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

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.

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

10.1101/151977 ◽

2017 ◽

Cited By ~ 1

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.

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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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In silico saturation mutagenesis of cancer genes

10.1101/2020.06.03.130211 ◽

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.

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PATH-40. SPORADIC NF2 WILD-TYPE MULTIPLE MENINGIOMAS HARBOR DISTINCT DRIVER MUTATIONS

Neuro-Oncology ◽

10.1093/neuonc/noab196.492 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi124-vi124

Author(s):

Insa Prilop ◽

Thomas Pinzer ◽

Daniel Cahill ◽

Priscilla Brastianos ◽

Gabriele Schackert ◽

...

Keyword(s):

Hot Spot ◽

Low Frequency ◽

Neurofibromatosis Type ◽

Driver Mutations ◽

Wild Type ◽

Driver Genes ◽

Who Grade ◽

Genetic Changes ◽

Histologic Subtype ◽

Multiple Meningiomas

Abstract OBJECTIVE Multiple meningiomas (MM) are rare and present a unique management challenge. While the mutational landscape of single meningiomas has been extensively studied, understanding the molecular pathogenesis of sporadic MM remains incomplete. The objective of this study is to elucidate the genetic features of sporadic MM. METHODS We identified nine patients with MM (n=19) defined as ≥2 spatially separated synchronous or metachronous meningiomas. We profiled genetic changes in these tumors using next-generation sequencing (NGS) assay that covers a large number of targetable and frequently mutated genes in meningiomas including AKT1, KLF4, NF2, PIK3CA/PIK3R1, POLR2A, SMARCB1, SMO, SUFU, TRAF7, and the TERT promoter. RESULTS Most of MM were WHO grade 1 (n= 16, 84.2%). Within individual patients, no driver mutation was shared between separate tumors. All but two cases harbored different hot spot mutations in known meningioma-driver genes like TRAF7 (n= 5), PIK3CA (n= 4), AKT1 (n= 3), POLR2A (n=1) and SMO (n= 1). Moreover, individual tumors differed in histologic subtype in 8/9 patients. The low frequency of NF2 mutations in our series stands in contrast to previous studies that included hereditary cases arising in the setting of neurofibromatosis type 2 (NF2). CONCLUSIONS Our findings provide evidence for genomic inter-tumor heterogeneity and an independent molecular origin of sporadic NF2 wild-type MM. Furthermore, these findings suggest that genetic characterization of each lesion is warranted in sporadic MM.

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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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Web-based platform to identify cancer driver mutations across tumor types based on new and existing sequencing data

Science-Business eXchange ◽

10.1038/scibx.2013.1107 ◽

2013 ◽

Vol 6 (39) ◽

pp. 1107-1107

Keyword(s):

Driver Mutations ◽

Sequencing Data ◽

Web Based ◽

Cancer Driver ◽

Tumor Types

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The landscape of actionable genomic alterations in cell-free circulating tumor DNA from 21,807 advanced cancer patients

10.1101/233205 ◽

2017 ◽

Cited By ~ 2

Author(s):

Oliver A. Zill ◽

Kimberly C. Banks ◽

Stephen R. Fairclough ◽

Stefanie A. Mortimer ◽

James V. Vowles ◽

...

Keyword(s):

Deep Sequencing ◽

Clonal Evolution ◽

Patient Treatment ◽

Large Set ◽

Sequencing Analysis ◽

Cancer Genes ◽

Sequencing Data ◽

Mutual Exclusivity ◽

Advanced Cancer Patients ◽

Driver Genes

AbstractCell-free DNA (cfDNA) sequencing provides a non-invasive method for obtaining actionable genomic information to guide personalized cancer treatment, but the presence of multiple alterations in circulation related to treatment and tumor heterogeneity pose analytical challenges. We present the somatic mutation landscape of 70 cancer genes from cfDNA deep-sequencing analysis of 21,807 patients with treated, late-stage cancers across >50 cancer types. Patterns and prevalence of cfDNA alterations in major driver genes for non-small cell lung, breast, and colorectal cancer largely recapitulated those from tumor tissue sequencing compendia (TCGA and COSMIC), with the principle differences in alteration prevalence being due to patient treatment. This highly sensitive cfDNA sequencing assay revealed numerous subclonal tumor-derived alterations, expected as a result of clonal evolution, but leading to an apparent departure from mutual exclusivity in treatment-naïve tumors. To facilitate interpretation of this added complexity, we developed methods to identify cfDNA copy-number driver alterations and cfDNA clonality. Upon applying these methods, robust mutual exclusivity was observed among predicted truncal driver cfDNA alterations, in effect distinguishing tumor-initiating alterations from secondary alterations. Treatment-associated resistance, including both novel alterations and parallel evolution, was common in the cfDNA cohort and was enriched in patients with targetable driver alterations. Together these retrospective analyses of a large set of cfDNA deep-sequencing data reveal subclonal structures and emerging resistance in advanced solid tumors.

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Identifying structural variants using linked-read sequencing data

10.1101/190454 ◽

2017 ◽

Cited By ~ 5

Author(s):

Rebecca Elyanow ◽

Hsin-Ta Wu ◽

Benjamin J. Raphael

Keyword(s):

Cancer Cell Line ◽

Whole Genome Sequencing Data ◽

Sequence Information ◽

The Novel ◽

Cancer Genes ◽

Structural Variants ◽

Sequencing Data ◽

Individual Genome ◽

Large Deletions ◽

Cancer Genomes

AbstractStructural variation, including large deletions, duplications, inversions, translocations, and other rearrangements, is common in human and cancer genomes. A number of methods have been developed to identify structural variants from Illumina short-read sequencing data. However, reliable identification of structural variants remains challenging because many variants have breakpoints in repetitive regions of the genome and thus are difficult to identify with short reads. The recently developed linked-read sequencing technology from 10X Genomics combines a novel barcoding strategy with Illumina sequencing. This technology labels all reads that originate from a small number (~5-10) DNA molecules ~50Kbp in length with the same molecular barcode. These barcoded reads contain long-range sequence information that is advantageous for identification of structural variants. We present Novel Adjacency Identification with Barcoded Reads (NAIBR), an algorithm to identify structural variants in linked-read sequencing data. NAIBR predicts novel adjacencies in a individual genome resulting from structural variants using a probabilistic model that combines multiple signals in barcoded reads. We show that NAIBR outperforms several existing methods for structural variant identification – including two recent methods that also analyze linked-reads – on simulated sequencing data and 10X whole-genome sequencing data from the NA12878 human genome and the HCC1954 breast cancer cell line. Several of the novel somatic structural variants identified in HCC1954 overlap known cancer genes.

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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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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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