OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers

Abstract Motivation Identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes. Results We have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method can identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. Our results indicate that OncodriveCLUSTL can be applied to the analysis of non-coding genomic elements and non-human mutations data. Availability and implementation OncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public License. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers

10.1101/500132 ◽

2018 ◽

Author(s):

Claudia Arnedo-Pac ◽

Loris Mularoni ◽

Ferran Muiños ◽

Abel Gonzalez-Perez ◽

Nuria Lopez-Bigas

Keyword(s):

Positive Selection ◽

Clustering Algorithm ◽

Source Code ◽

Cancer Genes ◽

Driver Genes ◽

Local Background ◽

Nucleotide Context ◽

Bona Fide ◽

Cancer Drivers ◽

Genomic Regions

AbstractSummaryThe identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes. We have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method is able to identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. We show that OncodriveCLUSTL may be applied to the analysis of non-coding genomic elements and non-human mutations data.Availability and implementationOncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public [email protected]

Download Full-text

Discovering the drivers of clonal hematopoiesis

10.1101/2020.10.22.350140 ◽

2020 ◽

Cited By ~ 1

Author(s):

Oriol Pich ◽

Iker Reyes-Salazar ◽

Abel Gonzalez-Perez ◽

Nuria Lopez-Bigas

Keyword(s):

Positive Selection ◽

Molecular Mechanisms ◽

Somatic Mutations ◽

Cancer Genomics ◽

Variant Calling ◽

Selective Advantage ◽

Cancer Genes ◽

Driver Genes ◽

Hematopoietic Stem ◽

Clonal Hematopoiesis

AbstractMutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

Download Full-text

pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers

10.1101/2020.04.23.058727 ◽

2020 ◽

Author(s):

Vu VH Pham ◽

Lin Liu ◽

Cameron P Bracken ◽

Thin Nguyen ◽

Gregory J Goodall ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Progression ◽

Gene Networks ◽

Supplementary Information ◽

Cancer Type ◽

Breast Cancer Patients ◽

Driver Genes ◽

Individual Level ◽

Novel Method ◽

Cancer Drivers

AbstractMotivationUnravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalised methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.ResultsWe propose the novel method, pDriver, to discover personalised cancer drivers. pDriver includes two stages: (1) Constructing gene networks for each cancer patient and (2) Discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyse the predicted personalised drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.Availability and implementationpDriver is available at https://github.com/pvvhoang/[email protected] informationSupplementary data are available at Bioinformatics online.

Download Full-text

DriveWays: A Method for Identifying Possibly Overlapping Driver Pathways in Cancer

10.1101/2020.04.01.015388 ◽

2020 ◽

Author(s):

Ilyes Baali ◽

Cesim Erten ◽

Hilal Kazan

Keyword(s):

Optimization Problem ◽

Network Connectivity ◽

Supplementary Information ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Data ◽

Cancer Drivers ◽

Definition Of ◽

Almost All ◽

Pan Cancer

AbstractMotivationThe majority of the previous methods for identifying cancer driver modules output non-overlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution.ResultsWe provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes.We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWays’s output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways.AvailabilityThe data, the source code, and useful scripts are available at: https://github.com/abu-compbio/DriveWaysSupplementary informationSupplementary data are available at Biorxiv.

Download Full-text

Accurate loop calling for 3D genomic data with cLoops

Bioinformatics ◽

10.1093/bioinformatics/btz651 ◽

2019 ◽

Cited By ~ 4

Author(s):

Yaqiang Cao ◽

Zhaoxiong Chen ◽

Xingwei Chen ◽

Daosheng Ai ◽

Guoyu Chen ◽

...

Keyword(s):

Clustering Algorithm ◽

Genome Mapping ◽

Statistical Significance ◽

Regulatory Elements ◽

Data Type ◽

Supplementary Information ◽

Sequencing Data ◽

3D Genome ◽

Local Background ◽

Hardware Costs

Abstract Motivation Sequencing-based 3D genome mapping technologies can identify loops formed by interactions between regulatory elements hundreds of kilobases apart. Existing loop-calling tools are mostly restricted to a single data type, with accuracy dependent on a predefined resolution contact matrix or called peaks, and can have prohibitive hardware costs. Results Here, we introduce cLoops (‘see loops’) to address these limitations. cLoops is based on the clustering algorithm cDBSCAN that directly analyzes the paired-end tags (PETs) to find candidate loops and uses a permuted local background to estimate statistical significance. These two data-type-independent processes enable loops to be reliably identified for both sharp and broad peak data, including but not limited to ChIA-PET, Hi-C, HiChIP and Trac-looping data. Loops identified by cLoops showed much less distance-dependent bias and higher enrichment relative to local regions than existing tools. Altogether, cLoops improves accuracy of detecting of 3D-genomic loops from sequencing data, is versatile, flexible, efficient, and has modest hardware requirements. Availability and implementation cLoops with documentation and example data are freely available at: https://github.com/YaqiangCao/cLoops. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Prediction of cancer driver genes through network-based moment propagation of mutation scores

Bioinformatics ◽

10.1093/bioinformatics/btaa452 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i508-i515 ◽

Cited By ~ 1

Author(s):

Anja C Gumpinger ◽

Kasper Lage ◽

Heiko Horn ◽

Karsten Borgwardt

Keyword(s):

Biological Networks ◽

Predictive Performance ◽

Supplementary Information ◽

Summary Statistics ◽

Cancer Genes ◽

Major Step ◽

Score Distribution ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

Abstract Motivation Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in this direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein–protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers. Results We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node’s local neighborhood with network propagation. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially labeled dataset, and develop a cross-validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared with baseline methods and yields a set of promising genes that constitute candidates for further biological validation. Availability and implementation Code available at https://github.com/BorgwardtLab/MoProEmbeddings. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Discovery of cancer driver genes based on nucleotide context

10.1101/485292 ◽

2018 ◽

Author(s):

Felix Dietlein ◽

Donate Weghorn ◽

Amaro Taylor-Weiner ◽

André Richters ◽

Brendan Reardon ◽

...

Keyword(s):

Mutation Rates ◽

Driver Mutations ◽

Supporting Evidence ◽

Cancer Genes ◽

Driver Genes ◽

High Background ◽

Cancer Driver ◽

Nucleotide Context ◽

Cancer Driver Genes ◽

Passenger Mutations

Many cancer genomes contain large numbers of somatic mutations, but few of these mutations drive tumor development. Current approaches to identify cancer driver genes are largely based on mutational recurrence, i.e. they search for genes with an increased number of nonsynonymous mutations relative to the local background mutation rate. Multiple studies have noted that the sensitivity of recurrence-based methods is limited in tumors with high background mutation rates, because passenger mutations dilute their statistical power. Here, we observe that passenger mutations tend to occur in characteristic nucleotide sequence contexts, while driver mutations follow a different distribution pattern determined by the location of functionally relevant genomic positions along the protein-coding sequence. To discover new cancer genes, we searched for genes with an excess of mutations in unusual nucleotide contexts that deviate from the characteristic context around passenger mutations. By applying this statistical framework to whole-exome sequencing data from 12,004 tumors, we discovered a long tail of novel candidate cancer genes with mutation frequencies as low as 1% and functional supporting evidence. Our results show that considering both the number and the nucleotide context around mutations helps identify novel cancer driver genes, particularly in tumors with high background mutation rates.

Download Full-text

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.

Download Full-text

Inferring perturbation profiles of cancer samples

Bioinformatics ◽

10.1093/bioinformatics/btab113 ◽

2021 ◽

Author(s):

Martin Pirkl ◽

Niko Beerenwinkel

Keyword(s):

Indirect Evidence ◽

R Package ◽

The Cancer Genome Atlas ◽

Supplementary Information ◽

Patient Specific ◽

Driver Genes ◽

Cancer Driver ◽

Molecular Alterations ◽

Incomplete Coverage ◽

Gene Perturbations

Abstract Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. Availability and implementation The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

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.

Download Full-text