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

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.

2018 ◽  
Vol 115 (26) ◽  
pp. E6010-E6019 ◽  
Author(s):  
Jaime Iranzo ◽  
Iñigo Martincorena ◽  
Eugene V. Koonin

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.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan

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.


2020 ◽  
Vol 49 (D1) ◽  
pp. D1289-D1301 ◽  
Author(s):  
Tao Wang ◽  
Shasha Ruan ◽  
Xiaolu Zhao ◽  
Xiaohui Shi ◽  
Huajing Teng ◽  
...  

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.


2017 ◽  
Author(s):  
Jaime Iranzo ◽  
Iñigo Martincorena ◽  
Eugene V. Koonin

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.


2020 ◽  
Author(s):  
László Bányai ◽  
Mária Trexler ◽  
Krisztina Kerekes ◽  
Orsolya Csuka ◽  
László Patthy

AbstractA major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes that are positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. In the present work we have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations. Oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.


2014 ◽  
Author(s):  
Özgün Babur ◽  
Mithat Gönen ◽  
Bülent Arman Aksoy ◽  
Nikolaus Schultz ◽  
Giovanni Ciriello ◽  
...  

Recent cancer genome studies have identified numerous genomic alterations in cancer genomes. It is hypothesized that only a fraction of these genomic alterations drive the progression of cancer -- often called driver mutations. Current sample sizes for cancer studies, often in the hundreds, are sufficient to detect pivotal drivers solely based on their high frequency of alterations. In cases where the alterations for a single function are distributed among multiple genes of a common pathway, however, single gene alteration frequencies might not be statistically significant. In such cases, we expect to observe that most samples are altered in only one of those alternative genes because additional alterations would not convey an additional selective advantage to the tumor. This leads to a mutual exclusion pattern of alterations, that can be exploited to identify these groups. We developed a novel method for the identification of sets of mutually exclusive gene alterations in a signaling network. We scan the groups of genes with a common downstream effect, using a mutual exclusivity criterion that makes sure that each gene in the group significantly contributes to the mutual exclusivity pattern. We have tested the method on all available TCGA cancer genomics datasets, and detected multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.


PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242780
Author(s):  
Houriiyah Tegally ◽  
Kevin H. Kensler ◽  
Zahra Mungloo-Dilmohamud ◽  
Anisah W. Ghoorah ◽  
Timothy R. Rebbeck ◽  
...  

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.


2018 ◽  
Author(s):  
Matthew A. Reyna ◽  
David Haan ◽  
Marta Paczkowska ◽  
Lieven P.C. Verbeke ◽  
Miguel Vazquez ◽  
...  

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.


2018 ◽  
Author(s):  
Paul Ashford ◽  
Camilla S.M. Pang ◽  
Aurelio A. Moya-García ◽  
Tolulope Adeyelu ◽  
Christine A. Orengo

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.


Author(s):  
Zedong Jiang ◽  
Gaoming Liao ◽  
Yiran Yang ◽  
Yujia Lan ◽  
Liwen Xu ◽  
...  

Somatic mutations accumulate over time in cancer cells as a consequence of mutational processes. However, the role of mutational processes in carcinogenesis remains poorly understood. Here, we infer the causal relationship between mutational processes and somatic mutations in 5,828 samples spanning 34 cancer subtypes. We found most mutational processes cause abundant recurrent mutations in cancer genes, while exceptionally ultraviolet exposure and altered activity of the error-prone polymerase bring a large number of recurrent non-driver mutations. Furthermore, some mutations are specifically induced by a certain mutational process, such as IDH1 p.R132H which is mainly caused by spontaneous deamination of 5-methylcytosine. At the pathway level, clock-like mutational processes extensively trigger mutations to dysregulate cancer signal transduction pathways. In addition, APOBEC mutational process destroys DNA double-strand break repair pathway, and bladder cancer patients with high APOBEC activity, though with homologous recombination proficient, show a significantly longer overall survival with platinum regimens. These findings help to understand how mutational processes act on the genome to promote carcinogenesis, and further, presents novel insights for cancer prevention and treatment, as our results showing, APOBEC mutagenesis and HRD synergistically contributed to the clinical benefits of platinum-based treatment.


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