Genexpressionsanalysen beim kolorektalen Karzinom -Statistische Auswertung und potentielle prognostische Bedeutung

Abstract

Classification of patient samples is an important aspect of cancer diagnosis and treatment. Recent microarray studies have shown that cancer classification by gene expression profiling is feasible and provides clinicians with additional information to choose the most appropriate forms of treatment. A „genetic algorithm“, using K-Nearest Neighbour-Classification was used to identify diagnostically relevant probeset combinations (classifier) to classify patients with sporadic colorectal cancer into stages without nodal and distant metastasis (UICCI/II) and patients with nodal and distant metastasis (UICC III/IV). The algorithm was fed with the 5% top and 5% bottom probesets after statistical ranking (Golub, Wilcoxon, foldchange). Thus 2228 probesets have been used as a starting pool. Discriminating probeset combinations have been identified in a training data set and checked using a non-overlapping test set. Probesets classifying correctly in more than 99% could be identified for tumor vs. normal tissue distinction and for UICC I/II vs. III/IV distinction in the training set. In non-overlapping test-set tumor/normal classifier performed well (90%), whereas UICC classifier only reached 60% performance. In conclusion, most likely generalization properties of the signatures are poor because data representativity is not sufficient using this approach. Sample number is quite appropriate to identify differentially expressed genes between tumor and normal tissues but it is likely to be isufficient to reliably reveal differentially expressed genes between distinct prognostic stages based on UICC.