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

2018 ◽  
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.

scholarly journals Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

2017 ◽  
Author(s):  
Esther Rheinbay ◽  
Morten Muhlig Nielsen ◽  
Federico Abascal ◽  
Grace Tiao ◽  
Henrik Hornshøj ◽  
...  
Keyword(s):  
Recurrent Events ◽  
Point Mutations ◽  
Regulatory Elements ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Protein Coding ◽  
Cancer Driver ◽  
Protein Coding Genes ◽  
Cancer Genomes ◽  
Non Coding Rnas

AbstractDiscovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5’UTRs ofTP53, RFTN1, RNF34,andMTG2,in the 3’UTRs ofNFKBIZandTOB1,and in the non-coding RNARMRP.We provide evidence that the previously reported non-coding RNAsNEAT1andMALAT1may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyondTERT.

scholarly journals Expanding discovery from cancer genomes by integrating protein network analyses with in vivo tumorigenesis assays

2017 ◽  
Author(s):  
Heiko Horn ◽  
Michael S. Lawrence ◽  
Candace R. Chouinard ◽  
Yashaswi Shrestha ◽  
Jessica Xin Hu ◽  
...  
Keyword(s):  
Statistical Tests ◽  
Cancer Genes ◽  
Driver Genes ◽  
Network Information ◽  
Network Analyses ◽  
Genome Data ◽  
Cancer Genomes ◽  
Complement Gene ◽  
Tumor Types

AbstractApproaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

scholarly journals In silico saturation mutagenesis of cancer genes

2020 ◽  
Author(s):  
Ferran Muiños ◽  
Francisco Martinez-Jimenez ◽  
Oriol Pich ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
In Silico ◽  
Tumor Development ◽  
Saturation Mutagenesis ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Mutation Probability ◽  
Tumor Types ◽  
Tumor Somatic Mutations

SummaryExtensive bioinformatics analysis of datasets of tumor somatic mutations data have revealed the presence of some 500-600 cancer driver genes. The identification of all potential driver mutations affecting cancer genes is essential to implement precision cancer medicine and to understand the interplay of mutation probability and selection in tumor development. Here, we present an in silico saturation mutagenesis approach to identify all driver mutations in 568 cancer genes across 66 tumor types. For most cancer genes the mutation probability across tissues --underpinned by active mutational processes-- influences which driver variants have been observed, although this differs significantly between tumor suppressor and oncogenes. The role of selection is apparent in some of the latter, the observed and unobserved driver mutations of which are equally likely to occur. The number of potential driver mutations in a cancer gene roughly determines how many mutations are available for detection across newly sequenced tumors.

scholarly journals A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

2015 ◽  
Author(s):  
Heiko Horn ◽  
Michael S. Lawrence ◽  
Jessica Xin Hu ◽  
Elizabeth Worstell ◽  
Nina Ilic ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Cancer Biology ◽  
Low Frequency ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Driver Genes ◽  
Significance Level ◽  
Cancer Genomes ◽  
Tumor Types

Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations, and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene’s network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.

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 Quantifying gene selection in cancer through protein functional alteration bias

2019 ◽  
Vol 47 (13) ◽  
pp. 6642-6655 ◽  
Author(s):  
Nadav Brandes ◽  
Nathan Linial ◽  
Michal Linial
Keyword(s):  
Somatic Mutations ◽  
Gene Selection ◽  
De Novo ◽  
Cancer Genes ◽  
Driver Genes ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Machine Learning Model ◽  
Implicit And Explicit ◽  
False Discoveries

Abstract Compiling the catalogue of genes actively involved in cancer is an ongoing endeavor, with profound implications to the understanding and treatment of the disease. An abundance of computational methods have been developed to screening the genome for candidate driver genes based on genomic data of somatic mutations in tumors. Existing methods make many implicit and explicit assumptions about the distribution of random mutations. We present FABRIC, a new framework for quantifying the selection of genes in cancer by assessing the effects of de-novo somatic mutations on protein-coding genes. Using a machine-learning model, we quantified the functional effects of ∼3M somatic mutations extracted from over 10 000 human cancerous samples, and compared them against the effects of all possible single-nucleotide mutations in the coding human genome. We detected 593 protein-coding genes showing statistically significant bias towards harmful mutations. These genes, discovered without any prior knowledge, show an overwhelming overlap with known cancer genes, but also include many overlooked genes. FABRIC is designed to avoid false discoveries by comparing each gene to its own background model using rigorous statistics, making minimal assumptions about the distribution of random somatic mutations. The framework is an open-source project with a simple command-line interface.

scholarly journals Chronic lymphocytic leukemia: interplay between noncoding RNAs and protein-coding genes

Blood ◽  
2009 ◽  
Vol 114 (23) ◽  
pp. 4761-4770 ◽  
Author(s):  
George A. Calin ◽  
Carlo M. Croce
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Noncoding Rnas ◽  
Malignant Potential ◽  
Lymphocytic Leukemia ◽  
Leukemic Cells ◽  
Cancer Genes ◽  
New Paradigm ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Molecular Oncology

Abstract One of the most unexpected and fascinating discoveries in oncology over the past few years is the interplay between abnormalities in protein-coding genes and noncoding RNAs (ncRNAs) that is causally involved in cancer initiation, progression, and dissemination. MicroRNAs (miRNAs), small regulatory ncRNAs, are involved in the pathogenesis of all types of human cancers, including leukemias, mainly via dysregulation of expression of cancer genes. Increasing evidence shows that miRNAs can work as tumor suppressors (inhibiting malignant potential) or oncogenes (activating malignant potential). Researchers first identified this new paradigm of molecular oncology in patients with chronic lymphocytic leukemia (CLL). Understanding the roles of miRNAs and other ncRNAs in leukemic cells is not only uncovering a new layer of gene regulation but also providing new markers for improved diagnosis and prognosis, as well as novel therapeutic options for CLL patients. Herein we focus on the roles of miRNAs and ultraconserved ncRNA genes in CLL, highlighting what is already known about their function, proposing a novel model of CLL predisposition and progression, and describing the challenges for the near future.

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

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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