Tumor stratification by a novel graph-regularized bi-clique finding algorithm
Abstract
Due to involved disease mechanisms, many complex diseases such as cancer, demonstrate significant heterogeneity with varying behaviors, including different survival time, treatment responses, and recurrence rates. The aim of tumor stratification is to identify disease subtypes, which is an important first step towards precision medicine. Recent advances in profiling a large number of molecular variables such as in The Cancer Genome Atlas (TCGA), have enabled researchers to implement computational methods, including traditional clustering and bi-clustering algorithms, to systematically analyze high-throughput molecular measurements to identify tumor subtypes as well as their corresponding associated biomarkers. In this study we discuss critical issues and challenges in existing computational approaches for tumor stratification. We show that the problem can be formulated as finding densely connected sub-graphs (bi-cliques) in a bipartite graph representation of genomic data. We propose a novel algorithm that takes advantage of prior biology knowledge through a gene-gene interaction network to find such sub-graphs, which helps simultaneously identify both tumor subtypes and their corresponding genetic markers. Our experimental results show that our proposed method outperforms current state-of-the-art methods for tumor stratification.
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Even when no sample classification is available, subpopulation-specific modules can be resolved, an approach that has been particularly successful in classifying different cancer subtypes to provide prognostic markers.
“Even when no sample classification is available, subpopulation-specific modules can be resolved, an approach that has been particularly successful in classifying different cancer subtypes to provide prognostic markers [18–20].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [20]
Cites
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590 · see citing works
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Nonparametric Estimation from Incomplete Observations
Kaplan, E. L., & Meier, P. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-4380-9_25 · Cited by 45,232 · see citing works
Nonparametric Estimation from Incomplete Observations
Kaplan, E. L., & Meier, P. (1958) · Journal of the American Statistical Association · doi:10.1080/01621459.1958.10501452 · Cited by 39,158 · see citing works
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785 · see citing works
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568 · see citing works
Integrated genomic analyses of ovarian carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927 · see citing works
Objective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,493 · see citing works
Biclustering algorithms for biological data analysis: a survey
Madeira, S. C., & Oliveira, A. L. (2004) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2004.2 · Cited by 2,049 · see citing works
Co-clustering documents and words using bipartite spectral graph partitioning
Dhillon, I. S. (2001) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502550 · Cited by 1,726 · see citing works
G-protein-coupled receptors and cancer
Dorsam, R. T., & Gutkind, J. S. (2007) · Nature reviews. Cancer · doi:10.1038/nrc2069 · Cited by 1,399 · see citing works
CHARM: An Efficient Algorithm for Closed Itemset Mining
Zaki, M. J., & Hsiao, C.-J. (2002) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972726.27 · Cited by 930 · see citing works
Associating Genes and Protein Complexes with Disease via Network Propagation
Vanunu, O., Magger, O., Ruppin, E., Shlomi, T., & Sharan, R. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000641 · Cited by 864 · see citing works
Modularity and community detection in bipartite networks
Barber, M. J. (2007) · Physical Review E · doi:10.1103/physreve.76.066102 · Cited by 774 · see citing works
Maxima for Graphs and a New Proof of a Theorem of Turán
Motzkin, T. S., & Straus, E. G. (1965) · Canadian Journal of Mathematics · doi:10.4153/cjm-1965-053-6 · Cited by 730 · see citing works
Gene expression profiling in breast cancer: classification, prognostication, and prediction
Reis-Filho, J. S., & Pusztai, L. (2011) · The Lancet · doi:10.1016/s0140-6736(11)61539-0 · Cited by 729 · see citing works
Network-based stratification of tumor mutations
Hofree, M., Shen, J. P., Carter, H. K., Gross, A. M., & Ideker, T. E. (2013) · Nature Methods · doi:10.1038/nmeth.2651 · Cited by 726 · see citing works
Network-constrained regularization and variable selection for analysis of genomic data
Li, C., & Li, H. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn081 · Cited by 602 · see citing works
Pearson, K. (1900) · Philosophical Transactions of the Royal Society of London Series A Containing Papers of a Mathematical or Physical Character · doi:10.1098/rsta.1900.0022 · Cited by 486 · see citing works
Konstantinopoulos, P. A., Spentzos, D., Karlan, B. Y., Taniguchi, T., Fountzilas, E., Francoeur, N. J., Levine, D. A., & Cannistra, S. A. (2010) · Journal of Clinical Oncology · doi:10.1200/jco.2009.27.5719 · Cited by 480 · see citing works
Non-negative Matrix Factorization on Manifold
Cai, D., He, X., Xiaoyun, W., & Han, J. (2008) · Proceedings · doi:10.1109/icdm.2008.57 · Cited by 433 · see citing works
Armitage, P. 1.-2. (2005) · Wiley eBooks · doi:10.1002/0470011815 · Cited by 409 · see citing works
New Algorithms for Enumerating All Maximal Cliques
Makino, K., & Uno, T. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27810-8_23 · Cited by 407 · see citing works
Cancer systems biology: a network modeling perspective
Kreeger, P. K., & Lauffenburger, D. A. (2009) · Carcinogenesis · doi:10.1093/carcin/bgp261 · Cited by 370 · see citing works
Bipartite graph partitioning and data clustering
Hongyuan Zha, B., He, X., Ding, C. H. Q., Simon, H. D., & Gu, M. (2001) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/502585.502591 · Cited by 305 · see citing works
Horiuchi, A., Imai, T., Wang, C., Ohira, S., Feng, Y., Nikaido, T., & Konishi, I. (2003) · Laboratory Investigation · doi:10.1097/01.lab.0000073128.16098.31 · Cited by 233 · see citing works
Zhang, Y., Phillips, C., Rogers, G. L., Baker, E. J., Chesler, E. J., & Langston, M. A. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-110 · Cited by 189 · see citing works
Esteva, F. J., Şahin, A. A., Cristofanilli, M., Coombes, K. R., Lee, S. J., Baker, J. B., Cronin, M. T., Walker, M., Watson, D., Shak, S., et al. (2005) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-04-1707 · Cited by 167 · see citing works
Sixth International Conference on Data Mining (ICDM'06)
(2006) · doi:10.1109/icdm11877.2006 · Cited by 130 · see citing works
Ip, C. K. M., Cheung, A., Ngan, H., & Wong, A. S. T. (2011) · Oncogene · doi:10.1038/onc.2010.615 · Cited by 104 · see citing works
Gene-expression profiling in epithelial ovarian cancer
Konstantinopoulos, P. A., Spentzos, D., & Cannistra, S. A. (2008) · Nature Clinical Practice Oncology · doi:10.1038/ncponc1178 · Cited by 100 · see citing works
Caneba, C. A., Bellancé, N., Yang, L., Pabst, L., & Nagrath, D. (2012) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00151.2012 · Cited by 94 · see citing works
de Berg, M., Justo, S. C., Giannopoulos, P., Knauer, C., van Oostrum, R., Veltkamp, R. C., Hagerup, T., Katajainen, J., Geometrie, B. E. V. O., & Informática, D. E. (2004) · Lecture notes in computer science · doi:10.1007/b98413 · Cited by 86 · see citing works
Ko, S. Y., Guo, H., Barengo, N., & Naora, H. (2009) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-08-2924 · Cited by 72 · see citing works
Gavalas, N. G., Karadimou, A., Dimopoulos, M. A., & Bamias, A. (2010) · Journal of Immunology Research · doi:10.1155/2010/791603 · Cited by 66 · see citing works
Linear Rank Tests in Survival Analysis
Harrington, D. T. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a11047 · Cited by 54 · see citing works
Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms
Li, J., Bennett, K. L., Stukalov, A., Fang, B., Zhang, G., Yoshida, T., Okamoto, I., Kim, J., Song, L., Bai, Y., et al. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.61 · Cited by 51 · see citing works
Self-citationBiclustering Protein Complex Interactions with a Biclique Finding Algorithm
Ding, C. H. Q., Zhang, Y., Li, T., & Holbrook, S. R. (2006) · Proceedings · doi:10.1109/icdm.2006.27 · Cited by 39 · see citing works
Vitamin A metabolism is impaired in human ovarian cancer
Williams, S. J., Cvetković, D., & Hamilton, T. C. (2008) · Gynecologic Oncology · doi:10.1016/j.ygyno.2008.11.015 · Cited by 36 · see citing works
Folkman, J. (2001) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/93.10.734 · Cited by 36 · see citing works
Recent insights into the role of NF-kappaB in ovarian carcinogenesis
Alvero, A. B. (2010) · Genome Medicine · doi:10.1186/gm177 · Cited by 36 · see citing works
Wang, Y. K., Print, C. G., & Crampin, E. J. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-102 · Cited by 28 · see citing works
A Novel Approach for Biclustering Gene Expression Data Using Modular Singular Value Decomposition
Aradhya, V. N. M., Masulli, F., & Stefano Rovetta, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14571-1_19 · Cited by 11 · see citing works
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Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
He, Z., Zhang, J., Yuan, X., Liu, Z., Liu, B., Tuo, S., & Liu, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177662 · Cited by 22 · see citing works
Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers
Alonso, R., Monroy, R., & Trejo, L. A. (2016) · Sensors · doi:10.3390/s16081311 · Cited by 14 · see citing works
- Authors
- Ahmadi Adl, A., & Qian, X.
- Affiliations
- University of South Florida (US)
- ORCID
- Qian, Xiaoning
- Venue
- Computational Biology and Chemistry
- Publisher
- Elsevier BV
- Published
- 2015-08
- Volume
- 57
- Pages
- 3-11
- ISSN
- 1476-9271 (ISSN-L)
- OpenAlex
- W1997975056
- Cited by
- 3
- References
- 46
- Citations by year
- 2017: 1 · 2016: 2
- FWCI
- 0.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computational Biology and Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- tumor stratification, tumor subtypes, graph regularization, bipartite graphs, gene interaction networks, biomarker discovery, tumor heterogeneity, biclustering, genomic data
- MeSH terms
- Algorithms, Humans, Neoplasms, Computational Biology
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