scholarly journals CpG methylation accounts for genome-wide C>T mutation variation and cancer driver formation across cancer types

2017 ◽  
Author(s):  
Rebecca C. Poulos ◽  
Jake Olivier ◽  
Jason W. H. Wong
Keyword(s):  
Cytosine Methylation ◽  
Excision Repair ◽  
Replication Timing ◽  
Cpg Methylation ◽  
Tissue Type ◽  
Driver Mutations ◽  
Cancer Subtypes ◽  
Cancer Driver ◽  
Timing Data ◽  
Cancer Types

AbstractCytosine methylation (5mC) is vital for cellular function, and yet 5mC sites are also commonly mutated in the genome. In this study, we analyse the genomes of over 900 cancer samples, together with tissue type-specific methylation and replication timing data. We describe a strong mutation-methylation association in colorectal cancers with microsatellite instability (MSI) or withPolymerase epsilon (POLE)exonuclease domain mutation. We describe a potential role for mismatch repair in the correction of mismatches resulting from deamination of 5mC, and propose a mutator phenotype to exist inPOLE-mutant cancers specifically at 5mC sites. We also associatePOLE-mutant hotspot coding mutations inAPCandTP53with CpG methylation. Analysing mutations across additional cancer types, we identify nucleotide excision repair- and AID/APOBEC-induced processes to underlie differential mutation-methylation associations in certain cancer subtypes. This study reveals differential associations vital for accurately mapping regional variation in mutation density and pinpointing driver mutations in cancer.

scholarly journals Nucleotide excision repair is impaired by binding of transcription factors to DNA

2015 ◽  
Author(s):  
Radhakrishnan Sabarinathan ◽  
Loris Mularoni ◽  
Jordi Deu-Pons ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
Nucleotide Excision Repair ◽  
Mutation Rate ◽  
Binding Sites ◽  
Somatic Mutations ◽  
Excision Repair ◽  
Replication Timing ◽  
Driver Mutations ◽  
Cancer Driver ◽  
Nucleotide Excision ◽  
Active Transcription

Somatic mutations are the driving force of cancer genome evolution. The rate of somatic mutations appears in great variability across the genome due to chromatin organization, DNA accessibility and replication timing. However, other variables that may influence the mutation rate locally, such as DNA-binding proteins, are unknown. Here we demonstrate that the rate of somatic mutations in melanoma tumors is highly increased at active Transcription Factor binding sites (TFBS) and nucleosome embedded DNA, compared to their flanking regions. Using recently available excision-repair sequencing (XR-seq) data, we show that the higher mutation rate at these sites is caused by a decrease of the levels of nucleotide excision repair (NER) activity. Therefore, our work demonstrates that DNA-bound proteins interfere with the NER machinery, which results in an increased rate of mutations at their binding sites. This finding has important implications in our understanding of mutational and DNA repair processes and in the identification of cancer driver mutations.

scholarly journals OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

2020 ◽  
Vol 49 (D1) ◽  
pp. D1289-D1301 ◽  
Author(s):  
Tao Wang ◽  
Shasha Ruan ◽  
Xiaolu Zhao ◽  
Xiaohui Shi ◽  
Huajing Teng ◽  
...  
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Cell Population ◽  
Cancer Types ◽  
Neutral Mutations ◽  
Analysis Platform

Abstract The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

scholarly journals The cancer-mutation network and the number and specificity of driver mutations

2017 ◽  
Author(s):  
Jaime Iranzo ◽  
Iñigo Martincorena ◽  
Eugene V. Koonin
Keyword(s):  
Linear Regression ◽  
Cancer Genomics ◽  
Driver Mutations ◽  
Bipartite Network ◽  
Cancer Driver ◽  
Cancer Mutation ◽  
Cancer Types ◽  
Extensive Information ◽  
The Mean ◽  
Network Component

AbstractCancer genomics has produced extensive information on cancer-associated genes but the number and specificity of cancer driver mutations remains a matter of debate. We constructed a bipartite network in which 7665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer-associated genes. We show that 27% of the tumors can be assigned to statistically supported modules, most of which encompass 1-2 cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene-specificity of driver mutations. Linear regression of the mutational loads in cancer-associated genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers is ~2, with a range of 1 to 5. Cancers that are associated to modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

scholarly journals Identifying cancer type specific oncogenes and tumor suppressors using limited size data

2016 ◽  
Vol 14 (06) ◽  
pp. 1650031 ◽  
Author(s):  
Ana B. Pavel ◽  
Cristian I. Vasile
Keyword(s):  
Tumor Suppressors ◽  
Molecular Mechanisms ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Cancer Types

Cancer is a complex and heterogeneous genetic disease. Different mutations and dysregulated molecular mechanisms alter the pathways that lead to cell proliferation. In this paper, we explore a method which classifies genes into oncogenes (ONGs) and tumor suppressors. We optimize this method to identify specific (ONGs) and tumor suppressors for breast cancer, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and colon adenocarcinoma (COAD), using data from the cancer genome atlas (TCGA). A set of genes were previously classified as ONGs and tumor suppressors across multiple cancer types (Science 2013). Each gene was assigned an ONG score and a tumor suppressor score based on the frequency of its driver mutations across all variants from the catalogue of somatic mutations in cancer (COSMIC). We evaluate and optimize this approach within different cancer types from TCGA. We are able to determine known driver genes for each of the four cancer types. After establishing the baseline parameters for each cancer type, we identify new driver genes for each cancer type, and the molecular pathways that are highly affected by them. Our methodology is general and can be applied to different cancer subtypes to identify specific driver genes and improve personalized therapy.

scholarly journals Learning the mutational landscape of the cancer genome

2021 ◽  
Author(s):  
Maxwell A Sherman ◽  
Adam Yaari ◽  
Oliver Priebe ◽  
Felix Dietlein ◽  
Po-Ru Loh ◽  
...  
Keyword(s):  
Deep Neural Networks ◽  
Mutation Rates ◽  
Untranslated Regions ◽  
Driver Mutations ◽  
Web Interface ◽  
Cancer Driver ◽  
Genome Wide ◽  
Cancer Types ◽  
Neutral Mutations ◽  
Proliferative Advantage

An ongoing challenge to better understand and treat cancer is to distinguish neutral mutations, which do not affect tumor fitness, from those that provide a proliferative advantage. However, the variability of mutation rates has limited our ability to model patterns of neutral mutations and therefore identify cancer driver mutations. Here, we predict cancer-specific mutation rates genome-wide by leveraging deep neural networks to learn mutation rates within kilobase-scale regions and then refining these estimates to test for evidence of selection on combinations of mutations by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and used these maps to identify new putative drivers in understudied regions of the genome including cryptic alternative-splice sites, 5 prime untranslated regions and infrequently mutated genes. These results, available for exploration via web interface, indicate the potential for high-resolution neutral mutation models to empower further driver discovery as cancer sequencing cohorts grow.

scholarly journals Glioblastoma signature in the DNA of blood-derived cells

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256831
Author(s):  
Siddharth Jain ◽  
Bijan Mazaheri ◽  
Netanel Raviv ◽  
Jehoshua Bruck
Keyword(s):  
Cancer Detection ◽  
Tandem Repeat ◽  
Early Cancer ◽  
Current Approach ◽  
Driver Mutations ◽  
Genetic Mutations ◽  
Cancer Driver ◽  
Cancer Prediction ◽  
Cancer Types ◽  
Continuous Accumulation

Current approach for the detection of cancer is based on identifying genetic mutations typical to tumor cells. This approach is effective only when cancer has already emerged, however, it might be in a stage too advanced for effective treatment. Cancer is caused by the continuous accumulation of mutations; is it possible to measure the time-dependent information of mutation accumulation and predict the emergence of cancer? We hypothesize that the mutation history derived from the tandem repeat regions in blood-derived DNA carries information about the accumulation of the cancer driver mutations in other tissues. To validate our hypothesis, we computed the mutation histories from the tandem repeat regions in blood-derived exomic DNA of 3874 TCGA patients with different cancer types and found a statistically significant signal with specificity ranging from 66% to 93% differentiating Glioblastoma patients from other cancer patients. Our approach and findings offer a new direction for future cancer prediction and early cancer detection based on information derived from blood-derived DNA.

scholarly journals Germline loss of MBD4 predisposes to leukaemia due to a mutagenic cascade driven by 5mC

2017 ◽  
Author(s):  
Mathijs A. Sanders ◽  
Edward Chew ◽  
Christoffer Flensburg ◽  
Annelieke Zeilemaker ◽  
Sarah E. Miller ◽  
...  
Keyword(s):  
Cytosine Methylation ◽  
Excision Repair ◽  
Driver Mutations ◽  
Dna Glycosylases ◽  
Mammalian Development ◽  
Cancer Predisposition Syndrome ◽  
Base Excision ◽  
Serial Samples ◽  
Biallelic Mutations ◽  
Cg Dinucleotides

Cytosine methylation is essential for normal mammalian development, yet also provides a major mutagenic stimulus. Methylcytosine (5mC) is prone to spontaneous deamination, which introduces cytosine to thymine transition mutations (C>T) upon replication1. Cells endure hundreds of 5mC deamination events each day and an intricate repair network is engaged to restrict this damage. Central to this network are the DNA glycosylases MBD42 and TDG3,4, which recognise T:G mispairing and initiate base excision repair (BER). Here we describe a novel cancer predisposition syndrome resulting from germline biallelic inactivation of MBD4 that leads to the development of acute myeloid leukaemia (AML). These leukaemias have an extremely high burden of C>T mutations, specifically in the context of methylated CG dinucleotides (CG>TG). This dependence on 5mC as a source of mutations may explain the remarkable observation that MBD4-deficient AMLs share a common set of driver mutations, including biallelic mutations in DNMT3A and hotspot mutations in IDH1/IDH2. By assessing serial samples taken over the course of treatment, we highlight a critical interaction with somatic mutations in DNMT3A that accelerates leukaemogenesis and accounts for the conserved path to AML. MBD4-deficiency was also detected, rarely, in sporadic cancers, which display the same mutational signature. Collectively these cancers provide a model of 5mC-dependent hypermutation and reveal factors that shape its mutagenic influence.

scholarly journals Pervasive conditional selection of driver mutations and modular epistasis networks in cancer

2022 ◽  
Author(s):  
Jaime Iranzo ◽  
George Gruenhagen ◽  
Jorge Calle-Espinosa ◽  
Eugene V. Koonin
Keyword(s):  
Cancer Genomics ◽  
Mutual Exclusion ◽  
Driver Mutations ◽  
Tumor Evolution ◽  
Cancer Genes ◽  
Cancer Subtypes ◽  
Cancer Driver ◽  
Conditional Selection ◽  
Tcga Dataset ◽  
Complex Scenario

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistatic interactions and their quantitative effect on tumor evolution remains a challenge. We developed a method to quantify COnditional SELection on the Excess of Nonsynonymous Substitutions (Coselens) in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identified 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection accounts for 25-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario, where gene-specific across-pathway interactions shape differentiated cancer subtypes.

scholarly journals Cancer-mutation network and the number and specificity of driver mutations

2018 ◽  
Vol 115 (26) ◽  
pp. E6010-E6019 ◽  
Author(s):  
Jaime Iranzo ◽  
Iñigo Martincorena ◽  
Eugene V. Koonin
Keyword(s):  
Cancer Genomics ◽  
Driver Mutations ◽  
Bipartite Network ◽  
Cancer Genes ◽  
Cancer Driver ◽  
Cancer Mutation ◽  
Cancer Types ◽  
Extensive Information ◽  
The Mean ◽  
Network Component

Cancer genomics has produced extensive information on cancer-associated genes, but the number and specificity of cancer-driver mutations remains a matter of debate. We constructed a bipartite network in which 7,665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer genes. We show that about 27% of the tumors can be assigned to statistically supported modules, most of which encompass one or two cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene specificity of driver mutations. Linear regression of the mutational loads in cancer genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers in known cancer genes is approximately two, with a range of one to five. Cancers that are associated with modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

scholarly journals Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Harald Vöhringer ◽  
Arne Van Hoeck ◽  
Edwin Cuppen ◽  
Moritz Gerstung
Keyword(s):  
Somatic Hypermutation ◽  
Excision Repair ◽  
Metastatic Cancer ◽  
Strand Break ◽  
Lymphoid Leukaemia ◽  
Mutational Signatures ◽  
Transcription Start Sites ◽  
Nucleotide Excision ◽  
Cancer Types ◽  
Underlying Processes

AbstractWe present TensorSignatures, an algorithm to learn mutational signatures jointly across different variant categories and their genomic localisation and properties. The analysis of 2778 primary and 3824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that all signatures operate dynamically in response to genomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis in 7 different cancer types. The algorithm also extracts distinct signatures of replication- and double strand break repair-driven mutagenesis by APOBEC3A and 3B with differential numbers and length of mutation clusters. Finally, TensorSignatures reproduces a signature of somatic hypermutation generating highly clustered variants at transcription start sites of active genes in lymphoid leukaemia, distinct from a general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types. In summary, TensorSignatures elucidates complex mutational footprints by characterising their underlying processes with respect to a multitude of genomic variables.

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