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Foggetti G, Li C, Cai H, Hellyer JA, Lin WY, Ayeni D, Hastings K, Choi J, Wurtz A, Andrejka L, Maghini DG, Rashleigh N, Levy S, Homer R, Gettinger SN, Diehn M, Wakelee HA, Petrov DA, Winslow MM, Politi K. Genetic Determinants of EGFR-Driven Lung Cancer Growth and Therapeutic Response In Vivo. Cancer Discov 2021; 11:1736-1753. [PMID: 33707235 PMCID: PMC8530463 DOI: 10.1158/2159-8290.cd-20-1385] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/23/2020] [Accepted: 02/11/2021] [Indexed: 11/16/2022]
Abstract
In lung adenocarcinoma, oncogenic EGFR mutations co-occur with many tumor suppressor gene alterations; however, the extent to which these contribute to tumor growth and response to therapy in vivo remains largely unknown. By quantifying the effects of inactivating 10 putative tumor suppressor genes in a mouse model of EGFR-driven Trp53-deficient lung adenocarcinoma, we found that Apc, Rb1, or Rbm10 inactivation strongly promoted tumor growth. Unexpectedly, inactivation of Lkb1 or Setd2-the strongest drivers of growth in a KRAS-driven model-reduced EGFR-driven tumor growth. These results are consistent with mutational frequencies in human EGFR- and KRAS-driven lung adenocarcinomas. Furthermore, KEAP1 inactivation reduced the sensitivity of EGFR-driven tumors to the EGFR inhibitor osimertinib, and mutations in genes in the KEAP1 pathway were associated with decreased time on tyrosine kinase inhibitor treatment in patients. Our study highlights how the impact of genetic alterations differs across oncogenic contexts and that the fitness landscape shifts upon treatment. SIGNIFICANCE: By modeling complex genotypes in vivo, this study reveals key tumor suppressors that constrain the growth of EGFR-mutant tumors. Furthermore, we uncovered that KEAP1 inactivation reduces the sensitivity of these tumors to tyrosine kinase inhibitors. Thus, our approach identifies genotypes of biological and therapeutic importance in this disease.This article is highlighted in the In This Issue feature, p. 1601.
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Affiliation(s)
- Giorgia Foggetti
- Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut
| | - Chuan Li
- Department of Biology, Stanford University, Stanford, California
| | - Hongchen Cai
- Department of Genetics, Stanford University School of Medicine, Stanford, California
| | - Jessica A Hellyer
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Wen-Yang Lin
- Department of Genetics, Stanford University School of Medicine, Stanford, California
| | - Deborah Ayeni
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut
| | | | - Jungmin Choi
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Anna Wurtz
- Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut
| | - Laura Andrejka
- Department of Genetics, Stanford University School of Medicine, Stanford, California
| | - Dylan G Maghini
- Department of Genetics, Stanford University School of Medicine, Stanford, California
| | | | - Stellar Levy
- Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut
| | - Robert Homer
- Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut
- VA Connecticut Healthcare System, Pathology and Laboratory Medicine Service, West Haven, Connecticut
| | - Scott N Gettinger
- Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut
- Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Maximilian Diehn
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Heather A Wakelee
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Dmitri A Petrov
- Department of Biology, Stanford University, Stanford, California
| | - Monte M Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, California.
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Department of Pathology, Stanford University School of Medicine, Stanford, California
| | - Katerina Politi
- Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut.
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut
- Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
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Mouse-human co-clinical trials demonstrate superior anti-tumour effects of buparlisib (BKM120) and cetuximab combination in squamous cell carcinoma of head and neck. Br J Cancer 2020; 123:1720-1729. [PMID: 32963347 PMCID: PMC7722843 DOI: 10.1038/s41416-020-01074-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 06/09/2020] [Accepted: 09/02/2020] [Indexed: 12/14/2022] Open
Abstract
Background Recurrent and/or metastatic squamous cell carcinoma of head and neck (R/M SCCHN) is a common cancer with high recurrence and mortality. Current treatments have low response rates (RRs). Methods Fifty-three patients with R/M SCCHN received continuous oral buparlisib. In parallel, patient-derived xenografts (PDXs) were established in mice to evaluate resistance mechanisms and efficacy of buparlisib/cetuximab combination. Baseline and on-treatment tumour genomes and transcriptomes were sequenced. Based on the integrated clinical and PDX data, 11 patients with progression under buparlisib monotherapy were treated with a combination of buparlisib and cetuximab. Results For buparlisib monotherapy, disease control rate (DCR) was 49%, RR was 3% and median progression-free survival (PFS) and overall survival (OS) were 63 and 143 days, respectively. For combination therapy, DCR was 91%, RR was 18% and median PFS and OS were 111 and 206 days, respectively. Four PDX models were originated from patients enrolled in the current clinical trial. While buparlisib alone did not inhibit tumour growth, combination therapy achieved tumour inhibition in three of seven PDXs. Genes associated with apoptosis and cell-cycle arrest were expressed at higher levels with combination treatment than with buparlisib or cetuximab alone. Conclusions The buparlisib/cetuximab combination has significant promise as a treatment strategy for R/M SCCHN. Clinical Trial Registration NCT01527877.
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Seo JS, Lee JW, Kim A, Shin JY, Jung YJ, Lee SB, Kim YH, Park S, Lee HJ, Park IK, Kang CH, Yun JY, Kim J, Kim YT. Whole Exome and Transcriptome Analyses Integrated with Microenvironmental Immune Signatures of Lung Squamous Cell Carcinoma. Cancer Immunol Res 2018; 6:848-859. [PMID: 29720381 DOI: 10.1158/2326-6066.cir-17-0453] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Revised: 12/18/2017] [Accepted: 04/25/2018] [Indexed: 11/16/2022]
Abstract
The immune microenvironment in lung squamous cell carcinoma (LUSC) is not well understood, with interactions between the host immune system and the tumor, as well as the molecular pathogenesis of LUSC, awaiting better characterization. To date, no molecularly targeted agents have been developed for LUSC treatment. Identification of predictive and prognostic biomarkers for LUSC could help optimize therapy decisions. We sequenced whole exomes and RNA from 101 tumors and matched noncancer control Korean samples. We used the information to predict subtype-specific interactions within the LUSC microenvironment and to connect genomic alterations with immune signatures. Hierarchical clustering based on gene expression and mutational profiling revealed subtypes that were either immune defective or immune competent. We analyzed infiltrating stromal and immune cells to further characterize the tumor microenvironment. Elevated expression of macrophage 2 signature genes in the immune competent subtype confirmed that tumor-associated macrophages (TAM) linked inflammation and mutation-driven cancer. A negative correlation was evident between the immune score and the amount of somatic copy-number variation (SCNV) of immune genes (r = -0.58). The SCNVs showed a potential detrimental effect on immunity in the immune-deficient subtype. Knowledge of the genomic alterations in the tumor microenvironment could be used to guide design of immunotherapy options that are appropriate for patients with certain cancer subtypes. Cancer Immunol Res; 6(7); 848-59. ©2018 AACR.
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Affiliation(s)
- Jeong-Sun Seo
- Precision Medicine Center, Seoul National University Bundang Hospital, Seongnamsi, Korea. .,Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Republic of Korea.,Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.,Macrogen Inc., Seoul, Republic of Korea
| | - Ji Won Lee
- Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Republic of Korea.,Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ahreum Kim
- Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Republic of Korea.,Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jong-Yeon Shin
- Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Republic of Korea.,Macrogen Inc., Seoul, Republic of Korea
| | - Yoo Jin Jung
- Seoul National University Cancer Research Institute, Seoul, Republic of Korea
| | - Sae Bom Lee
- Seoul National University Cancer Research Institute, Seoul, Republic of Korea
| | - Yoon Ho Kim
- Seoul National University Cancer Research Institute, Seoul, Republic of Korea
| | - Samina Park
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun Joo Lee
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - In-Kyu Park
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chang-Hyun Kang
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Ji-Young Yun
- Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Republic of Korea.,Macrogen Inc., Seoul, Republic of Korea
| | - Jihye Kim
- Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Republic of Korea.,Macrogen Inc., Seoul, Republic of Korea
| | - Young Tae Kim
- Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Republic of Korea. .,Seoul National University Cancer Research Institute, Seoul, Republic of Korea.,Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea
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Peng Y, Li W, Liu Y. A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data for Cancer Classification. Cancer Inform 2017. [DOI: 10.1177/117693510600200024] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Microarrays allow researchers to monitor the gene expression patterns for tens of thousands of genes across a wide range of cellular responses, phenotype and conditions. Selecting a small subset of discriminate genes from thousands of genes is important for accurate classification of diseases and phenotypes. Many methods have been proposed to find subsets of genes with maximum relevance and minimum redundancy, which can distinguish accurately between samples with different labels. To find the minimum subset of relevant genes is often referred as biomarker discovery. Two main approaches, filter and wrapper techniques, have been applied to biomarker discovery. In this paper, we conducted a comparative study of different biomarker discovery methods, including six filter methods and three wrapper methods. We then proposed a hybrid approach, FR-Wrapper, for biomarker discovery. The aim of this approach is to find an optimum balance between the precision of the biomarker discovery and the computation cost, by taking advantages of both filter method's efficiency and wrapper method's high accuracy. Our hybrid approach applies Fisher's ratio, a simple method easy to understand and implement, to filter out most of the irrelevant genes, then a wrapper method is employed to reduce the redundancy. The performance of FR-Wrapper approach is evaluated over four widely used microarray datasets. Analysis of experimental results reveals that the hybrid approach can achieve the goal of maximum relevance with minimum redundancy.
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Affiliation(s)
- Yanxiong Peng
- Laboratory for Bioinformatics and Medical Informatics, University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A
- Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A
| | - Wenyuan Li
- Laboratory for Bioinformatics and Medical Informatics, University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A
- Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A
| | - Ying Liu
- Laboratory for Bioinformatics and Medical Informatics, University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A
- Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A
- Department of Molecular and Cell Biology, University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A
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Karim AF, Chandra P, Chopra A, Siddiqui Z, Bhaskar A, Singh A, Kumar D. Express path analysis identifies a tyrosine kinase Src-centric network regulating divergent host responses to Mycobacterium tuberculosis infection. J Biol Chem 2011; 286:40307-19. [PMID: 21953458 PMCID: PMC3220550 DOI: 10.1074/jbc.m111.266239] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Global gene expression profiling has emerged as a major tool in understanding complex response patterns of biological systems to perturbations. However, a lack of unbiased analytical approaches has restricted the utility of complex microarray data to gain novel system level insights. Here we report a strategy, express path analysis (EPA), that helps to establish various pathways differentially recruited to achieve specific cellular responses under contrasting environmental conditions in an unbiased manner. The analysis superimposes differentially regulated genes between contrasting environments onto the network of functional protein associations followed by a series of iterative enrichments and network analysis. To test the utility of the approach, we infected THP1 macrophage cells with a virulent Mycobacterium tuberculosis strain (H37Rv) or the attenuated non-virulent strain H37Ra as contrasting perturbations and generated the temporal global expression profiles. EPA of the results provided details of response-specific and time-dependent host molecular network perturbations. Further analysis identified tyrosine kinase Src as the major regulatory hub discriminating the responses between wild-type and attenuated Mtb infection. We were then able to verify this novel role of Src experimentally and show that Src executes its role through regulating two vital antimicrobial processes of the host cells (i.e. autophagy and acidification of phagolysosome). These results bear significant potential for developing novel anti-tuberculosis therapy. We propose that EPA could prove extremely useful in understanding complex cellular responses for a variety of perturbations, including pathogenic infections.
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Affiliation(s)
- Ahmad Faisal Karim
- Immunology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
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Wan R, Kiseleva L, Harada H, Mamitsuka H, Horton P. HAMSTER: visualizing microarray experiments as a set of minimum spanning trees. SOURCE CODE FOR BIOLOGY AND MEDICINE 2009; 4:8. [PMID: 19925686 PMCID: PMC2784758 DOI: 10.1186/1751-0473-4-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2009] [Accepted: 11/20/2009] [Indexed: 11/17/2022]
Abstract
Background Visualization tools allow researchers to obtain a global view of the interrelationships between the probes or experiments of a gene expression (e.g. microarray) data set. Some existing methods include hierarchical clustering and k-means. In recent years, others have proposed applying minimum spanning trees (MST) for microarray clustering. Although MST-based clustering is formally equivalent to the dendrograms produced by hierarchical clustering under certain conditions; visually they can be quite different. Methods HAMSTER (Helpful Abstraction using Minimum Spanning Trees for Expression Relations) is an open source system for generating a set of MSTs from the experiments of a microarray data set. While previous works have generated a single MST from a data set for data clustering, we recursively merge experiments and repeat this process to obtain a set of MSTs for data visualization. Depending on the parameters chosen, each tree is analogous to a snapshot of one step of the hierarchical clustering process. We scored and ranked these trees using one of three proposed schemes. HAMSTER is implemented in C++ and makes use of Graphviz for laying out each MST. Results We report on the running time of HAMSTER and demonstrate using data sets from the NCBI Gene Expression Omnibus (GEO) that the images created by HAMSTER offer insights that differ from the dendrograms of hierarchical clustering. In addition to the C++ program which is available as open source, we also provided a web-based version (HAMSTER+) which allows users to apply our system through a web browser without any computer programming knowledge. Conclusion Researchers may find it helpful to include HAMSTER in their microarray analysis workflow as it can offer insights that differ from hierarchical clustering. We believe that HAMSTER would be useful for certain types of gradient data sets (e.g time-series data) and data that indicate relationships between cells/tissues. Both the source and the web server variant of HAMSTER are available from http://hamster.cbrc.jp/.
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Affiliation(s)
- Raymond Wan
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, 611-0011, Japan.
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7
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Peng Y, Li W, Liu Y. A hybrid approach for biomarker discovery from microarray gene expression data for cancer classification. Cancer Inform 2007; 2:301-11. [PMID: 19458773 PMCID: PMC2675487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Microarrays allow researchers to monitor the gene expression patterns for tens of thousands of genes across a wide range of cellular responses, phenotype and conditions. Selecting a small subset of discriminate genes from thousands of genes is important for accurate classification of diseases and phenotypes. Many methods have been proposed to find subsets of genes with maximum relevance and minimum redundancy, which can distinguish accurately between samples with different labels. To find the minimum subset of relevant genes is often referred as biomarker discovery. Two main approaches, filter and wrapper techniques, have been applied to biomarker discovery. In this paper, we conducted a comparative study of different biomarker discovery methods, including six filter methods and three wrapper methods. We then proposed a hybrid approach, FR-Wrapper, for biomarker discovery. The aim of this approach is to find an optimum balance between the precision of the biomarker discovery and the computation cost, by taking advantages of both filter method's efficiency and wrapper method's high accuracy. Our hybrid approach applies Fisher's ratio, a simple method easy to understand and implement, to filter out most of the irrelevant genes, then a wrapper method is employed to reduce the redundancy. The performance of FR-Wrapper approach is evaluated over four widely used microarray datasets. Analysis of experimental results reveals that the hybrid approach can achieve the goal of maximum relevance with minimum redundancy.
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Affiliation(s)
- Yanxiong Peng
- Laboratory for Bioinformatics and Medical Informatics
- Department of Computer Science
| | - Wenyuan Li
- Laboratory for Bioinformatics and Medical Informatics
- Department of Computer Science
| | - Ying Liu
- Laboratory for Bioinformatics and Medical Informatics
- Department of Computer Science
- Department of Molecular and Cell Biology, University of Texas at Dallas, Richardson, TX 75083-0688, U.S.A
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New Plastic Microparticles and Nanoparticles for Fluorescent Sensing and Encoding. SPRINGER SERIES ON FLUORESCENCE 2007. [DOI: 10.1007/4243_2007_013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Maroulis DE, Flaounas IN, Iakovidis DK, Karkanis SA. Microarray-MD: a system for exploratory analysis of microarray gene expression data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2006; 83:157-67. [PMID: 16893587 DOI: 10.1016/j.cmpb.2006.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2005] [Revised: 05/30/2006] [Accepted: 06/08/2006] [Indexed: 05/11/2023]
Abstract
In this paper, we present Microarray Medical Data explorer (Microarray-MD), a novel software system that is able to assist in the exploratory analysis of gene expression microarray data. It implements a combination scheme of multiple Support Vector Machines, which integrates a variety of gene selection criteria and allows for the discrimination of multiple diseases or subtypes of a disease. The system can be trained and automatically tune its parameters with the provision of pathologically characterized gene expression data to its input. Given a set of new, uncharacterized, patient's data as input, it outputs a decision on the type or the subtype of a disease. A graphical user interface provides easy access to the system operations and direct adjustment of its parameters. It has been tested on various publicly available datasets. The overall accuracy it achieves was estimated to exceed 90%.
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Affiliation(s)
- D E Maroulis
- Real-Time Systems & Image Analysis Group, Department of Informatics and Telecommunication, University of Athens, Panepistimiopolis, Ilisia, 15784 Athens, Greece.
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Liu Y, Navathe SB, Civera J, Dasigi V, Ram A, Ciliax BJ, Dingledine R. Text mining biomedical literature for discovering gene-to-gene relationships: a comparative study of algorithms. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2005; 2:62-76. [PMID: 17044165 DOI: 10.1109/tcbb.2005.14] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.
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Affiliation(s)
- Ying Liu
- College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA 30322, USA.
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Potamias G, Dermon CR. Protein synthesis profiling in the developing brain: a graph theoretic clustering approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2004; 76:115-129. [PMID: 15451161 DOI: 10.1016/j.cmpb.2004.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2004] [Revised: 05/05/2004] [Accepted: 05/06/2004] [Indexed: 05/24/2023]
Abstract
Mapping regional brain development in terms of protein synthesis (PS) activity yields insight on specific spatio-temporal ontogenetic patterns. The biosynthetic activity of an individual brain nucleus is represented as a time-series object, and clustering of time-series contributes to the problem of inducing indicative patterns of brain developmental events and forming respective PS chronological maps. Clustering analysis of PS chronological maps, in comparison with epigenetic influences of alpha2 adrenoceptors treatment, reveals relationships between distantly located brain structures. Clustering is performed with a novel graph theoretic clustering approach (GTC). The approach is based on the weighted graph arrangement of the input objects and the iterative partitioning of the corresponding minimum spanning tree. The final result is a hierarchical clustering-tree organization of the input objects. Application of GTC on the PS patterns in developing brain revealed five main clusters that correspond to respective brain development indicative profiles. The induced profiles confirm experimental findings, and provide evidence for further experimental studies.
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Affiliation(s)
- George Potamias
- Institute of Computer Science, Foundation for Research and Technology-Hellas, Vassilika Vouton, 711 10 Heraklion, Crete, Greece.
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