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Simultaneous discovery of cancer subtypes and subtype features by molecular data integration

(2016) BIOINFORMATICS. 32(17). p.445-454
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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Motivation: Subtyping cancer is key to an improved and more personalized prognosis/treatment. The increasing availability of tumor related molecular data provides the opportunity to identify molecular subtypes in a data-driven way. Molecular subtypes are defined as groups of samples that have a similar molecular mechanism at the origin of the carcinogenesis. The molecular mechanisms are reflected by subtype-specific mutational and expression features. Data-driven subtyping is a complex problem as subtyping and identifying the molecular mechanisms that drive carcinogenesis are confounded problems. Many current integrative subtyping methods use global mutational and/or expression tumor profiles to group tumor samples in subtypes but do not explicitly extract the subtype-specific features. We therefore present a method that solves both tasks of subtyping and identification of subtype-specific features simultaneously. Hereto our method integrates' mutational and expression data while taking into account the clonal properties of carcinogenesis. Key to our method is a formalization of the problem as a rank matrix factorization of ranked data that approaches the subtyping problem as multi-view bi-clustering. Results: We introduce a novel integrative framework to identify subtypes by combining mutational and expression features. The incomparable measurement data is integrated by transformation into ranked data and subtypes are defined as multi-view bi-clusters. We formalize the model using rank matrix factorization, resulting in the SRF algorithm. Experiments on simulated data and the TCGA breast cancer data demonstrate that SRF is able to capture subtle differences that existing methods may miss.
Keywords
IBCN, IDENTIFICATION, BREAST-CANCER, GLIOBLASTOMA, MUTATIONS, CYTOSCAPE, COPY-NUMBER ALTERATION, PATTERNS, TUMORS, INTERACTION NETWORKS, GENOMIC DATA

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Citation

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Chicago
Van, Thanh Le, Matthijs van Leeuwen, Ana Carolina Elisa Fierro Gutierrez, Dries De Maeyer, Jimmy Van den Eynden, Lieven Verbeke, Luc De Raedt, Kathleen Marchal, and Siegfries Nijssen. 2016. “Simultaneous Discovery of Cancer Subtypes and Subtype Features by Molecular Data Integration.” Bioinformatics 32 (17): 445–454.
APA
Van, T. L., van Leeuwen, M., Fierro Gutierrez, A. C. E., De Maeyer, D., Van den Eynden, J., Verbeke, L., De Raedt, L., et al. (2016). Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. BIOINFORMATICS, 32(17), 445–454. Presented at the 15th European Conference on Computational Biology (ECCB).
Vancouver
1.
Van TL, van Leeuwen M, Fierro Gutierrez ACE, De Maeyer D, Van den Eynden J, Verbeke L, et al. Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. BIOINFORMATICS. 2016;32(17):445–54.
MLA
Van, Thanh Le, Matthijs van Leeuwen, Ana Carolina Elisa Fierro Gutierrez, et al. “Simultaneous Discovery of Cancer Subtypes and Subtype Features by Molecular Data Integration.” BIOINFORMATICS 32.17 (2016): 445–454. Print.
@article{8173869,
  abstract     = {Motivation: Subtyping cancer is key to an improved and more personalized prognosis/treatment. The increasing availability of tumor related molecular data provides the opportunity to identify molecular subtypes in a data-driven way. Molecular subtypes are defined as groups of samples that have a similar molecular mechanism at the origin of the carcinogenesis. The molecular mechanisms are reflected by subtype-specific mutational and expression features. Data-driven subtyping is a complex problem as subtyping and identifying the molecular mechanisms that drive carcinogenesis are confounded problems. Many current integrative subtyping methods use global mutational and/or expression tumor profiles to group tumor samples in subtypes but do not explicitly extract the subtype-specific features. We therefore present a method that solves both tasks of subtyping and identification of subtype-specific features simultaneously. Hereto our method integrates' mutational and expression data while taking into account the clonal properties of carcinogenesis. Key to our method is a formalization of the problem as a rank matrix factorization of ranked data that approaches the subtyping problem as multi-view bi-clustering. 
Results: We introduce a novel integrative framework to identify subtypes by combining mutational and expression features. The incomparable measurement data is integrated by transformation into ranked data and subtypes are defined as multi-view bi-clusters. We formalize the model using rank matrix factorization, resulting in the SRF algorithm. Experiments on simulated data and the TCGA breast cancer data demonstrate that SRF is able to capture subtle differences that existing methods may miss.},
  author       = {Van, Thanh Le and van Leeuwen, Matthijs and Fierro Gutierrez, Ana Carolina Elisa and De Maeyer, Dries and Van den Eynden, Jimmy and Verbeke, Lieven and De Raedt, Luc and Marchal, Kathleen and Nijssen, Siegfries},
  issn         = {1367-4803},
  journal      = {BIOINFORMATICS},
  keyword      = {IBCN,IDENTIFICATION,BREAST-CANCER,GLIOBLASTOMA,MUTATIONS,CYTOSCAPE,COPY-NUMBER ALTERATION,PATTERNS,TUMORS,INTERACTION NETWORKS,GENOMIC DATA},
  language     = {eng},
  location     = {The Hague, The Netherlands},
  number       = {17},
  pages        = {445--454},
  title        = {Simultaneous discovery of cancer subtypes and subtype features by molecular data integration},
  url          = {http://dx.doi.org/10.1093/bioinformatics/btw434},
  volume       = {32},
  year         = {2016},
}

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