BACKGROUND: The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses. RESULTS: The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs. CONCLUSION: The proposed learning framework represents a promising approach predicting drug response in tumour cells.
BACKGROUND: The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses. RESULTS: The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs. CONCLUSION: The proposed learning framework represents a promising approach predicting drug response in tumour cells.
Authors: Anna G Antonacopoulou; Petros D Grivas; Lambros Skarlas; Melpomeni Kalofonos; Chrisoula D Scopa; Haralabos P Kalofonos Journal: Anticancer Res Date: 2008 Mar-Apr Impact factor: 2.480
Authors: Simon Rogers; Mark Girolami; Walter Kolch; Katrina M Waters; Tao Liu; Brian Thrall; H Steven Wiley Journal: Bioinformatics Date: 2008-10-30 Impact factor: 6.937