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Pathway relevance ranking for tumor samples through network-based data integration

(2015) PLOS ONE. 10(7).
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01MR0410
Abstract
The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival.
Keywords
MUTATIONS, PARADIGM, OVARIAN-CANCER, IBCN, BREAST-CANCER, IMPACT ANALYSIS, NOTCH, PRIORITIZATION, ARCHITECTURE, CLASSIFICATION, GENOMES

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Citation

Please use this url to cite or link to this publication:

MLA
Verbeke, Lieven et al. “Pathway Relevance Ranking for Tumor Samples Through Network-based Data Integration.” PLOS ONE 10.7 (2015): n. pag. Print.
APA
Verbeke, Lieven, Van den Eynden, J., Fierro Gutierrez, A. C. E., Demeester, P., Fostier, J., & Marchal, K. (2015). Pathway relevance ranking for tumor samples through network-based data integration. PLOS ONE, 10(7).
Chicago author-date
Verbeke, Lieven, Jimmy Van den Eynden, Ana Carolina Elisa Fierro Gutierrez, Piet Demeester, Jan Fostier, and Kathleen Marchal. 2015. “Pathway Relevance Ranking for Tumor Samples Through Network-based Data Integration.” Plos One 10 (7).
Chicago author-date (all authors)
Verbeke, Lieven, Jimmy Van den Eynden, Ana Carolina Elisa Fierro Gutierrez, Piet Demeester, Jan Fostier, and Kathleen Marchal. 2015. “Pathway Relevance Ranking for Tumor Samples Through Network-based Data Integration.” Plos One 10 (7).
Vancouver
1.
Verbeke L, Van den Eynden J, Fierro Gutierrez ACE, Demeester P, Fostier J, Marchal K. Pathway relevance ranking for tumor samples through network-based data integration. PLOS ONE. 2015;10(7).
IEEE
[1]
L. Verbeke, J. Van den Eynden, A. C. E. Fierro Gutierrez, P. Demeester, J. Fostier, and K. Marchal, “Pathway relevance ranking for tumor samples through network-based data integration,” PLOS ONE, vol. 10, no. 7, 2015.
@article{6990481,
  abstract     = {The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival.},
  articleno    = {e0133503},
  author       = {Verbeke, Lieven and Van den Eynden, Jimmy and Fierro Gutierrez, Ana Carolina Elisa and Demeester, Piet and Fostier, Jan and Marchal, Kathleen},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keywords     = {MUTATIONS,PARADIGM,OVARIAN-CANCER,IBCN,BREAST-CANCER,IMPACT ANALYSIS,NOTCH,PRIORITIZATION,ARCHITECTURE,CLASSIFICATION,GENOMES},
  language     = {eng},
  number       = {7},
  title        = {Pathway relevance ranking for tumor samples through network-based data integration},
  url          = {http://dx.doi.org/10.1371/journal.pone.0133503},
  volume       = {10},
  year         = {2015},
}

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