Cancer Driver Mutations Research Articles

Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era.

One of the key challenges of cancer biology is to catalogue and understand the somatic genomic alterations leading to cancer. Although alternative definitions and search methods have been developed to identify cancer driver genes and mutations, analyses of thousands of cancer genomes return a remarkably similar catalogue of around 300 genes that are mutated in at least one cancer type. Yet, many features of these genes and their role in cancer remain unclear, first and foremost when a somatic mutation is truly oncogenic. In this review, we first summarize some of the recent efforts in completing the catalogue of cancer driver genes. Then, we give an overview of different aspects that influence the oncogenicity of somatic mutations in the core cancer driver genes, including their interactions with the germline genome, other cancer driver mutations, the immune system, or their potential role in healthy tissues. In the coming years, this research holds promise to illuminate how, when, and why cancer driver genes and mutations are really drivers, and thereby move personalized cancer medicine and targeted therapies forward.

Roles and mechanisms of alternative splicing in cancer - implications for care.

Removal of introns from messenger RNA precursors (pre-mRNA splicing) is an essential step for the expression of most eukaryotic genes. Alternative splicing enables the regulated generation of multiple mRNA and protein products from a single gene. Cancer cells have general as well as cancer type-specific and subtype-specific alterations in the splicing process that can have prognostic value and contribute to every hallmark of cancer progression, including cancer immune responses. These splicing alterations are often linked to the occurrence of cancer driver mutations in genes encoding either core components or regulators of the splicing machinery. Of therapeutic relevance, the transcriptomic landscape of cancer cells makes them particularly vulnerable to pharmacological inhibition of splicing. Small-molecule splicing modulators are currently in clinical trials and, in addition to splice site-switching antisense oligonucleotides, offer the promise of novel and personalized approaches to cancer treatment.

CScape-somatic: distinguishing driver and passenger point mutations in the cancer genome.

MotivationNext-generation sequencing technologies have accelerated the discovery of single nucleotide variants in the human genome, stimulating the development of predictors for classifying which of these variants are likely functional in disease, and which neutral. Recently, we proposed CScape, a method for discriminating between cancer driver mutations and presumed benign variants. For the neutral class, this method relied on benign germline variants found in the 1000 Genomes Project database. Discrimination could, therefore, be influenced by the distinction of germline versus somatic, rather than neutral versus disease driver. This motivates this article in which we consider predictive discrimination between recurrent and rare somatic single point mutations based solely on using cancer data, and the distinction between these two somatic classes and germline single point mutations.ResultsFor somatic point mutations in coding and non-coding regions of the genome, we propose CScape-somatic, an integrative classifier for predictively discriminating between recurrent and rare variants in the human cancer genome. In this study, we use purely cancer genome data and investigate the distinction between minimal occurrence and significantly recurrent somatic single point mutations in the human cancer genome. We show that this type of predictive distinction can give novel insight, and may deliver more meaningful prediction in both coding and non-coding regions of the cancer genome. Tested on somatic mutations, CScape-somatic outperforms alternative methods, reaching 74% balanced accuracy in coding regions and 69% in non-coding regions, whereas even higher accuracy may be achieved using thresholds to isolate high-confidence predictions.Availability and implementationPredictions and software are available at http://CScape-somatic.biocompute.org.uk/.Contactmark.f.rogers.phd@gmail.com or C.Campbell@bristol.ac.ukSupplementary informationSupplementary data are available at Bioinformatics online.

Plasmatic KRAS Kinetics for the Prediction of Treatment Response and Progression in Patients With KRAS-mutant Lung Adenocarcinoma

IntroductionKRAS is the most common driver mutation in lung cancer. ctDNA-based assessment offers advantages over tumor as a minimally invasive method able to capture tumor heterogeneity. Monitoring KRAS mutational load in ctDNA may be useful in the management of the patients. MethodsConsecutive patients diagnosed with KRAS mutant lung adenocarcinoma in the tumor biopsy were included in this study. Plasma samples were obtained at different time points during the course of the disease. KRAS mutations in plasma were quantified using digital PCR and correlated with mutations in tumor and with radiological response and progression. ResultsTwo hundred and forty-five plasma samples from 56 patients were analyzed. The rate of detection of KRAS mutations in plasma in our previously characterized KRAS-mutant cases was 82% overall, reaching 96% in cases with more than 1 metastatic location. The dynamics of KRAS mutational load predicted response in 93% and progression in 63% of cases, 33 and 50 days respectively in advance of radiological evaluation. Progression-free survival for patients in whom ctDNA was not detectable in plasma after treatment initiation was significantly longer than for those in whom ctDNA remained detectable (7.7 versus 3.2 months; HR: 0.44, p=0.004). ConclusionsThe detection of KRAS mutations in ctDNA showed a good correlation with that in tumor biopsy and, in most cases, predicted tumor response and progression to chemotherapy in advance of radiographic evaluation. The liquid biopsies for ctDNA-based molecular analyses are a reliable tool for KRAS testing in clinical practice.

Why is cancer not more common? A changing microenvironment may help to explain why, and suggests strategies for anti-cancer therapy.

One of the great unsolved puzzles in cancer biology is not why cancers occur, but rather explaining why so few cancers occur compared with the theoretical number that could occur, given the number of progenitor cells in the body and the normal mutation rate. We hypothesized that a contributory explanation is that the tumour microenvironment (TME) is not fixed due to factors such as immune cell infiltration, and that this could impair the ability of neoplastic cells to retain a high enough fitness to become a cancer. The TME has implicitly been assumed to be static in most cancer evolution models, and we therefore developed a mathematical model of spatial cancer evolution assuming that the TME, and thus the optimum cancer phenotype, changes over time. Based on simulations, we show how cancer cell populations adapt to diverse changing TME conditions and fitness landscapes. Compared with static TMEs, which generate neutral dynamics, changing TMEs lead to complex adaptations with characteristic spatio-temporal heterogeneity involving variable fitness effects of driver mutations, subclonal mixing, subclonal competition and phylogeny patterns. In many cases, cancer cell populations fail to grow or undergo spontaneous regression, and even extinction. Our analyses predict that cancer evolution in a changing TME is challenging, and can help to explain why cancer is neither inevitable nor as common as expected. Should cancer driver mutations with effects dependent of the TME exist, they are likely to be selected. Anti-cancer prevention and treatment strategies based on changing the TME are feasible and potentially effective.

Cancer-associated mutations in endometriosis: shedding light on the pathogenesis and pathophysiology

Endometriosis is a benign gynaecological disease. Thus, it came as a complete surprise when it was reported recently that the majority of deep endometriosis lesions harbour somatic mutations and a sizeable portion of them contain known cancer-associated mutations (CAMs). Four more studies have since been published, all demonstrating the existence of CAMs in different subtypes of endometriosis. While the field is still evolving, the confirmation of CAMs has raised many questions that were previously overlooked. A comprehensive overview of CAMs in endometriosis has been produced. In addition, with the recently emerged understanding of the natural history of endometriotic lesions as well as CAMs in normal and apparently healthy tissues, this review attempts to address the following questions: Why has there been such a wild discrepancy in reported mutation frequencies? Why does ectopic endometrium have a higher mutation rate than that of eutopic endometrium? Would the presence of CAMs in endometriotic lesions increase the risk of cancer to the bearers? Why do endometriotic epithelial cells have much higher mutation frequencies than their stromal counterpart? What clinical implications, if any, do the CAMs have for the bearers? Do these CAMs tell us anything about the pathogenesis and/or pathophysiology of endometriosis? The PubMed database was searched, from its inception to September 2019, for all papers in English using the term 'endometriosis and CAM', 'endometriosis and cancer-driver mutation', 'somatic mutations', 'fibrosis', 'fibrosis and epigenetic', 'CAMs and tumorigenesis', 'somatic mutation and normal tissues', 'oestrogen receptor and fibrosis', 'oxidative stress and fibrosis', 'ARID1A mutation', and 'Kirsten rat sarcoma mutation and therapeutics'. All retrieved papers were read and, when relevant, incorporated into the review results. Seven papers that identified CAMs in endometriosis using various sequencing methods were retrieved, and their results were somewhat different. Yet, it is apparent that those using microdissection techniques and more accurate sequencing methods found more CAMs, echoing recent discoveries that apparently healthy tissues also harbour CAMs as a result of the replicative aging process. Hence endometriotic lesions, irrespective of subtype, if left intact, would generate CAMs as part of replicative aging, oxidative stress and perhaps other factors yet to be identified and, in some rare cases, develop cancer. The published data still are unable to paint a clear picture on pathogenesis of endometriosis. However, since endometriotic epithelial cells have a higher turnover than their stromal counterpart due to cyclic bleeding, and since the endometriotic stromal component can be formed by refresh influx of mesenchymal cells through epithelial-mesenchymal transition, endothelial-mesenchymal transition, mesothelial-mesenchymal transition and other processes as well as recruitment of bone-marrow-derived stem cells and outflow due to smooth muscle metaplasia, endometriotic epithelial cells have much higher mutation frequencies than their stromal counterpart. The epithelial and stromal cellular components develop in a dependent and co-evolving manner. Genes involved in CAMs are likely to be active players in lesional fibrogenesis, and hyperestrogenism and oxidative stress are likely drivers of both CAMs and fibrogenesis. Finally, endometriotic lesions harbouring CAMs would conceivably be more refractory to medical treatment, due, in no small part, to their high fibrotic content and reduced vascularity and cellularity. The accumulating data on CAMs in endometriosis have shed new light on the pathogenesis and pathophysiology of endometriosis. They also suggest new challenges in management. The distinct yet co-evolving developmental trajectories of endometriotic stroma and epithelium underscore the importance of the lesional microenvironment and ever-changing cellular identity. Mutational profiling of normal endometrium from women of different ages and reproductive history is needed in order to gain a deeper understanding of the pathogenesis. Moreover, one area that has conspicuously received scant attention is the epigenetic landscape of ectopic, eutopic and normal endometrium.

Genetic Interactions and Tissue Specificity Modulate the Association of Mutations with Drug Response.

In oncology, biomarkers are widely used to predict subgroups of patients that respond to a given drug. Although clinical decisions often rely on single gene biomarkers, machine learning approaches tend to generate complex multi-gene biomarkers that are hard to interpret. Models predicting drug response based on multiple altered genes often assume that the effects of single alterations are independent. We asked whether the association of cancer driver mutations with drug response is modulated by other driver mutations or the tissue of origin. We developed an analytic framework based on linear regression to study interactions in pharmacogenomic data from two large cancer cell line panels. Starting from a model with only covariates, we included additional variables only if they significantly improved simpler models. This allows to systematically assess interactions in small, easily interpretable models. Our results show that including mutation-mutation interactions in drug response prediction models tends to improve model performance and robustness. For example, we found that TP53 mutations decrease sensitivity to BRAF inhibitors in BRAF-mutated cell lines and patient tumors, suggesting a therapeutic benefit of combining inhibition of oncogenic BRAF with reactivation of the tumor suppressor TP53. Moreover, we identified tissue-specific mutation-drug associations and synthetic lethal triplets where the simultaneous mutation of two genes sensitizes cells to a drug. In summary, our interaction-based approach contributes to a holistic view on the determining factors of drug response.

Comprehensive assessment of computational algorithms in predicting cancer driver mutations

BackgroundThe initiation and subsequent evolution of cancer are largely driven by a relatively small number of somatic mutations with critical functional impacts, so-called driver mutations. Identifying driver mutations in a patient’s tumor cells is a central task in the era of precision cancer medicine. Over the decade, many computational algorithms have been developed to predict the effects of missense single-nucleotide variants, and they are frequently employed to prioritize mutation candidates. These algorithms employ diverse molecular features to build predictive models, and while some algorithms are cancer-specific, others are not. However, the relative performance of these algorithms has not been rigorously assessed.ResultsWe construct five complementary benchmark datasets: mutation clustering patterns in the protein 3D structures, literature annotation based on OncoKB, TP53 mutations based on their effects on target-gene transactivation, effects of cancer mutations on tumor formation in xenograft experiments, and functional annotation based on in vitro cell viability assays we developed including a new dataset of ~ 200 mutations. We evaluate the performance of 33 algorithms and found that CHASM, CTAT-cancer, DEOGEN2, and PrimateAI show consistently better performance than the other algorithms. Moreover, cancer-specific algorithms show much better performance than those designed for a general purpose.ConclusionsOur study is a comprehensive assessment of the performance of different algorithms in predicting cancer driver mutations and provides deep insights into the best practice of computationally prioritizing cancer mutation candidates for end-users and for the future development of new algorithms.

Deregulated expression of Elastin Microfibril Interfacer 2 (EMILIN2) in gastric cancer affects tumor growth and angiogenesis

Gastric cancer is a frequent human tumor and often a lethal disease. Targeted therapy for gastric carcinomas is far behind vis-à-vis other solid tumors, primarily because of the paucity of cancer-driving mutations that could be efficiently and specifically targeted by current therapy. Thus, there is a need to discover actionable pathways/proteins and new diagnostic and prognostic biomarkers. In this study, we explored the role of the extracellular matrix glycoprotein EMILIN2, Elastin Microfibril Interfacer 2, in a cohort of gastric cancer patients. We discovered that EMILIN2 expression was consistently suppressed in gastric cancer and high expression levels of this glycoprotein were linked to abnormal vascular density. Furthermore, we found that EMILIN2 had a dual effect on gastric carcinoma cells: on one hand, it decreased tumor cell proliferation by triggering apoptosis, and on the other hand, it evoked the production of a number of cytokines involved in angiogenesis and inflammation, such as IL-8. Collectively, our findings posit EMILIN2 as an important onco-regulator exerting pleiotropic effects on the gastric cancer microenvironment.

Abstract P5-08-07: Tobacco use, alcohol consumption, and breast cancer somatic genomic alterations

Abstract Background: Understanding the molecular influence of epidemiological risk factors can provide insights into breast cancer etiology and progression, and lead to better prevention efforts and treatment guidelines. The link between modifiable breast cancer risk factors and tumor genomic alterations remains unexplored. This study evaluated the association of tobacco use and alcohol consumption with somatic copy number variation (SCNV), total somatic mutation burden (TSMB), seven single base substitution (SBS) signatures (SBS1, SBS2, SBS3, SBS5, SBS13, SBS29, and SBS30) (1-3), and nine breast cancer driver mutations (CDH1, GATA3, KMT2C, MAP2K4, MAP3K1, NCOR1, PIK3CA, RUNX1, and TP53) (4,5), in a subset of The Cancer Genome Atlas (TCGA). Method: Clinical and genomic data were retrieved from the TCGA database, and breast cancer risk factor information was collected from four TCGA sites; in all, 219 female breast cancer cases were included. Pre-diagnostic tobacco use was defined as never or ever; alcohol consumption was defined as drinkers or non-drinkers. Total SCNV was obtained by summing all segments with ≥|0.2|. TSMB was calculated by summing all significant mutations for each case. SBS signatures were computed using whole exome sequencing data. Analyses were conducted in all tumors (i.e., pooled) and by stratifying according to estrogen receptor (ER) status using multivariate regression, adjusting for co-variables. Results: Tobacco users had higher TSMB compared to non-users (p<0.05 all tumors). Alcohol drinkers had higher SCNV compared to non-drinkers (p<0.05 all tumors and among ER+ tumors). Tobacco use was not associated with any SBS signature. SBS3 signature (defective homologous recombination-based repair) was exclusively found in alcohol drinkers with ER- disease. No breast cancer driver mutation was associated with tobacco use or alcohol consumption. Conclusion: This study provides preliminary evidence that tobacco use and alcohol consumption can influence breast tumor biology, in particular, DNA alterations. Future larger epidemiological studies are required to confirm these findings.

Molecular Profiling for Supernatants and Matched Cell Pellets of Pleural Effusions in Non–Small-Cell Lung Cancer

Pleural effusion (PE) is commonly observed in advanced lung cancer patients. Cell-free total nucleic acid (cfTNA) isolated from cancer patients' plasma has allowed noninvasive tumor genome analyses; however, there are limited studies of detection and characterization of cfTNA in PE. Herein, we included 47 advanced non-small-cell lung cancer patients with PE, who had lung cancer driver mutations tested on tumor tissue specimens either at diagnosis or during disease progression. The supernatant and cell pellet of each PE were evaluated for molecular profiles in parallel on an Ion Torrent next-generation sequencing platform. Somatic mutations were detected in 89.1% supernatant cfTNA, but in only 54.3% of cell pellets. The overall concordance rate between supernatants and formalin-fixed, paraffin-embedded cell pellets at the mutation level was 53.3%. By contrast, 41.7% and 5.0% of somatic alterations were detected in supernatants and cell pellets, respectively. Furthermore, joint analysis of supernatants and cell pellets from PE showed a high concordance (88.3%) of variant detection with their respective tumor tissue specimens. Low-frequency T790M mutations in three cases (0.29%, 0.41%, and 1.56%) were detected in supernatants but not in the matched cell pellets or tumor tissues. In conclusion, pleural effusion-derived cfTNA can effectively be used in clinical practice for molecular analysis by next-generation sequencing, even in cases where corresponding cell pellets or tumor tissuesyield insufficient material.

Pathway and network analysis of more than 2500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

Development of a Novel One-Step Automated Rapid in situ Hybridization for Anaplastic Lymphoma Kinase Rearrangement Using Non-Contact Alternating-Current Electric-Field Mixing

Echinoderm microtubule-associated protein-like 4 and anaplastic lymphoma kinase (ALK) fusion gene rearrangement is a key driver mutation in non-small cell lung cancer (NSCLC). Although Break-Apart ALK fluorescence in situ hybridization (FISH) is a reliable diagnostic method for detecting ALK gene rearrangement, it is also costly and time-consuming to use as a routine screening test. Our aim was to evaluate the clinical utility of a novel one-step, automated, rapid FISH (Auto-RaFISH) method developed to facilitate hybridization. This method takes advantage of the non-contact mixing effect of an alternating-current electric field. Ten representative specimens from 85 patients diagnosed at multiple centers with primary lung cancer with identified ALK-FISH status were collected. The specimens were all tested using FISH, RaFISH, and Auto-RaFISH. With both RaFISH protocols, the ALK test was completed within 4.5 h, as compared to the 20 h needed for the standard protocol. We found 100% agreement between the standard FISH, RaFISH, and new Auto-RaFISH based on the ALK status, and all methods stained equally well. These findings suggest that Auto-RaFISH could potentially serve as an automated clinical tool for prompt determination of ALK status in NSCLC.

Mutational processes of distinct POLE exonuclease domain mutants drive an enrichment of a specific TP53 mutation in colorectal cancer

Cancer genomes with mutations in the exonuclease domain of Polymerase Epsilon (POLE) present with an extraordinarily high somatic mutation burden. In vitro studies have shown that distinct POLE mutants exhibit different polymerase activity. Yet, genome-wide mutation patterns and driver mutation formation arising from different POLE mutants remains unclear. Here, we curated somatic mutation calls from 7,345 colorectal cancer samples from published studies and publicly available databases. These include 44 POLE mutant samples including 9 with whole genome sequencing data available. The POLE mutant samples were categorized based on the specific POLE mutation present. Mutation spectrum, associations of somatic mutations with epigenomics features and co-occurrence with specific driver mutations were examined across different POLE mutants. We found that different POLE mutants exhibit distinct mutation spectrum with significantly higher relative frequency of C>T mutations in POLE V411L mutants. Our analysis showed that this increase frequency in C>T mutations is not dependent on DNA methylation and not associated with other genomic features and thus specifically due to DNA sequence context alone. Notably, we found strong association of the TP53 R213* mutation specifically with POLE P286R mutants. This truncation mutation occurs within the TT[C>T]GA context. For C>T mutations, this sequence context is significantly more likely to be mutated in POLE P286R mutants compared with other POLE exonuclease domain mutants. This study refines our understanding of DNA polymerase fidelity and underscores genome-wide mutation spectrum and specific cancer driver mutation formation observed in POLE mutant cancers.

Decoding Molecular Mechanisms of Disease with Medical Biophysics

We study cellular networks and the underlying molecular interactions trying to understand how their perturbation can lead to disease. To accomplish this, we develop and apply molecular modeling, molecular dynamics simulation methods, and use experimental data to guide us in designing new hybrid approaches. Such methods assess molecular mechanisms of mutational effects on proteins, protein interactomes and epigenetic components, and as a result, offer experimental leads in identifying drug-resistant and cancer driver mutations. Potential application to the design of drugs that target protein-protein interactions will be also discussed.

Atom Contact Profile by Alpha-Shape Improves Prediction of Effects of Missense Variant

Missense variant results in substitution of amino acids in the transcribed protein. These mutations may be neutral or may lead to phenotypes that are deleterious. Assessing the effect of missense variants has been the focus of many studies. With the rapid growth of the number of known protein structures, recent methods incorporate structural properties, including those generated through extensive simulation, which complement traditional sequence-based features. However, only marginal improvement is achieved over conventional methods in most cases. In this study, we report the approach of construction of atom contact profile based on alpha-shapes, which are derived from weighted Delaunay triangulations of atoms of different radius. With detailed atom-level contact information, we build a predictor to estimate the effect of missense variants. Our preliminary result shows that we achieve significant improvement over previous methods. Using the HumDiv benchmark dataset, our predictor has an accuracy of 0.906, recall of 0.930, precision of 0.887, and F-1 score of 0.908. These results compare favorably with reported results of several start-of-the-art methods. Our approach is general and can be applied to other problems including identifying cancer driver mutations.

Negative reactions of BRAF mutation-specific immunohistochemistry to non-V600E mutations of BRAF.

BRAF mutations are rare driver mutations in non-small cell lung cancer (NSCLC), accounting for 1%-2% of the driver mutations, and the mutation spectrum has a wide range in contrast to other tumors. While V600E is a dominant mutation in melanoma, more than half of the mutations in NSCLCs are non-V600E. However, treatment with dabrafenib plus trametinib targets the BRAF V600E mutation exclusively. Therefore, distinguishing between V600E and non-V600E mutations is crucial for biomarker testing in NSCLC in order to determine treatment of choice. Immunohistochemistry (IHC) using the BRAF V600E mutation-specific antibody is clinically used in melanoma patients, but little is known about its application in NSCLC, particularly with regard to the assay performance for non-V600E mutations. In the present study, we examined 117 tumors with BRAF mutations, including 30 with non-V600E mutations, using BRAF mutation-specific IHC. None of the tumors with non-V600E mutations, including two compound mutations, showed a positive reaction. Furthermore, all V600E mutations were positive except for one case with combined BRAF V600E and K601_W604 deletion. Our findings confirmed that the BRAF V600E mutation-specific IHC is specific without any cross-reactions to non-V600E mutations, suggesting that this assay can be a useful screening tool in clinical practice.

Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks

A comprehensive catalog of cancer driver mutations is essential for understanding tumorigenesis and developing therapies. Exome-sequencing studies have mapped many protein-coding drivers, yet few non-coding drivers are known because genome-wide discovery is challenging. We developed a driver discovery method, ActiveDriverWGS, and analyzed 120,788 cis-regulatory modules (CRMs) across 1,844 whole tumor genomes from the ICGC-TCGA PCAWG project. We found 30 CRMs with enriched SNVs and indels (FDR< 0.05). These frequently mutated regulatory elements (FMREs) were ubiquitously active in human tissues, showed long-range chromatin interactions and mRNA abundance associations with target genes, and were enriched in motif-rewiring mutations and structural variants. Genomic deletion of one FMRE in human cells caused proliferative deficiencies and transcriptional deregulation of cancer genes CCNB1IP1, CDH1, and CDKN2B, validating observations in FMRE-mutated tumors. Pathway analysis revealed further sub-significant FMREs at cancer genes and processes, indicating an unexplored landscape of infrequent driver mutations in the non-coding genome.

DOP50 The landscape of somatic mutations in non-neoplastic IBD-affected colon

Abstract Background Under normal physiological conditions, colonic crypts accrue ~40 somatic mutations for every year of life. That somatic mutations contribute to the development of cancer is well established, but their patterns, burden and functional consequences in diseases other than cancer have not been extensively studied and our understanding of the effects of chronic inflammation on the mutational profile and clonal structure of the colon is limited. Here, we investigated how the recurrent cycles of inflammation, ulceration and regeneration seen in IBD impact the mutational and clonal structure of intestinal epithelia. Methods We isolated and whole-genome sequenced ~400 individual colonic crypts from 46 IBD patients and compared these to ~400 crypts from 41 non-IBD controls. We compared the mutation burden, mutational signature exposure, clonal structure and cancer driver mutation landscape in crypts from actively and previously inflamed regions with crypts dissected from controls. Results We estimated the base substitution rate of affected colonic epithelial cells to be doubled after IBD onset. This change was primarily driven by acceleration of mutational processes ubiquitously observed in normal colon (Figure 1), and we did not detect an IBD-specific mutational process. In contrast to the normal colon, where clonal expansions outside the confines of the crypt are rare, we observed widespread millimetre-scale clonal expansions, even in the absence of mutations in KRAS, TP53 and APC (Figure 2). We discovered that mutations in ARID1A, PIGR and ZC3H12A, and genes in the interleukin 17 and Toll-like receptor pathways, were under positive selection in colonic crypts from IBD patients (Figure 3). With the exception of ARID1A, these genes and pathways have not been previously associated with cancer risk. A previously published mouse model of ZC3H12A suggests that LoF mutations in this gene may facilitate healing of affected mucosa without promoting tumorigenesis. This could make the encoded protein an attractive drug target. The observed enrichment of mutations in PIGR and IL17 and TLR pathways suggests that somatic mutations may initiate, maintain or perpetuate IBD pathogenesis through disruption of microbe-epithelial homeostasis. Conclusion Our results provide new insights into the consequences of chronic intestinal inflammation on the mutational profile and clonal structure of colonic epithelia. We identify the mutagens driving the increase in mutation burden and mutations which are under positive selection in the context of inflammation. Our results suggest that studying somatic mutations in the colon can reveal putative drug targets and pathogenic mechanisms for IBD.

In situ CRISPR-Cas9 base editing for the development of genetically engineered mouse models of breast cancer.

Genetically engineered mouse models (GEMMs) of cancer have proven to be of great value for basic and translational research. Although CRISPR-based gene disruption offers a fast-track approach for perturbing gene function and circumvents certain limitations of standard GEMM development, it does not provide a flexible platform for recapitulating clinically relevant missense mutations invivo. To this end, we generated knock-in mice with Cre-conditional expression of a cytidine base editor and tested their utility for precise somatic engineering of missense mutations in key cancer drivers. Upon intraductal delivery of sgRNA-encoding vectors, we could install point mutations with high efficiency in one or multiple endogenous genes insitu and assess the effect of defined allelic variants on mammary tumorigenesis. While the system also produces bystander insertions and deletions that can stochastically be selected for when targeting a tumor suppressor gene, we could effectively recapitulate oncogenic nonsense mutations. We successfully applied this system in a model of triple-negative breast cancer, providing the proof of concept for extending this flexible somatic base editing platform to other tissues and tumor types.