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Zhang T, Zhang SW, Xie MY, Li Y. Identifying cooperating cancer driver genes in individual patients through hypergraph random walk. J Biomed Inform 2024; 157:104710. [PMID: 39159864 DOI: 10.1016/j.jbi.2024.104710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024]
Abstract
OBJECTIVE Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Ming-Yu Xie
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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2
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Arici MK, Tuncbag N. Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction. Brief Bioinform 2024; 25:bbae399. [PMID: 39163205 PMCID: PMC11334722 DOI: 10.1093/bib/bbae399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 06/26/2024] [Accepted: 08/07/2024] [Indexed: 08/22/2024] Open
Abstract
Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.
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Affiliation(s)
- Muslum Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara 06800, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul 34450, Turkey
- School of Medicine, Koc University, Istanbul 34450, Turkey
- Koc University Research Center for Translational Medicine (KUTTAM), Koc University, Istanbul 34450, Turkey
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3
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Zhang N, Ma F, Guo D, Pang Y, Wang C, Zhang Y, Zheng X, Wang M. A novel hypergraph model for identifying and prioritizing personalized drivers in cancer. PLoS Comput Biol 2024; 20:e1012068. [PMID: 38683860 PMCID: PMC11081510 DOI: 10.1371/journal.pcbi.1012068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 05/09/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024] Open
Abstract
Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.
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Affiliation(s)
- Naiqian Zhang
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Fubin Ma
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Dong Guo
- School of Mathematics and Statistics, Shandong University, Weihai, China
- Department of Central Lab, Weihai Municipal Hospital, Shandong University, Weihai, China
| | - Yuxuan Pang
- SDU-ANU Joint Science College, Shandong University, Weihai, China
| | - Chenye Wang
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Xiaoqi Zheng
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mingyi Wang
- School of Mathematics and Statistics, Shandong University, Weihai, China
- Department of Central Lab, Weihai Municipal Hospital, Shandong University, Weihai, China
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Huang Y, Chen F, Sun H, Zhong C. Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation. BMC Bioinformatics 2024; 25:34. [PMID: 38254011 PMCID: PMC10804660 DOI: 10.1186/s12859-024-05662-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/18/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. RESULTS Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. CONCLUSIONS LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .
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Affiliation(s)
- Yiran Huang
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
- Key Laboratory of Parallel, Distributed and Intelligent Computing in Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, 530004, China
| | - Fuhao Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
| | - Hongtao Sun
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
| | - Cheng Zhong
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China.
- Key Laboratory of Parallel, Distributed and Intelligent Computing in Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China.
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, 530004, China.
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5
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Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
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Xu J, Pang B, Lan Y, Dou R, Wang S, Kang S, Zhang W, Liu Y, Zhang Y, Ping Y. Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals. Mol Oncol 2023; 17:2472-2490. [PMID: 37491836 PMCID: PMC10620122 DOI: 10.1002/1878-0261.13499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/21/2023] [Accepted: 07/24/2023] [Indexed: 07/27/2023] Open
Abstract
High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.
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Affiliation(s)
- Jinyuan Xu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Bo Pang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yujia Lan
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Renjie Dou
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Shuai Wang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Shaobo Kang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Wanmei Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yuanyuan Liu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yijing Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yanyan Ping
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
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7
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Gillman R, Field MA, Schmitz U, Karamatic R, Hebbard L. Identifying cancer driver genes in individual tumours. Comput Struct Biotechnol J 2023; 21:5028-5038. [PMID: 37867967 PMCID: PMC10589724 DOI: 10.1016/j.csbj.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/24/2023] Open
Abstract
Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.
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Affiliation(s)
- Rhys Gillman
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
| | - Matt A. Field
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
- Immunogenomics Lab, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia
| | - Ulf Schmitz
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
| | - Rozemary Karamatic
- Gastroenterology and Hepatology, Townsville University Hospital, PO Box 670, Townsville, Queensland 4810, Australia
- College of Medicine and Dentistry, Division of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia
| | - Lionel Hebbard
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
- Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Sydney, New South Wales, Australia
- Australian Institute for Tropical Health and Medicine, Townsville, Queensland, Australia
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8
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Peng W, Yu P, Dai W, Fu X, Liu L, Pan Y. A Graph Convolution Network-Based Model for Prioritizing Personalized Cancer Driver Genes of Individual Patients. IEEE Trans Nanobioscience 2023; 22:744-754. [PMID: 37195839 DOI: 10.1109/tnb.2023.3277316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Cancer driver genes are mutated genes that play a key role in the growth of cancer cells. Accurately identifying the cancer driver genes helps us understand cancer's pathogenesis and develop effective treatment strategies. However, cancers are highly heterogeneous diseases; patients with the same cancer type may have different genomic characteristics and clinical symptoms. Hence, it is urgent to devise effective methods to identify personalized cancer driver genes of individual patients to help determine whether a patient can be treated with a certain targeted drug. This work presents a method for predicting personalized cancer Driver genes of individual patients based on Graph Convolution Networks and Neighbor Interactions called NIGCNDriver. NIGCNDriver first constructs a gene-sample association matrix using the associations between a sample and its known driver genes. Then, it employs graph convolution models on the gene-sample network to aggregate neighbor node features, and themself features, and then combines with the element-wise level interactions between neighbors to learn new feature representations for the samples and gene nodes. Finally, a linear correlation coefficient decoder is used to reconstruct the association between the sample and the mutant gene, enabling the prediction of a personalized driver gene for the individual sample. We applied the NIGCNDriver method to predict cancer driver genes for individual samples in the TCGA and cancer cell line datasets. The results show that our method outperforms the baseline methods in cancer driver gene prediction for individual samples.
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Zhu X, Zhao W, Zhou Z, Gu X. Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. J Mol Evol 2023:10.1007/s00239-023-10117-0. [PMID: 37246992 DOI: 10.1007/s00239-023-10117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.
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Affiliation(s)
- Xunuo Zhu
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wenyi Zhao
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhan Zhou
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
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Chan AP, Choi Y, Rangan A, Zhang G, Podder A, Berens M, Sharma S, Pirrotte P, Byron S, Duggan D, Schork NJ. Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges. Methods Mol Biol 2023; 2590:1-30. [PMID: 36335489 DOI: 10.1007/978-1-0716-2819-5_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Human DNA sequencing protocols have revolutionized human biology, biomedical science, and clinical practice, but still have very important limitations. One limitation is that most protocols do not separate or assemble (i.e., "phase") the nucleotide content of each of the maternally and paternally derived chromosomal homologs making up the 22 autosomal pairs and the chromosomal pair making up the pseudo-autosomal region of the sex chromosomes. This has led to a dearth of studies and a consequent underappreciation of many phenomena of fundamental importance to basic and clinical genomic science. We discuss a few protocols for obtaining phase information as well as their limitations, including those that could be used in tumor phasing settings. We then describe a number of biological and clinical phenomena that require phase information. These include phenomena that require precise knowledge of the nucleotide sequence in a chromosomal segment from germline or somatic cells, such as DNA binding events, and insight into unique cis vs. trans-acting functionally impactful variant combinations-for example, variants implicated in a phenotype governed by compound heterozygosity. In addition, we also comment on the need for reliable and consensus-based diploid-context computational workflows for variant identification as well as the need for laboratory-based functional verification strategies for validating cis vs. trans effects of variant combinations. We also briefly describe available resources, example studies, as well as areas of further research, and ultimately argue that the science behind the study of human diploidy, referred to as "diplomics," which will be enabled by nucleotide-level resolution of phased genomes, is a logical next step in the analysis of human genome biology.
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Affiliation(s)
- Agnes P Chan
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Yongwook Choi
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Aditya Rangan
- Courant Institute of Mathematical Sciences at New York University, New York, NY, USA
| | - Guangfa Zhang
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Avijit Podder
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Michael Berens
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Sunil Sharma
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Patrick Pirrotte
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Sara Byron
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Dave Duggan
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Nicholas J Schork
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA.
- The City of Hope National Medical Center, Duarte, CA, USA.
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Zhang SW, Wang ZN, Li Y, Guo WF. Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network. BMC Bioinformatics 2022; 23:341. [PMID: 35974311 PMCID: PMC9380343 DOI: 10.1186/s12859-022-04802-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 06/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network. These methods ignore the personalized edge weight information in gene interaction network, leading to false positive results. Results In this work, we presented a novel algorithm (called PDGPCS) to predict the Personalized cancer Driver Genes based on the Prize-Collecting Steiner tree model by considering the personalized edge weight information. PDGPCS first constructs the personalized weighted gene interaction network by integrating the personalized gene expression data and prior known gene/protein interaction network knowledge. Then the gene mutation data and pathway data are integrated to quantify the impact of each mutant gene on every dysregulated pathway with the prize-collecting Steiner tree model. Finally, according to the mutant gene’s aggregated impact score on all dysregulated pathways, the mutant genes are ranked for prioritizing the personalized cancer driver genes. Experimental results on four TCGA cancer datasets show that PDGPCS has better performance than other personalized driver gene prediction methods. In addition, we verified that the personalized edge weight of gene interaction network can improve the prediction performance. Conclusions PDGPCS can more accurately identify the personalized driver genes and takes a step further toward personalized medicine and treatment. The source code of PDGPCS can be freely downloaded from https://github.com/NWPU-903PR/PDGPCS. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04802-y.
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Affiliation(s)
- Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Zhen-Nan Wang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China.
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12
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Yan J, Hu Z, Li ZW, Sun S, Guo WF. Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer. Front Oncol 2022; 12:891676. [PMID: 35712516 PMCID: PMC9195174 DOI: 10.3389/fonc.2022.891676] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022] Open
Abstract
Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.
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Affiliation(s)
- Jipeng Yan
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Zhuo Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Zong-Wei Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiren Sun
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
| | - Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
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13
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Sudhakar M, Rengaswamy R, Raman K. Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes. Front Genet 2022; 13:854190. [PMID: 35620468 PMCID: PMC9127508 DOI: 10.3389/fgene.2022.854190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/04/2022] [Indexed: 12/12/2022] Open
Abstract
The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes.
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Affiliation(s)
- Malvika Sudhakar
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, India.,Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India
| | - Raghunathan Rengaswamy
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, India.,Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.,Department of Chemical Engineering, IIT Madras, Chennai, India
| | - Karthik Raman
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, India.,Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India
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14
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Sudhakar M, Rengaswamy R, Raman K. Novel ratio-metric features enable the identification of new driver genes across cancer types. Sci Rep 2022; 12:5. [PMID: 34997044 PMCID: PMC8741763 DOI: 10.1038/s41598-021-04015-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 12/13/2021] [Indexed: 12/27/2022] Open
Abstract
An emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG .
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Affiliation(s)
- Malvika Sudhakar
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology Madras, Chennai, India
| | - Raghunathan Rengaswamy
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology Madras, Chennai, India.
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology Madras, Chennai, India.
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.
| | - Karthik Raman
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology Madras, Chennai, India.
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology Madras, Chennai, India.
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15
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Ahmed R, Erten C, Houdjedj A, Kazan H, Yalcin C. A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers. Front Genet 2021; 12:746495. [PMID: 34899838 PMCID: PMC8664367 DOI: 10.3389/fgene.2021.746495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/27/2021] [Indexed: 12/03/2022] Open
Abstract
One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.
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Affiliation(s)
- Rafsan Ahmed
- Electrical and Computer Engineering Graduate Program, Antalya Bilim University, Antalya, Turkey
| | - Cesim Erten
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Aissa Houdjedj
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Cansu Yalcin
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
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16
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Tang YY, Wei PJ, Zhao JP, Xia J, Cao RF, Zheng CH. Identification of driver genes based on gene mutational effects and network centrality. BMC Bioinformatics 2021; 22:457. [PMID: 34560840 PMCID: PMC8461858 DOI: 10.1186/s12859-021-04377-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 08/23/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. RESULTS To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein-protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. CONCLUSIONS Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.
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Affiliation(s)
- Yun-Yun Tang
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Pi-Jing Wei
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Jian-Ping Zhao
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
| | - Junfeng Xia
- Institute of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Rui-Fen Cao
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China.,Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China. .,College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.
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17
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Ülgen E, Sezerman OU. driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics 2021; 22:263. [PMID: 34030627 PMCID: PMC8142487 DOI: 10.1186/s12859-021-04203-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 05/17/2021] [Indexed: 12/15/2022] Open
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. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04203-7.
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Affiliation(s)
- Ege Ülgen
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.
| | - O Uğur Sezerman
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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18
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Zhang D, Bin Y. DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis. Front Genet 2021; 11:607798. [PMID: 33679866 PMCID: PMC7933651 DOI: 10.3389/fgene.2020.607798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/30/2020] [Indexed: 01/07/2023] Open
Abstract
Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the mutation and gene expression information based on subnetwork enrichment analysis. Compared with the existing classic methods, DriverSubNet can rank driver genes and filter out passenger genes more efficiently in terms of precision, recall, and F1 score, as indicated by the analysis of four cancer datasets. The method recovered about 50% more known cancer driver genes in the top 100 detected genes than those found in other algorithms. Intriguingly, DriverSubNet was able to find these unknown cancer driver genes which could act as potential therapeutic targets and useful prognostic biomarkers for cancer patients. Therefore, DriverSubNet may act as a useful tool for the identification of driver genes by subnetwork enrichment analysis.
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Affiliation(s)
- Di Zhang
- College of Information Engineering, Shaoguan University, Shaoguan, China
| | - Yannan Bin
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
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19
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Malagoli Tagliazucchi G, Taccioli C. GMIEC: a shiny application for the identification of gene-targeted drugs for precision medicine. BMC Genomics 2020; 21:619. [PMID: 32912170 PMCID: PMC7488130 DOI: 10.1186/s12864-020-06996-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/17/2020] [Indexed: 11/28/2022] Open
Abstract
Abstract Background Precision medicine is a medical approach that takes into account individual genetic variability and often requires Next Generation Sequencing data in order to predict new treatments. Here we present GMIEC, Genomic Modules Identification et Characterization for genomics medicine, an application that is able to identify specific drugs at the level of single patient integrating multi-omics data such as RNA-sequencing, copy-number variation, methylation, Chromatin Immuno-Precipitation and Exome/Whole Genome sequencing. It is also possible to include clinical data related to each patient. GMIEC has been developed as a web-based R-Shiny platform and gives as output a table easy to use and explore. Results We present GMIEC, a Shiny application for genomics medicine. The tool allows the users the integration of two or more multiple omics datasets (e.g. gene-expression, copy-number), at sample level, to identify groups of genes that share common genomic and corresponding drugs. We demonstrate the characteristics of our application by using it to analyze a prostate cancer data set. Conclusions GMIEC provides a simple interface for genomics medicine. GMIEC was develop with Shiny to provide an application that does not require advanced programming skills. GMIEC consists of three sub-application for the analysis (GMIEC-AN), the visualization (GMIEC-VIS) and the exploration of results (GMIEC-RES). GMIEC is an open source software and is available at https://github.com/guidmt/GMIEC-shiny
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Affiliation(s)
- Guidantonio Malagoli Tagliazucchi
- Division of Cardiology, Azienda Ospedaliero-Universitaria di Parma, 43126, Parma, Italy.,Present address: Department of Genetics, Evolution and Environment, Darwin Building, Gower Street WC1E 6BT, London, UK
| | - Cristian Taccioli
- Department of Animal Medicine, Production and Health, University of Padova, 35020, Legnaro, PD, Italy.
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