scholarly journals Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

2017 ◽  
Author(s):  
Henrik Hornshøj ◽  
Morten Muhlig Nielsen ◽  
Nicholas A. Sinnott-Armstrong ◽  
Michał P. Świtnicki ◽  
Malene Juul ◽  
...  
Keyword(s):  
Mutational Analysis ◽  
Independent Set ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Cancer Screen ◽  
Genome Wide ◽  
Whole Genomes ◽  
Cancer Types ◽  
Driver Potential ◽  
Pan Cancer

AbstractCancer develops by accumulation of somatic driver mutations, which impact cellular function. Non-coding mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from ten cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including theTERTpromoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g.,PAX5,TOX3,PCF11,MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g.TERTandCDH10) and survival (e.g.CDH9andCDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4,128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., nearGATA3,CDC6,ZNF217andCTCFtranscription factor binding sites). Survival analysis further pointed toMIR122, a known marker of poor prognosis in liver cancer. This screen for significant mutation patterns followed by correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

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 DriverPower: Combined burden and functional impact tests for cancer driver discovery

2017 ◽  
Author(s):  
Shimin Shuai ◽  
Steven Gallinger ◽  
Lincoln Stein ◽  
Keyword(s):  
Driver Mutations ◽  
Functional Interpretation ◽  
Functional Impact ◽  
Genomic Features ◽  
Cancer Driver ◽  
Mutational Burden ◽  
Mutation Model ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Pan Cancer

AbstractWe describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify cancer driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1,373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across a variety of tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2,583 cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Group, DriverPower has the highest F1-score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

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.

Mutational Profiling of Multiple Myeloma Bone Marrow Aspirates As a Clinical Tool for Personalized Treatment of Myeloma

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3990-3990
Author(s):  
Anuj Mahindra ◽  
Dora Dias-Santagata ◽  
Darrell Borger ◽  
Loredana Santo ◽  
Tyler A. Scullen ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Cell Lines ◽  
Research Funding ◽  
Mutational Analysis ◽  
Clinical Laboratory ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Oncogenic Mutations ◽  
Therapeutic Decisions

Abstract Abstract 3990 Introduction: Multiple Myeloma (MM) is increasingly being recognized as a heterogeneous disease. Previous studies have reported various oncogenic mutations in myeloma. Recent genome sequencing studies have revealed activating mutations of BRAF kinase in 4% of patients, a potentially druggable target, underscoring the importance of identifying these and other potential oncogenic mutations. However, a validated methodology for clinical assessment of mutation profiles is lacking. Applying such technologies will be increasingly important to accelerate development of targeted therapies toward tumor-specific oncogenic pathways. Utilizing a high-throughput genotyping platform we evaluated the commonly described somatic mutations in bone marrow aspirates in patients diagnosed with MM. Methods: We developed a CLIA (Clinical Laboratory Improvement Amendments) validated, highly sensitive multiplexed PCR-based assay (SNaPshot) to simultaneously identify 70 genetic loci frequently mutated in 15 cancer genes. This assay has been used at our institution for over 3 years for tumor genotyping and to help guide therapeutic decisions for patients with solid malignancies. To test the value of this approach in patients with MM, we first validated the methodology on MM cell lines followed by testing patient derived bone marrow samples. Results: The detectable mutations in the 8 MM cell lines tested include: MMIR: KRAS 35G>C, G12A mutation; MMIS: KRAS 35G>C, G12A mutation; H929: NRAS 38G>A, G13D mutation; LR5: KRAS 35G>C, G12A mutation; RPMI8226: TP53 743G>A, R248Q mutation;U266: no mutations identified; OPM2: NRAS 182A>T, Q61L mutation; OPM1: TP53 524G>A, R175H mutation. Thirty six bone marrow aspirates from patients with MM have been analyzed to date. Tweny five percent of patients had a detectable mutation, with KRAS mutation in 17%, NRAS in 5% and TP53 in 3%. Additional data in a larger cohort is currently being analyzed and will be presented. Conclusion: Oncogenic driver mutations can be detected by SNaPshot from routinely-collected bone marrow aspirate samples. Experience from lung and breast cancer studies indicates that while the type and frequency of mutation varies somewhat by tumor phenotype, the common oncogenic mutations are often found in isolation. Broad tumor mutational analysis will be helpful to efficiently tailor personalized therapies based on individual tumor genetic profiles. Disclosures: Dias-Santagata: Bio-Reference Laboratories, Inc.: Consultancy. Borger:Bio-Reference Laboratories, Inc: Consultancy. Iafrate:Bio-Reference Laboratories, Inc: Consultancy. Raje:Eli-Lilly: Research Funding; Amgen: Research Funding; Acetylon: Research Funding; Millennium: Consultancy; Celgene: Consultancy; Onyx: Consultancy.

scholarly journals MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

2019 ◽  
Author(s):  
Rafsan Ahmed ◽  
Ilyes Baali ◽  
Cesim Erten ◽  
Evis Hoxha ◽  
Hilal Kazan
Keyword(s):  
Random Walk ◽  
Mutual Exclusion ◽  
Risk Scores ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

AbstractMotivationGenomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.ResultsWe present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions, mutual exclusion, and coverage to identify cancer driver modules. MEXCOWalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples, and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code, and useful scripts are available at:https://github.com/abu-compbio/[email protected]

scholarly journals The FABRIC Cancer Portal: A Ranked Catalogue of Gene Selection in Tumors over the Human Coding Genome

2020 ◽  
Author(s):  
Guy Kelman ◽  
Nadav Brandes ◽  
Michal Linial
Keyword(s):  
Gene Selection ◽  
Robust Statistics ◽  
Cancer Type ◽  
Cancer Genes ◽  
Protein Coding ◽  
Strength Of Evidence ◽  
Related Information ◽  
Cancer Types ◽  
Visual Interface ◽  
Pan Cancer

AbstractWe present The FABRIC Cancer Portal, a comprehensive catalogue of gene selection in cancer covering the entire human coding genome (~18,000 protein-coding genes) across 33 cancer types and pan-cancer. Genes in the portal are ranked according to the strength of evidence for selection in tumor, based on rigorous and robust statistics. Gene selection is quantified by combining genomic data with rich proteomic annotations. The portal includes a selected set of cross-references to the most relevant resources providing genomic, proteomic and cancer-related information. We showcase the portal with known and overlooked cancer genes, demonstrating the usefulness of the portal via its simple visual interface that allows to pivot between gene-centric and cancer-type views. The portal is available at fabric-cancer.huji.ac.il.

scholarly journals MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules

2019 ◽  
Author(s):  
Rafsan Ahmed ◽  
Ilyes Baali ◽  
Cesim Erten ◽  
Evis Hoxha ◽  
Hilal Kazan
Keyword(s):  
Random Walk ◽  
Supplementary Information ◽  
Risk Scores ◽  
Cancer Genes ◽  
Mutual Exclusivity ◽  
Cancer Driver ◽  
Cancer Data ◽  
Ppi Networks ◽  
Cancer Types ◽  
Pan Cancer

Abstract Motivation Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein–protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules. Results We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein–protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits

2020 ◽  
pp. jmedgenet-2019-106799
Author(s):  
Matteo Di Giovannantonio ◽  
Benjamin HL Harris ◽  
Ping Zhang ◽  
Isaac Kitchen-Smith ◽  
Lingyun Xiong ◽  
...  
Keyword(s):  
Cancer Risk ◽  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Strong Association ◽  
Tissue Type ◽  
Cancer Genes ◽  
Anthropometric Measures ◽  
Genome Wide ◽  
Increased Risk ◽  
Cancer Types

BackgroundHeight and other anthropometric measures are consistently found to associate with differential cancer risk. However, both genetic and mechanistic insights into these epidemiological associations are notably lacking. Conversely, inherited genetic variants in tumour suppressors and oncogenes increase cancer risk, but little is known about their influence on anthropometric traits.MethodsBy integrating inherited and somatic cancer genetic data from the Genome-Wide Association Study Catalog, expression Quantitative Trait Loci databases and the Cancer Gene Census, we identify SNPs that associate with different cancer types and differential gene expression in at least one tissue type, and explore the potential pleiotropic associations of these SNPs with anthropometric traits through SNP-wise association in a cohort of 500,000 individuals.ResultsWe identify three regulatory SNPs for three important cancer genes, FANCA, MAP3K1 and TP53 that associate with both anthropometric traits and cancer risk. Of particular interest, we identify a previously unrecognised strong association between the rs78378222[C] SNP in the 3' untranslated region (3'-UTR) of TP53 and both increased risk for developing non-melanomatous skin cancer (OR=1.36 (95% 1.31 to 1.41), adjusted p=7.62E−63), brain malignancy (OR=3.12 (2.22 to 4.37), adjusted p=1.43E−12) and increased standing height (adjusted p=2.18E−24, beta=0.073±0.007), lean body mass (adjusted p=8.34E−37, beta=0.073±0.005) and basal metabolic rate (adjusted p=1.13E−31, beta=0.076±0.006), thus offering a novel genetic link between these anthropometric traits and cancer risk.ConclusionOur results clearly demonstrate that heritable variants in key cancer genes can associate with both differential cancer risk and anthropometric traits in the general population, thereby lending support for a genetic basis for linking these human phenotypes.

scholarly journals Selection for or against Escape from Nonsense Mediated Decay is a Novel Signature for the Detection of Cancer Genes

2021 ◽  
Author(s):  
Runjun D Kumar ◽  
Briana A Burns ◽  
Paul J Vandeventer ◽  
Pamela N Luna ◽  
Chad A Shaw
Keyword(s):  
Functional Characterization ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Gene Products ◽  
Cancer Genes ◽  
Nonsense Mediated Decay ◽  
Novel Approach ◽  
Cancer Genome Atlas ◽  
Selection For ◽  
Pan Cancer

Escape from nonsense mediated decay (NMD-) can produce activated or inactivated gene products, and bias in rates of escape can identify functionally important genes in germline disease. We hypothesized that the same would be true of cancer genes, and tested for NMD- bias within The Cancer Genome Atlas pan-cancer somatic mutation dataset. We identify 29 genes that show significantly elevated or suppressed rates of NMD-. This novel approach to cancer gene discovery reveals genes not previously cataloged as potentially tumorigenic, and identifies many potential driver mutations in known cancer genes for functional characterization.

scholarly journals Pathway-based dissection of the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

2016 ◽  
Author(s):  
Francesco Iorio ◽  
Luz Garcia-Alonso ◽  
Jonathan S. Brammeld ◽  
Iñigo Martincorena ◽  
David R. Wille ◽  
...  
Keyword(s):  
Somatic Mutations ◽  
Population Level ◽  
Computational Method ◽  
Driver Mutations ◽  
Cancer Type ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Hallmarks ◽  
Pathway Gene ◽  
Cancer Types

ABSTRACTCancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages.Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks’ predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.

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