scholarly journals PRODIGY: personalized prioritization of driver genes

2019 ◽  
Author(s):  
Gal Dinstag ◽  
Ron Shamir
Keyword(s):  
Drug Targets ◽  
Somatic Mutations ◽  
Supplementary Information ◽  
Gene Identification ◽  
Centrality Measures ◽  
Patient Specific ◽  
Driver Gene ◽  
Tree Model ◽  
Driver Genes ◽  
The Impact

Abstract Motivation Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, while most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification, but patient-specific driver gene identification remains a challenge. Methods We developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways. Results In testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further towards personalized medicine and treatment. Availability The Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PRODIGY: personalized prioritization of driver genes

2018 ◽  
Author(s):  
Gal Dinstag ◽  
Ron Shamir
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Somatic Mutations ◽  
Gene Identification ◽  
Centrality Measures ◽  
Patient Specific ◽  
Driver Gene ◽  
Tree Model ◽  
Driver Genes ◽  
The Impact

AbstractBackgroundEvolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, while most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification, but patient-specific driver gene identification remains a challenge.MethodsWe developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways.ResultsIn testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further towards personalized medicine and treatment.AvailabilityThe Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY.

scholarly journals Inferring perturbation profiles of cancer samples

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.

scholarly journals Clonal haematopoiesis in chronic ischaemic heart failure: prognostic role of clone size for DNMT3A- and TET2-driver gene mutations

2020 ◽  
Author(s):  
Birgit Assmus ◽  
Sebastian Cremer ◽  
Klara Kirschbaum ◽  
David Culmann ◽  
Katharina Kiefer ◽  
...  
Keyword(s):  
Heart Failure ◽  
Somatic Mutations ◽  
Prognostic Significance ◽  
Gene Mutations ◽  
Amplicon Sequencing ◽  
Driver Mutations ◽  
Driver Gene ◽  
Roc Curve Analysis ◽  
Driver Genes ◽  
Clone Size

Abstract Aims Somatic mutations of the epigenetic regulators DNMT3A and TET2 causing clonal expansion of haematopoietic cells (clonal haematopoiesis; CH) were shown to be associated with poor prognosis in chronic ischaemic heart failure (CHF). The aim of our analysis was to define a threshold of variant allele frequency (VAF) for the prognostic significance of CH in CHF. Methods and results We analysed bone marrow and peripheral blood-derived cells from 419 patients with CHF by error-corrected amplicon sequencing. Cut-off VAFs were optimized by maximizing sensitivity plus specificity from a time-dependent receiver operating characteristic (ROC) curve analysis from censored data. 56.2% of patients were carriers of a DNMT3A- (N = 173) or a TET2- (N = 113) mutation with a VAF >0.5%, with 59 patients harbouring mutations in both genes. Survival ROC analyses revealed an optimized cut-off value of 0.73% for TET2- and 1.15% for DNMT3A-CH-driver mutations. Five-year-mortality was 18% in patients without any detected DNMT3A- or TET2 mutation (VAF < 0.5%), 29% with only one DNMT3A- or TET2-CH-driver mutations above the respective cut-off level and 42% in patients harbouring both DNMT3A- and TET2-CH-driver mutations above the respective cut-off levels. In carriers of a DNMT3A mutation with VAF ≥ 1.15%, 5-year mortality was 31%, compared with 18% mortality in those with VAF < 1.15% (P = 0.048). Likewise, in patients with TET2 mutations, 5-year mortality was 32% with VAF ≥ 0.73%, compared with 19% mortality with VAF < 0.73% (P = 0.029). Conclusion The present study defines novel threshold levels for clone size caused by acquired somatic mutations in the CH-driver genes DNMT3A and TET2 that are associated with worse outcome in patients with CHF.

scholarly journals DriverGroup: A novel method for identifying driver gene groups

2020 ◽  
Author(s):  
Vu VH Pham ◽  
Lin Liu ◽  
Cameron P Bracken ◽  
Gregory J Goodall ◽  
Jiuyong Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Single Gene ◽  
Gene Interaction ◽  
Computational Method ◽  
Supplementary Information ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Critical Nodes ◽  
Novel Method

AbstractMotivationIdentifying cancer driver genes is a key task in cancer informatics. Most exisiting methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesise that there are driver gene groups that work in concert to regulate cancer and we develop a novel computational method to detect those driver gene groups.ResultsWe develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (1) Constructing the gene network, (2) Discovering critical nodes of the constructed network, and (3) Identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify coding and non-coding driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup.Availability and implementationDriverGroup is available at https://github.com/pvvhoang/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication

2019 ◽  
Author(s):  
Jianing Xi ◽  
Xiguo Yuan ◽  
Minghui Wang ◽  
Ao Li ◽  
Xuelong Li ◽  
...  
Keyword(s):  
Ground Truth ◽  
Subspace Learning ◽  
Detection Methods ◽  
Supplementary Information ◽  
Driver Gene ◽  
Key Factors ◽  
Gene Detection ◽  
Driver Genes ◽  
Learning Framework ◽  
Mutation Data

AbstractMotivationDetecting driver genes from gene mutation data is a fundamental task for tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various subgroups, subgroup-specific driver genes are the key factors in the development of precision medicine for heterogeneous cancer. However, the existing driver gene detection methods are not designed to identify subgroup specificities of their detected driver genes, and therefore cannot indicate which group of patients is associated with the detected driver genes, which is difficult to provide specifically clinical guidance for individual patients.ResultsBy incorporating the subspace learning framework, we propose a novel bioinformatics method called DriverSub, which can efficiently predict subgroup-specific driver genes in the situation where the subgroup annotations are not available. When evaluated by simulation datasets with known ground truth and compared with existing methods, DriverSub yields the best prediction of driver genes and the inference of their related subgroups. When we apply DriverSub on the mutation data of real heterogeneous cancers, we can observe that the predicted results of DriverSub are highly enriched for experimentally validated known driver genes. Moreover, the subgroups inferred by DriverSub are significantly associated with the annotated molecular subgroups, indicating its capability of predicting subgroup-specific driver genes.Availability and implementationThe source code is publicly available at https://github.com/JianingXi/DriverSub.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Cancer mutational signatures representation by large-scale context embedding

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i309-i316 ◽  
Author(s):  
Yang Zhang ◽  
Yunxuan Xiao ◽  
Muyu Yang ◽  
Jian Ma
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
Somatic Mutations ◽  
Genome Structure ◽  
Superior Performance ◽  
Supplementary Information ◽  
Patient Specific ◽  
Cancer Subtypes ◽  
Cancer Heterogeneity ◽  
And Function

Abstract Motivation The accumulation of somatic mutations plays critical roles in cancer development and progression. However, the global patterns of somatic mutations, especially non-coding mutations, and their roles in defining molecular subtypes of cancer have not been well characterized due to the computational challenges in analysing the complex mutational patterns. Results Here, we develop a new algorithm, called MutSpace, to effectively extract patient-specific mutational features using an embedding framework for larger sequence context. Our method is motivated by the observation that the mutation rate at megabase scale and the local mutational patterns jointly contribute to distinguishing cancer subtypes, both of which can be simultaneously captured by MutSpace. Simulation evaluations show that MutSpace can effectively characterize mutational features from known patient subgroups and achieve superior performance compared with previous methods. As a proof-of-principle, we apply MutSpace to 560 breast cancer patient samples and demonstrate that our method achieves high accuracy in subtype identification. In addition, the learned embeddings from MutSpace reflect intrinsic patterns of breast cancer subtypes and other features of genome structure and function. MutSpace is a promising new framework to better understand cancer heterogeneity based on somatic mutations. Availability and implementation Source code of MutSpace can be accessed at: https://github.com/ma-compbio/MutSpace. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DriverGroup: a novel method for identifying driver gene groups

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i583-i591
Author(s):  
Vu V H Pham ◽  
Lin Liu ◽  
Cameron P Bracken ◽  
Gregory J Goodall ◽  
Jiuyong Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Single Gene ◽  
Gene Interaction ◽  
Computational Method ◽  
Supplementary Information ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Critical Nodes ◽  
Novel Method

Abstract Motivation Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. Results We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. Availability and implementation DriverGroup is available at https://github.com/pvvhoang/DriverGroup Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MutSpot: detection of non-coding mutation hotspots in cancer genomes

2019 ◽  
Author(s):  
Yu Amanda Guo ◽  
Mei Mei Chang ◽  
Anders Jacobsen Skanderup
Keyword(s):  
Somatic Mutations ◽  
R Package ◽  
Supplementary Information ◽  
Patient Specific ◽  
Supplementary Data ◽  
Link Type ◽  
Genome Wide ◽  
Cancer Genomes ◽  
User Friendly ◽  
Regulatory Dna

AbstractSummaryRecurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes.Availability and implementationMutSpot is implemented as an R package and is available at https://github.com/skandlab/MutSpot/Supplementary informationSupplementary data are available at https://github.com/skandlab/MutSpot/

scholarly journals Novel Mutations Detection with Next-Generation Sequencing and Its Association with Clinical Outcome in Unilateral Primary Aldosteronism

Biomedicines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1167
Author(s):  
Che-Hsiung Wu ◽  
Kang-Yung Peng ◽  
Daw-Yang Hwang ◽  
Yen-Hung Lin ◽  
Vin-Cent Wu ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Primary Aldosteronism ◽  
Sanger Sequencing ◽  
Somatic Mutations ◽  
Clinical Success ◽  
Clinical Success Rate ◽  
Driver Gene ◽  
Next Generation ◽  
Driver Genes ◽  
Generation Sequencing

Somatic mutations have been identified in adrenal tissues of unilateral primary aldosteronism (uPA). The spectrum of somatic mutations in uPAs was investigated using a customized and targeted next-generation sequencing (cNGS) approach. We also assessed whether cNGS or Sanger sequencing-identified mutations have an association with clinical outcomes in uPA. Adrenal tumoral tissues of uPA patients who underwent adrenalectomy were obtained. Conventional somatic mutation hotspots in 240 extracted DNA samples were initially screened using Sanger sequencing. A total of 75 Sanger-negative samples were further investigated by sequencing the entire coding regions of the known aldosterone-driver genes by our cNGS gene panel. Somatic mutations in aldosterone-driver genes were detected in 21 (28%) of these samples (8.8% of all samples), with 9 samples, including mutations in CACNA1D gene (12%), 5 in CACNA1H (6.6%), 3 in ATP2B3 (4%), 2 in CLCN2 (2.6%), 1 in ATP1A1 (1.3%), and 1 in CTNNB1 (1.3%). Via combined cNGS and Sanger sequencing aldosterone-driver gene mutations were detected in altogether 186 of our 240 (77.5%) uPA samples. The complete clinical success rate of patients containing cNGS-identified mutations was higher than those without mutations (odds ratio (OR) = 10.9; p = 0.012). Identification of somatic mutations with cNGS or Sanger sequencing may facilitate the prediction of complete clinical success after adrenalectomy in uPA patients.

scholarly journals Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Quang-Huy Nguyen ◽  
Duc-Hau Le
Keyword(s):  
Drug Targets ◽  
Expression Profiles ◽  
Prognostic Index ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Clinical Feature ◽  
Driver Gene ◽  
Omics Data ◽  
Analysis Pipeline ◽  
Driver Genes

AbstractThe cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.

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