Daemen Anneleen, Gevaert Olivier, De Bie Tijl, Debucquoy Annelies, Machiels Jean-Pascal, De Moor Bart, Haustermans Karin
Katholieke Universiteit Leuven, Department of Electrical Engineering (ESAT), SCD-SISTA (BIOI), Kasteelpark Arenberg 10--bus 2446, B-3001 Leuven, Heverlee, Belgium.
Pac Symp Biocomput. 2008:166-77.
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%.
为研究西妥昔单抗、卡培他滨与放疗联合用于直肠癌患者术前治疗的效果，在治疗前（T0）、一剂西妥昔单抗给药后但在卡培他滨放疗前（T1）以及手术时（T2）采集了40份肿瘤样本。在所有时间点，肿瘤样本和血浆样本分别接受了Affymetrix微阵列分析和Luminex蛋白质组学分析。手术时，记录直肠癌消退分级（RCRG）。我们使用基于核的最小二乘支持向量机方法，基于T0和T1时微阵列和蛋白质组学数据的整合来预测RCRG。我们证明，合并多个数据源可提高预测能力。最佳模型基于T0和T1时的5个基因和10种蛋白质，预测RCRG的准确率为91.7%，灵敏度为96.2%，特异性为80%。
