Gene expression monitoring accurately predicts medulloblastoma positive and negative clinical outcomes.
Prediction of medulloblastoma clinical outcome is crucial to personalizing treatment, both to identify high-risk patients for aggressive or alternative therapy and to spare those at low risk from excessive treatment. The best predictors [Pomeroy et al. (2002) Nature 415, 436-442], based on gene expression monitoring at diagnosis, have shown much less accuracy in recognizing patients with eventual failed outcomes - <50% for the predictor making fewest total errors - than those who would survive, while a single gene predictor exhibited reverse asymmetry. Such inaccuracy in recognizing one of the outcomes is a problem for clinical use. We hypothesized that a non-linear model could be built to significantly improve prediction of medulloblastoma outcome, thereby promoting use of gene-expression-based predictors in a clinical setting. In fact, this approach resulted in fewer errors and much less asymmetry in prediction, and bidirectional accuracy of about 80% could be obtained via its combination with other methods. Indeed, three combinations of methods were identified that yielded significantly better predictions of clinical outcome than previously attained, making feasible predictors of medulloblastoma treatment response with greatly improved bidirectional accuracy essential for clinical use.
Korenberg
Michael J
MJ
Department of Electrical and Computer Engineering, Queen's University, K7L 3N6, Kingston, ON, Canada. korenberg@post.queensu.ca
D002528:Cerebellar Neoplasms / Q000235:genetics* / Q000628:therapy; D020869:Gene Expression Profiling; D006801:Humans; D008527:Medulloblastoma / Q000235:genetics* / Q000628:therapy; D008954:Models, Biological; D017711:Nonlinear Dynamics; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D011379:Prognosis; D012307:Risk Factors; D016896:Treatment Outcome