Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells.

TitleConsensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells.
Publication TypeJournal Article
Year of Publication2011
AuthorsCavill R, Kamburov A, Ellis JK, Athersuch TJ, Blagrove MSC, Herwig R, Ebbels TMD, Keun HC
JournalPLoS Comput Biol
Volume7
Issue3
Paginatione1001113
Date Published2011 Mar
ISSN1553-7358
KeywordsAntineoplastic Agents, Biological Markers, Carboplatin, Cell Line, Tumor, Cisplatin, Computational Biology, Drug Screening Assays, Antitumor, False Positive Reactions, Humans, Lipoproteins, Metabolomics, Neoplasms, Oligonucleotide Array Sequence Analysis, Organoplatinum Compounds, Phenotype, Transcription, Genetic
Abstract

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

DOI10.1371/journal.pcbi.1001113
Alternate JournalPLoS Comput. Biol.
PubMed ID21483477