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 SynQuant: an automatic tool to quantify synapses from microscopy images

2019 ◽  
Vol 36 (5) ◽  
pp. 1599-1606 ◽  
Author(s):  
Yizhi Wang ◽  
Congchao Wang ◽  
Petter Ranefall ◽  
Gerard Joey Broussard ◽  
Yinxue Wang ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Transmission ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Supplementary Information ◽  
Antibody Concentration ◽  
Imaging Data ◽  
Spot Detection ◽  
Synapse Detection

Abstract Motivation Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. Results We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. Availability and implementation Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. Supplementary information Supplementary data are available at Bioinformatics online.

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 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 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 The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic

2018 ◽  
Author(s):  
AM Frankell ◽  
S Jammula ◽  
X Li ◽  
G Contino ◽  
S Killcoyne ◽  
...  
Keyword(s):  
Detection Methods ◽  
Mutation Rates ◽  
Prognostic Indicators ◽  
Driver Gene ◽  
Cancer Type ◽  
Driver Genes ◽  
Gata Factors ◽  
Functional Relationships ◽  
Clinical Annotation ◽  
Highly Correlated

AbstractEsophageal Adenocarcinoma (EAC) is a poor prognosis cancer type with rapidly rising incidence. Our understanding of genetic events which drive EAC development is limited and there are few molecular biomarkers for prognostication or therapeutics. We have accumulated a cohort of 551 genomically characterised EACs (73% WGS and 27% WES) with clinical annotation and matched RNA-seq. Using a variety of driver gene detection methods, we discover 77 EAC driver genes (73% novel) and 21 non-coding driver elements (95% novel), and describe mutation and CNV types with specific functional impact. We identify a mean of 4.4 driver events per case derived from both copy number events and mutations. We compare driver mutation rates to the exome-wide mutational excess calculated using Non-synonymous vs Synonymous mutation rates (dNdS). We observe mutual exclusivity or co-occurrence of events within and between a number of EAC pathways (GATA factors, Core Cell cycle genes, TP53 regulators and the SWI/SNF complex) suggestive of important functional relationships. These driver variants correlate with tumour differentiation, sex and prognosis. Poor prognostic indicators (SMAD4, GATA4) are verified in independent cohorts with significant predictive value. Over 50% of EACs contain sensitising events for CDK4/6 inhibitors which are highly correlated with clinically relevant sensitivity in a panel EAC cell lines and organoids.

scholarly journals Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ali Reza Ebadi ◽  
Ali Soleimani ◽  
Abdulbaghi Ghaderzadeh
Keyword(s):  
Subspace Learning ◽  
Driver Gene ◽  
Controversial Issues ◽  
Learning Method ◽  
Driver Genes ◽  
Gene Group ◽  
Optimized Model ◽  
Cancer Studies ◽  
Degree Of Overlap ◽  
Better Than

AbstractExtracting the drivers from genes with mutation, and segregation of driver and passenger genes are known as the most controversial issues in cancer studies. According to the heterogeneity of cancer, it is not possible to identify indicators under a group of associated drivers, in order to identify a group of patients with diseases related to these subgroups. Therefore, the precise identification of the related driver genes using artificial intelligence techniques is still considered as a challenge for researchers. In this research, a new method has been developed using the subspace learning method, unsupervised learning, and with more constraints. Accordingly, it has been attempted to extract the driver genes with more precision and accurate results. The obtained results show that the proposed method is more to predict the driver genes and subgroups of driver genes which have the highest degree of overlap due to p-value with known driver genes in valid databases. Driver genes are the benchmark of MsigDB which have more overlap compared to them as selected driver genes. In this article, in addition to including the driver genes defined in previous work, introduce newer driver genes. The minister will define newer groups of driver genes compared to other methods the p-value of the proposed method was 9.21e-7 better than previous methods for 200 genes. Due to the overlap and newer driver genes and driver gene group and subgroups. The results show that the p value of the proposed method is about 2.7 times less than the driver sub method due to overlap, indicating that the proposed method can identify driver genes in cancerous tumors with greater accuracy and reliability.

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals ZRSR2 overexpression is a frequent and early event in castration-resistant prostate cancer development

2021 ◽  
Author(s):  
Haiqing He ◽  
Jun Hao ◽  
Xin Dong ◽  
Yu Wang ◽  
Hui Xue ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Cycle Progression ◽  
Locally Advanced ◽  
Patient Treatment ◽  
Early Event ◽  
Driver Gene ◽  
Castration Resistant Prostate Cancer ◽  
Driver Genes ◽  
Castration Resistant ◽  
Pdx Models

Abstract Background Androgen deprivation therapy (ADT) remains the leading systemic therapy for locally advanced and metastatic prostate cancers (PCa). While a majority of PCa patients initially respond to ADT, the durability of response is variable and most patients will eventually develop incurable castration-resistant prostate cancer (CRPC). Our research objective is to identify potential early driver genes responsible for CRPC development. Methods We have developed a unique panel of hormone-naïve PCa (HNPC) patient-derived xenograft (PDX) models at the Living Tumor Laboratory. The PDXs provide a unique platform for driver gene discovery as they allow for the analysis of differentially expressed genes via transcriptomic profiling at various time points after mouse host castration. In the present study, we focused on genes with expression changes shortly after castration but before CRPC has fully developed. These are likely to be potential early drivers of CRPC development. Such genes were further validated for their clinical relevance using data from PCa patient databases. ZRSR2 was identified as a top gene candidate and selected for further functional studies. Results ZRSR2 is significantly upregulated in our PDX models during the early phases of CRPC development after mouse host castration and remains consistently high in fully developed CRPC PDX models. Moreover, high ZRSR2 expression is also observed in clinical CRPC samples. Importantly, elevated ZRSR2 in PCa samples is correlated with poor patient treatment outcomes. ZRSR2 knockdown reduced PCa cell proliferation and delayed cell cycle progression at least partially through inhibition of the Cyclin D1 (CCND1) pathway. Conclusion Using our unique HNPC PDX models that develop into CRPC after host castration, we identified ZRSR2 as a potential early driver of CRPC development.

scholarly journals Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiang Li ◽  
Jianzheng Liu ◽  
Jessica Baron ◽  
Khoa Luu ◽  
Eric Patterson
Keyword(s):  
Focal Length ◽  
Ground Truth ◽  
Detection Methods ◽  
Viewing Angle ◽  
Deep Convolutional Neural Networks ◽  
Landmark Detection ◽  
High Performing ◽  
Detection Techniques ◽  
And Performance ◽  
Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

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.

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