PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Integrating Microarray and Proteomics Data to Predict the Response of Cetuximab in Patients with Rectal Cancer
Anneleen Daemen, Olivier Gevaert, Tijl De Bie, Annelies Debucquoy, Jean-Pascal Machiels, Bart De Moor and Karin Haustermans
In: Pacific Symposium on Biocomputing (PSB), Hawaii(2008).


To investigate the combination of cetuximab, capecitabine and radiotherapy in the preoperative treatment of patients with rectal cancer, fourty tumour samples were gathered before treatment (T0), after one dose of cetuximab but before radiotherapy with capecitabine (T1) and at moment of surgery (T2). The tumour and plasma samples were subjected at all timepoints to Affymetrix microarray and Luminex proteomics analysis, respectively. At surgery, the Rectal Cancer Regression Grade (RCRG) was registered. We used a kernel-based method with Least Squares Support Vector Machines to predict RCRG based on the integration of microarray and proteomics data on To and T1. We demonstrated that combining multiple data sources improves the predictive power. The best model was based on 5 genes and 10 proteins at T0 and T1 and could predict the RCRG with an accuracy of 91.7%, sensitivity of 96.2% and specificity of 80%.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:4807
Deposited By:Tijl De Bie
Deposited On:24 March 2009