian cancer cell growth. Additionally, GNL2 silencing diminished xenograft tumor formation, whereas overexpression stimulated proliferation and colony formation in FTSECs. GNL2 influences 60S ribosomal subunit maturation and global protein synthesis in ovarian cancer and FTSECs, providing a potential mechanism of how GNL2 upregulation might promote ovarian cancer development and mediate genetic susceptibility of HGSOC.

Estimating the predictive power of silent mutations on cancer classification and prognosis

In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.

Leveraging Genomics, Transcriptomics, and Epigenomics to Understand the Biology and Chemoresistance of Ovarian Cancer

Ovarian cancer is a major cause of fatality due to a gynecological malignancy. This lethality is largely due to the unspecific clinical manifestations of ovarian cancer, which lead to late detection and to high resistance to conventional therapies based on platinum. In recent years, we have advanced our understanding of the mechanisms provoking tumor relapse, and the advent of so-called omics technologies has provided exceptional tools to evaluate molecular mechanisms leading to therapy resistance in ovarian cancer. Here, we review the contribution of genomics, transcriptomics, and epigenomics techniques to our knowledge about the biology and molecular features of ovarian cancers, with a focus on therapy resistance. The use of these technologies to identify molecular markers and mechanisms leading to chemoresistance in these tumors is discussed, as well as potential further applications.

Non-coding driver mutations in human cancer

Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.

Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

Background

Cancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. Entire classes of regulatory elements are affected by local variations in mutation frequency. However, the underlying mechanisms with functional and genetic determinants remain poorly understood.

Results

We characterise the mutational landscape of 1.3 million gene-regulatory and chromatin architectural elements in 2419 whole cancer genomes with transcriptional and pathway activity, functional conservation and recurrent driver events. We develop RM2, a statistical model that quantifies mutational enrichment or depletion in classes of genomic elements through genetic, trinucleotide and megabase-scale effects. We report a map of localised mutational processes affecting CTCF binding sites, transcription start sites (TSS) and tissue-specific open-chromatin regions. Increased mutation frequency in TSSs associates with mRNA abundance in most cancer types, while open-chromatin regions are generally enriched in mutations. We identify ~ 10,000 CTCF binding sites with core DNA motifs and constitutive binding in 66 cell types that represent focal points of mutagenesis. We detect site-specific mutational signature enrichments, such as SBS40 in open-chromatin regions in prostate cancer and SBS17b in CTCF binding sites in gastrointestinal cancers. Candidate drivers of localised mutagenesis are also apparent: BRAF mutations associate with mutational enrichments at CTCF binding sites in melanoma, and ARID1A mutations with TSS-specific mutagenesis in pancreatic cancer.

Conclusions

Our method and catalogue of localised mutational processes provide novel perspectives to cancer genome evolution, mutagenesis, DNA repair and driver gene discovery. The functional and genetic correlates of mutational processes suggest mechanistic hypotheses for future studies.

Association of genomic variants at PAX8 and PBX2 with cervical cancer risk

Cervical malignancy is triggered by human papillomavirus infection but the risk for cervical cancer has a hereditary component. From a recent Genome Wide Association Study meta-analysis, 2q14.1 (PAX8) and 6p21.32 (PBX2) have been proposed as novel cervical cancer susceptibility loci. We investigated the two main signals at these loci in an independent case-control series of 2578 cases with cervical dysplasia or carcinoma and 1483 healthy females. We find significant associations for both variants, rs10175462 at PAX8 and rs2856437 at PBX2, with overall cervical disease (rs10175462: odds ratio [OR] 0.82, 95% confidence interval [CI] 0.74-0.91, P = 2.4 × 10-4 ; rs2856437: OR 1.52, 95% CI 1.14-2.02, P = .004). Both variants showed evidence of association with invasive squamous cervical cancer (rs10175462: OR 0.80, 95% CI 0.68-0.94, P = .006; rs2856437: OR 1.56, 95% CI 1.03-2.36, P = .036) and with high-grade dysplasia (rs10175462: OR 0.79, 95%CI 0.70-0.90, P = 1.9 × 10-4 ; rs2856437: OR 1.58, 95% CI 1.15-2.17, P = .005). A combined analysis of high-grade dysplasia and invasive cervical cancer also showed significant associations for both variants (rs10175462: OR 0.81, 95% CI 0.73-0.91, P = 2.4 × 10-4 ; rs2856437: OR 1.57, 95% CI 1.18-2.10, P = .002). No association was detected for rs2856437 with low-grade dysplasia, while rs10175462 showed weak evidence of association (P = .05). RNA analyses in cervical samples revealed that PAX8 transcripts were upregulated in HPV-positive lesions (P = .008) but this was not observed in the presence of the protective minor allele of rs10175462. The rs10175462 genotype also correlated with reduced levels of the lncRNA PAX8-AS1 (P < .001). Taken together, our results extend the evidence for a link between genomic risk variants at the HLA region (PBX2) with cervical disease and support PAX8 as the first consistent non-HLA cervical cancer susceptibility locus.

Unraveling the Mysteries of PAX8 in Reproductive Tract Cancers

Paired Box 8 (PAX8) is a lineage-specific transcription factor that has essential roles during embryogenesis and tumorigenesis. The importance of PAX8 in the development of the reproductive system is highlighted by abnormalities observed upon the loss or mutation of this PAX family member. In cancer, PAX8 expression is deregulated in a key set of neoplasms, including those arising from the Müllerian ducts. The roles of PAX8 in oncogenesis are diverse and include epigenetic remodeling, stimulation of proliferation, inhibition of apoptosis, and regulation of angiogenesis. PAX8 can interact with different protein partners during cancer progression and may exhibit significant function-altering alternative splicing. Moreover, expression of PAX8 in cancer can also serve as a biomarker for diagnostic and prognostic purposes. In this review, we focus on the roles of PAX8 in cancers of the reproductive system. Understanding the diverse mechanisms of action of PAX8 in development and oncogenesis may identify new vulnerabilities in malignancies that currently lack effective therapies.

Transcription factors in epithelial ovarian cancer: histotype-specific drivers and novel therapeutic targets

Transcription factors (TFs) are major contributors to cancer risk and somatic development. In preclinical and clinical studies, direct or indirect inhibition of TF-mediated oncogenic gene expression profiles have proven to be effective in many tumor types, highlighting this group of proteins as valuable therapeutic targets. In spite of this, our understanding of TFs in epithelial ovarian cancer (EOC) is relatively limited. EOC is a heterogeneous disease composed of five major histologic subtypes; high-grade serous, low-grade serous, endometrioid, clear cell and mucinous. Each histology is associated with unique clinical etiologies, sensitivity to therapies, and molecular signatures - including diverse transcriptional regulatory programs. While some TFs are shared across EOC subtypes, a set of TFs are expressed in a histotype-specific manner and likely explain part of the histologic diversity of EOC subtypes. Targeting TFs present with unique opportunities for development of novel precision medicine strategies for ovarian cancer. This article reviews the critical TFs in EOC subtypes and highlights the potential of exploiting TFs as biomarkers and therapeutic targets.

Ovarian Cancer Risk Variants Are Enriched in Histotype-Specific Enhancers and Disrupt Transcription Factor Binding Sites

Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome-wide association studies have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, where they likely function through interaction with gene regulatory elements. In this study we first estimated the heritability explained by known common low penetrance risk alleles for EOC. The narrow sense heritability (hg2) of EOC overall and high-grade serous ovarian cancer (HGSOCs) were estimated to be 5%-6%. Partitioned SNP heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from Roadmap Epigenomics and ENCODE, and from H3K27Ac ChIP-seq data generated in 26 ovarian cancer and precursor-related cell and tissue types. We identified significant enrichment of risk single-nucleotide polymorphisms (SNPs) in active regulatory elements marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active regulatory elements influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that are predicted to significantly break transcription factor (TF) motifs. The most frequently broken motif was REST (p value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. Overall, these systematic functional annotations with epigenomic data improve interpretation of EOC risk variants and shed light on likely cells of origin.

Identification of key factors associated with early- and late-onset ovarian serous cystadenocarcinoma

Aim: To uncover the molecular mechanisms of early-onset ovarian serous cystadenocarcinoma (EOOSC; patients <50 years old) and late-onset ovarian serous cystadenocarcinoma (LOOSC; patients ≥50 years old). Materials & methods: Bioinformatics was utilized to identify the key factors. Results: 478 EOOSC and 899 LOOSC individual differentially expressed genes were identified and enriched in different pathways. The expression of key genes LAG3, LRRC63 and MT1B significantly influenced the overall survival of EOOSC patients. The expression of key genes RDH12, NTSR1, ZSCAN16, CT45A3 and EPPIN_WFDC6 significantly affected the overall survival of LOOSC patients. Conclusions: The molecular mechanisms of EOOSC and LOOSC appear to be different, so that patients might be treated individually in respect of age.

Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes.. [DOI: 10.1101/2020.07.29.226373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

Cancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. Entire classes of regulatory elements are affected by local variations in mutation frequency. However, the underlying mutational mechanisms with functional and genetic determinants remain poorly understood.

Results

We characterised the mutational landscape of 1.3 million gene regulatory and chromatin architectural elements in 2,419 whole cancer genomes with transcriptional and pathway activity, functional conservation and recurrent driver events. We developed RM2, a statistical model that quantifies mutational enrichment or depletion in classes of genomic elements through genetic, trinucleotide and megabase-scale effects. We report a map of localised mutational processes affecting CTCF binding sites, transcription start sites (TSS) and tissue-specific open-chromatin regions. We show that increased mutational frequency in TSSs correlates with mRNA abundance in most cancer types, while open-chromatin regions are generally enriched in mutations. We identified ∼10,000 CTCF binding sites with core DNA motifs and constitutive binding in 66 cell types that represent focal points of local mutagenesis. We detected site-specific mutational signatures, such as SBS40 in open-chromatin regions in prostate cancer and SBS17b in CTCF binding sites in gastrointestinal cancers. We also proposed candidate drivers of localised mutagenesis: BRAF mutations associate with mutational enrichments at CTCF binding sites in melanoma, and ARID1A mutations with TSS-specific mutations in pancreatic cancer.

Conclusions

Our method and catalogue of localised mutational processes provide novel perspectives to cancer genome evolution, mutagenesis, DNA repair and driver discovery. Functional and genetic correlates of localised mutagenesis provide mechanistic hypotheses for future studies.