title: Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.
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.
authors.affiliation: Department of Information Technology, Ghent University-iMinds, Ghent, Belgium.
authors.forename: Lieven P C
authors.initials: LP
authors.lastname: Verbeke
authors.affiliation: Department of Information Technology, Ghent University-iMinds, Ghent, Belgium; Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
authors.forename: Jimmy
authors.initials: J
authors.lastname: Van den Eynden
authors.affiliation: Department of Information Technology, Ghent University-iMinds, Ghent, Belgium; Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
authors.forename: Ana Carolina
authors.initials: AC
authors.lastname: Fierro
authors.affiliation: Department of Information Technology, Ghent University-iMinds, Ghent, Belgium.
authors.forename: Piet
authors.initials: P
authors.lastname: Demeester
authors.affiliation: Department of Information Technology, Ghent University-iMinds, Ghent, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Fostier
authors.affiliation: Department of Information Technology, Ghent University-iMinds, Ghent, Belgium; Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Marchal
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D056915:DNA Copy Number Variations; D019175:DNA Methylation; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D020935:MAP Kinase Signaling System / Q000235:genetics; D009154:Mutation; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000378:metabolism*; D051057:Proto-Oncogene Proteins c-akt / Q000235:genetics / Q000378:metabolism; D050791:STAT Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D050791:STAT Transcription Factors; D051057:Proto-Oncogene Proteins c-akt