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

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.

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

10.1101/237016 ◽

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.

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

Nucleic Acids Research ◽

10.1093/nar/gkaa1033 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D1289-D1301 ◽

Cited By ~ 2

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.

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

10.1101/2022.01.10.475617 ◽

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.

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Ranking cancer drivers via betweenness-based outlier detection and random walks

BMC Bioinformatics ◽

10.1186/s12859-021-03989-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Cesim Erten ◽

Aissa Houdjedj ◽

Hilal Kazan

Keyword(s):

Cancer Genomics ◽

Interaction Network ◽

Molecular Data ◽

Alternative Methods ◽

Patient Specific ◽

Cancer Genes ◽

Driver Genes ◽

Cancer Driver ◽

Protein Protein Interaction ◽

Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

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Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽

10.1371/journal.pone.0242780 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242780

Author(s):

Houriiyah Tegally ◽

Kevin H. Kensler ◽

Zahra Mungloo-Dilmohamud ◽

Anisah W. Ghoorah ◽

Timothy R. Rebbeck ◽

...

Keyword(s):

Gene Expression ◽

Large Scale ◽

Cancer Genomics ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Driver Mutations ◽

Literature Mining ◽

Cancer Genes ◽

Multiple Cancer ◽

Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

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Pathway and network analysis of more than 2,500 whole cancer genomes

10.1101/385294 ◽

2018 ◽

Cited By ~ 4

Author(s):

Matthew A. Reyna ◽

David Haan ◽

Marta Paczkowska ◽

Lieven P.C. Verbeke ◽

Miguel Vazquez ◽

...

Keyword(s):

Rna Splicing ◽

Driver Mutations ◽

Cancer Genes ◽

Protein Coding ◽

Cancer Driver ◽

Network Analyses ◽

Protein Coding Genes ◽

Cancer Genomes ◽

Tumor Types ◽

Promoter Mutations

AbstractThe 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 notablyTERTpromoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression inTP53, TLE4, andTCF4. We found 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 was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. 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.

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A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

10.1101/399014 ◽

2018 ◽

Author(s):

Paul Ashford ◽

Camilla S.M. Pang ◽

Aurelio A. Moya-García ◽

Tolulope Adeyelu ◽

Christine A. Orengo

Keyword(s):

Zinc Finger Protein ◽

Point Mutations ◽

Driver Mutations ◽

Cancer Genes ◽

Driver Genes ◽

Recurrent Point ◽

Cancer Driver ◽

Functional Sites ◽

Cancer Driver Genes ◽

Family Based

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) – structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated.Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

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Learning the mutational landscape of the cancer genome

10.1101/2021.08.03.454669 ◽

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.

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ConsensusDriver improves upon individual algorithms for predicting driver alterations in different cancer types and individual patients – a toolbox for precision oncology

10.1101/127985 ◽

2017 ◽

Cited By ~ 2

Author(s):

Denis Bertrand ◽

Sibyl Drissler ◽

Burton Chia ◽

Jia Yu Koh ◽

Li Chenhao ◽

...

Keyword(s):

Cancer Genomics ◽

Clinical Phenotype ◽

Genetic Alterations ◽

Driver Gene ◽

Prediction Methods ◽

Precision Oncology ◽

Cancer Driver ◽

Recurrent Mutations ◽

Cancer Types ◽

Patient Subgroups

AbstractBackgroundIn recent years, several large-scale cancer genomics studies have helped generate detailed molecular profiling datasets for many cancer types and thousands of patients. These datasets provide a unique resource for studying cancer driver prediction methods and their utility for precision oncology, both to predict driver genetic alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.MethodsWe performed the most comprehensive assessment to date of 18 driver gene prediction methods, on more than 3,400 tumour samples, from 15 cancer types, to determine their suitability in guiding precision medicine efforts. These methods have diverse approaches, which can be classified into five categories:functionalimpact on proteins in general (FI) or specific tocancer (FIC),cohort-basedanalysis for recurrent mutations (CBA),mutations withexpressioncorrelation (MEC) and methods that use geneinteractionnetwork-basedanalysis (INA).ResultsThe performance of driver prediction methods varies considerably, with concordance with a gold-standard varying from 9% to 68%. FI methods show relatively poor performance (concordance <22%) while CBA methods provide conservative results, but require large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provide the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of drivers, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity). This tool can be applied to predict driver alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.ConclusionExisting cancer driver prediction methods are based on very different assumptions and each of them can only detect a particular subset of driver events. Consensus-based methods, like ConsensusDriver, are thus a promising approach to harness the strengths of different driver prediction paradigms.

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A Review of Cancer Genetics and Genomics Studies in Africa

Frontiers in Oncology ◽

10.3389/fonc.2020.606400 ◽

2021 ◽

Vol 10 ◽

Cited By ~ 1

Author(s):

Solomon O. Rotimi ◽

Oluwakemi A. Rotimi ◽

Bodour Salhia

Keyword(s):

Cancer Genomics ◽

Cancer Genetics ◽

Cervix Uterus ◽

Cancer Genes ◽

Multiple Cancer ◽

Biological Regulation ◽

Mesh Terms ◽

African Populations ◽

Cancer Types ◽

Black Populations

Cancer is the second leading cause of death globally and is projected to overtake infectious disease as the leading cause of mortality in Africa within the next two decades. Cancer is a group of genomic diseases that presents with intra- and inter-population unique phenotypes, with Black populations having the burden of morbidity and mortality for most types. At large, the prevention and treatment of cancers have been propelled by the understanding of the genetic make-up of the disease of mostly non-African populations. By the same token, there is a wide knowledge gap in understanding the underlying genetic causes of, and genomic alterations associated with, cancer among black Africans. Accordingly, we performed a review of the literature to survey existing studies on cancer genetics/genomics and curated findings pertaining to publications across multiple cancer types conducted on African populations. We used PubMed MeSH terms to retrieve the relevant publications from 1990 to December 2019. The metadata of these publications were extracted using R text mining packages: RISmed and Pubmed.mineR. The data showed that only 0.329% of cancer publications globally were on Africa, and only 0.016% were on cancer genetics/genomics from Africa. Although the most prevalent cancers in Africa are cancers of the breast, cervix, uterus, and prostate, publications representing breast, colorectal, liver, and blood cancers were the most frequent in our review. The most frequently reported cancer genes were BRCA1, BRCA2, and TP53. Next, the genes reported in the reviewed publications’ abstracts were extracted and annotated into three gene ontology classes. Genes in the cellular component class were mostly associated with cell part and organelle part, while those in biological process and molecular function classes were mainly associated with cell process, biological regulation, and binding, and catalytic activity, respectively. Overall, this review highlights the paucity of research on cancer genomics on African populations, identified gaps, and discussed the need for concerted efforts to encourage more research on cancer genomics in Africa.

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