1
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Integrating Protein-Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer. Interdiscip Sci 2021; 14:151-167. [PMID: 34491536 DOI: 10.1007/s12539-021-00475-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/20/2021] [Accepted: 08/22/2021] [Indexed: 10/20/2022]
Abstract
With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data, such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), it gains increasing importance to detect the functional high-frequency mutation gene set in cells that causes cancer in the field of medicine. In this study, we propose a new recognition method of driver modules, named ECSWalk to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules detection, based on human protein-protein interaction networks and pan-cancer somatic mutation data. This study first utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Second, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are split using induced subgraph method, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules. This method is applied to TCGA pan-cancer data and the experimental results show that ECSWalk can detect driver modules more effectively and accurately, and can identify new candidate gene sets with higher biological relevance and statistical significance than MEXCOWalk and HotNet2. Thus, ECSWalk is of theoretical implication and practical value for cancer diagnosis, treatment and drug targets.
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2
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Klein MI, Cannataro VL, Townsend JP, Newman S, Stern DF, Zhao H. Identifying modules of cooperating cancer drivers. Mol Syst Biol 2021; 17:e9810. [PMID: 33769711 PMCID: PMC7995435 DOI: 10.15252/msb.20209810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 01/20/2021] [Accepted: 01/26/2021] [Indexed: 12/22/2022] Open
Abstract
Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co-occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.
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Affiliation(s)
- Michael I Klein
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Bioinformatics R&DSema4StamfordCTUSA
| | - Vincent L Cannataro
- Department of BiologyEmmanuel CollegeBostonMAUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
| | - Jeffrey P Townsend
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Yale Cancer CenterYale UniversityNew HavenCTUSA
| | | | - David F Stern
- Yale Cancer CenterYale UniversityNew HavenCTUSA
- Department of PathologyYale School of MedicineNew HavenCTUSA
| | - Hongyu Zhao
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Yale Cancer CenterYale UniversityNew HavenCTUSA
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3
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Baali I, Erten C, Kazan H. DriveWays: a method for identifying possibly overlapping driver pathways in cancer. Sci Rep 2020; 10:21971. [PMID: 33319839 PMCID: PMC7738685 DOI: 10.1038/s41598-020-78852-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 11/19/2020] [Indexed: 11/22/2022] Open
Abstract
The majority of the previous methods for identifying cancer driver modules output nonoverlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution. We provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes. We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWay’s output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways.
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Affiliation(s)
- Ilyes Baali
- Electrical and Computer Engineering Graduate Program, Antalya Bilim University, 07190, Antalya, Turkey
| | - Cesim Erten
- Department of Computer Engineering, Antalya Bilim University, 07190, Antalya, Turkey.
| | - Hilal Kazan
- Department of Computer Engineering, Antalya Bilim University, 07190, Antalya, Turkey.
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4
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Ahmed R, Baali I, Erten C, Hoxha E, Kazan H. MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules. Bioinformatics 2020; 36:872-879. [PMID: 31432076 DOI: 10.1093/bioinformatics/btz655] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 07/03/2019] [Accepted: 08/18/2019] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules. RESULTS We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rafsan Ahmed
- Electrical and Computer Engineering Graduate Program, Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey
| | - Ilyes Baali
- Electrical and Computer Engineering Graduate Program, Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey
| | - Cesim Erten
- Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey
| | - Evis Hoxha
- Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya Bilim University, Antalya 07190, Turkey
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5
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Mateo L, Duran-Frigola M, Gris-Oliver A, Palafox M, Scaltriti M, Razavi P, Chandarlapaty S, Arribas J, Bellet M, Serra V, Aloy P. Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns. Genome Med 2020; 12:78. [PMID: 32907621 PMCID: PMC7488324 DOI: 10.1186/s13073-020-00774-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/11/2020] [Indexed: 12/19/2022] Open
Abstract
Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.
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Affiliation(s)
- Lidia Mateo
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Albert Gris-Oliver
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain
| | - Marta Palafox
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain
| | - Maurizio Scaltriti
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Department of Pathology, MSKCC, New York, NY, 10065, USA
| | - Pedram Razavi
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Breast Medicine Service, Department of Medicine, MSKCC and Weill-Cornell Medical College, New York, NY, 10065, USA
| | - Sarat Chandarlapaty
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Breast Medicine Service, Department of Medicine, MSKCC and Weill-Cornell Medical College, New York, NY, 10065, USA
| | - Joaquin Arribas
- Growth Factors Laboratory, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.,CIBERONC, Barcelona, Spain
| | - Meritxell Bellet
- Breast Cancer Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,Department of Medical Oncology, Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Violeta Serra
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,CIBERONC, Barcelona, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain. .,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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6
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Wei PJ, Wu FX, Xia J, Su Y, Wang J, Zheng CH. Prioritizing Cancer Genes Based on an Improved Random Walk Method. Front Genet 2020; 11:377. [PMID: 32411180 PMCID: PMC7198854 DOI: 10.3389/fgene.2020.00377] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 03/26/2020] [Indexed: 12/18/2022] Open
Abstract
Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.
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Affiliation(s)
- 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
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Computer Sciences, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Yansen Su
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Jing Wang
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
- College of Computer and Information Engineering, Fuyang Normal University, Fuyang, 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
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7
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Zhang W, Zeng Y, Wang L, Liu Y, Cheng YN. An Effective Graph Clustering Method to Identify Cancer Driver Modules. Front Bioeng Biotechnol 2020; 8:271. [PMID: 32318558 PMCID: PMC7154174 DOI: 10.3389/fbioe.2020.00271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 03/16/2020] [Indexed: 12/15/2022] Open
Abstract
Identifying the molecular modules that drive cancer progression can greatly deepen the understanding of cancer mechanisms and provide useful information for targeted therapies. Most methods currently addressing this issue primarily use mutual exclusivity without making full use of the extra layer of module property. In this paper, we propose MCLCluster to identity cancer driver modules, which use somatic mutation data, Cancer Cell Fraction (CCF) data, gene functional interaction network and protein-protein interaction (PPI) network to derive the module property on mutual exclusivity, connectivity in PPI network and functionally similarity of genes. We have taken three effective measures to ensure the effectiveness of our algorithm. First, we use CCF data to choose stronger signals and more confident mutations. Second, the weighted gene functional interaction network is used to quantify the gene functional similarity in PPI. The third, graph clustering method based on Markov is exploited to extract the candidate module. MCLCluster is tested in the two TCGA datasets (GBM and BRCA), and identifies several well-known oncogenes driver modules and some modules with functionally associated driver genes. Besides, we compare it with Multi-Dendrix, FSME Cluster and RME in simulated dataset with background noise and passenger rate, MCLCluster outperforming all of these methods.
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Affiliation(s)
- Wei Zhang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, China
| | - Yifu Zeng
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, China
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China
| | - Yue Liu
- College of Computer Science and Electronics Engineering, Hunan University, Changsha, China
| | - Yi-Nan Cheng
- College of Science, Southern University of Science and Technology, Shenzhen, China
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8
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Zhang W, Wang SL. An Integrated Framework for Identifying Mutated Driver Pathway and Cancer Progression. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:455-464. [PMID: 29990286 DOI: 10.1109/tcbb.2017.2788016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Next-generation sequencing (NGS) technologies provide amount of somatic mutation data in a large number of patients. The identification of mutated driver pathway and cancer progression from these data is a challenging task because of the heterogeneity of interpatient. In addition, cancer progression at the pathway level has been proved to be more reasonable than at the gene level. In this paper, we introduce an integrated framework to identify mutated driver pathways and cancer progression (iMDPCP) at the pathway level from somatic mutation data. First, we use uncertainty coefficient to quantify mutual exclusivity on gene driver pathways and develop a computational framework to identify mutated driver pathways based on the adaptive discrete differential evolution algorithm. Then, we construct cancer progression model for driver pathways based on the Bayesian Network. Finally, we evaluate the performance of iMDPCP on real cancer somatic mutation datasets. The experimental results indicate that iMDPCP is more accurate than state-of-the-art methods according to the enrichment of KEGG pathways, and it also provides new insights on identifying cancer progression at the pathway level.
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9
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Personalized prediction of genes with tumor-causing somatic mutations based on multi-modal deep Boltzmann machine. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.02.096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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10
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Wu H, Dong J, Wei J. Network‐based method for detecting dysregulated pathways in glioblastoma cancer. IET Syst Biol 2018; 12:39-44. [PMID: 29337288 PMCID: PMC8687240 DOI: 10.1049/iet-syb.2017.0033] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The knowledge on the biological molecular mechanisms underlying cancer is important for the precise diagnosis and treatment of cancer patients. Detecting dysregulated pathways in cancer can provide insights into the mechanism of cancer and help to detect novel drug targets. Based on the wide existing mutual exclusivity among mutated genes and the interrelationship between gene mutations and expression changes, this study presents a network‐based method to detect the dysregulated pathways from gene mutations and expression data of the glioblastoma cancer. First, the authors construct a gene network based on mutual exclusivity between each pair of genes and the interaction between gene mutations and expression changes. Then they detect all complete subgraphs using CFinder clustering algorithm in the constructed gene network. Next, the two gene sets whose overlapping scores are above a specific threshold are merged. Finally, they obtain two dysregulated pathways in which there are glioblastoma‐related multiple genes which are closely related to the two subtypes of glioblastoma. The results show that one dysregulated pathway revolving around epidermal growth factor receptor is likely to be associated with the primary subtype of glioblastoma, and the other dysregulated pathway revolving around TP53 is likely to be associated with the secondary subtype of glioblastoma.
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Affiliation(s)
- Hao Wu
- College of Information EngineeringNorthwest A&F UniversityYangling712100ShaanxiPeople's Republic of China
| | - Jihua Dong
- Department of Foreign LanguagesNorthwest A&F UniversityYangling712100ShaanxiPeople's Republic of China
| | - Jicheng Wei
- College of Information EngineeringNorthwest A&F UniversityYangling712100ShaanxiPeople's Republic of China
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11
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Ujiie H, Kato T, Lee D, Hu HP, Fujino K, Kaji M, Kaga K, Matsui Y, Yasufuku K. Overexpression of MAGEA2 has a prognostic significance and is a potential therapeutic target for patients with lung cancer. Int J Oncol 2017; 50:2154-2170. [PMID: 28498455 DOI: 10.3892/ijo.2017.3984] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 03/16/2017] [Indexed: 11/06/2022] Open
Abstract
Melanoma-associated antigens (MAGE) are expressed in different type of cancers including lung cancer and have been shown to be functionally related to p53 tumor suppressor gene. Little is known about the relationship between MAGE genes and p53 aberrant expression in lung cancer. The aims of this study were to observe the expression of MAGEA2, examine the role of MAGEA2 in lung cancer survival, investigate its correlation between MAGEA2 and p53, and explore its clinicopathologic significance as a prognostic marker. Quantitative reverse transcription-polymerase chain reaction was performed to detect the expression of MAGEA2 using 36 primary tumors and 31 metastatic lymph nodes from patients with lung cancer. The role of MAGEA2 in cancer cell growth and in the regulation of p53 downstream genes were examined using small interfering RNA. The expression of MAGEA2 and p53 were analyzed immunohistochemically using tissue microarray from 353 resected lung specimens. High-level expression of MAGEA2 (High-MAGEA2) was confirmed in lung tumors with high frequency. Inhibiting MAGEA2 expression effectively suppressed cancer cell growth and decreased the expression of p53 downstream target genes in vitro. In adenocarcinoma, High-MAGEA2 was strongly associated with aberrant p53 expression (P<0.001) and was associated with worse clinical outcomes (5-year OS, 87.1% in low vs. 74.1% in high, P=0.014). Aberrant p53 expression was also significant worse prognostic factor (P=0.029). Among the adenocarcinoma patients with wild-type p53, High-MAGEA2 had poorer prognosis than low-level MAGEA2 groups (5-year OS, 90.1% vs. 72.1%, P=0.037), whereas had no difference in p53 aberrant tumors. On multivariate analysis, MAGEA2 was independently associated with survival (hazard ratio; 2.12, P=0.030). In conclusion, suppression of MAGEA2 in lung cancer cells significantly reduced the growth/survival of cancer cells. High-MAGEA2 was identified as an independent prognostic factor in lung adenocarcinoma. Specific inhibition of MAGEA2 may be a promising therapeutic strategy for patients with lung cancer.
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Affiliation(s)
- Hideki Ujiie
- Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, University of Toronto, Ontario, Canada
| | - Tatsuya Kato
- Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, University of Toronto, Ontario, Canada
| | - Daiyoon Lee
- Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, University of Toronto, Ontario, Canada
| | - Hsin-Pei Hu
- Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, University of Toronto, Ontario, Canada
| | - Kosuke Fujino
- Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, University of Toronto, Ontario, Canada
| | - Mitsuhito Kaji
- Department of Thoracic Surgery, Sapporo Minami-sanjo Hospital, Sapporo, Hokkaido, Japan
| | - Kichizo Kaga
- Department of Cardiovascular and Thoracic Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
| | - Yoshiro Matsui
- Department of Cardiovascular and Thoracic Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
| | - Kazuhiro Yasufuku
- Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, University of Toronto, Ontario, Canada
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12
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Wu H, Gao L, Kasabov NK. Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:1036-1044. [PMID: 26915128 DOI: 10.1109/tcbb.2016.2520934] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Large-scale cancer genomics projects are providing a wealth of somatic mutation data from a large number of cancer patients. However, it is difficult to obtain several samples with a temporal order from one patient in evaluating the cancer progression. Therefore, one of the most challenging problems arising from the data is to infer the temporal order of mutations across many patients. To solve the problem efficiently, we present a Network-based method (NetInf) to Infer cancer progression at the pathway level from cross-sectional data across many patients, leveraging on the exclusive property of driver mutations within a pathway and the property of linear progression between pathways. To assess the robustness of NetInf, we apply it on simulated data with the addition of different levels of noise. To verify the performance of NetInf, we apply it to analyze somatic mutation data from three real cancer studies with large number of samples. Experimental results reveal that the pathways detected by NetInf show significant enrichment. Our method reduces computational complexity by constructing gene networks without assigning the number of pathways, which also provides new insights on the temporal order of somatic mutations at the pathway level rather than at the gene level.
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13
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Niroula A, Vihinen M. Variation Interpretation Predictors: Principles, Types, Performance, and Choice. Hum Mutat 2016; 37:579-97. [DOI: 10.1002/humu.22987] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 03/07/2016] [Indexed: 12/18/2022]
Affiliation(s)
- Abhishek Niroula
- Department of Experimental Medical Science; Lund University; BMC B13 Lund SE-22184 Sweden
| | - Mauno Vihinen
- Department of Experimental Medical Science; Lund University; BMC B13 Lund SE-22184 Sweden
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14
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Identification of Gene Expression Pattern Related to Breast Cancer Survival Using Integrated TCGA Datasets and Genomic Tools. BIOMED RESEARCH INTERNATIONAL 2015; 2015:878546. [PMID: 26576432 PMCID: PMC4630377 DOI: 10.1155/2015/878546] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Revised: 09/14/2015] [Accepted: 09/28/2015] [Indexed: 11/18/2022]
Abstract
Several large-scale human cancer genomics projects such as TCGA offered huge genomic and clinical data for researchers to obtain meaningful genomics alterations which intervene in the development and metastasis of the tumor. A web-based TCGA data analysis platform called TCGA4U was developed in this study. TCGA4U provides a visualization solution for this study to illustrate the relationship of these genomics alternations with clinical data. A whole genome screening of the survival related gene expression patterns in breast cancer was studied. The gene list that impacts the breast cancer patient survival was divided into two patterns. Gene list of each of these patterns was separately analyzed on DAVID. The result showed that mitochondrial ribosomes play a more crucial role in the cancer development. We also reported that breast cancer patients with low HSPA2 expression level had shorter overall survival time. This is widely different to findings of HSPA2 expression pattern in other cancer types. TCGA4U provided a new perspective for the TCGA datasets. We believe it can inspire more biomedical researchers to study and explain the genomic alterations in cancer development and discover more targeted therapies to help more cancer patients.
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