Personalized medicine for mucositis: Bayesian networks identify unique gene clusters which predict the response to gamma-D-glutamyl-L-tryptophan (SCV-07) for the attenuation of chemoradiation-induced oral mucositis

Gamma-D-glutamyl-L-tryptophan (SCV-07) demonstrated an overall efficacy signal in ameliorating oral mucositis (OM) in a clinical trial of head and neck cancer patients. However, not all SCV-07-treated subjects responded positively. Here we determined if specific gene clusters could discriminate between subjects who responded to SCV-07 and those who did not. Microarrays were done using peripheral blood RNA obtained at screening and on the last day of radiation from 28 subjects enrolled in the SCV-07 trial. An analytical technique was applied that relied on learned Bayesian networks to identify gene clusters which discriminated between individuals who received SCV-07 and those who received placebo, and which differentiated subjects for whom SCV-07 was an effective OM intervention from those for whom it was not. We identified 107 genes that discriminated SCV-07 responders from non-responders using four models and applied Akaike Information Criteria (AIC) and Bayes Factor (BF) analysis to evaluate predictive accuracy. AIC were superior to BF: the accuracy of predicting placebo vs. treatment was 78% using BF, but 91% using the AIC score. Our ability to differentiate responders from non-responders using the AIC score was dramatic and ranged from 93% to 100% depending on the dataset that was evaluated. Predictive Bayesian networks were identified and functional cluster analyses were performed. A specific 10 gene cluster was a critical contributor to the predictability of the dataset. Our results demonstrate proof of concept in which the application of a genomics-based analytical paradigm was capable of discriminating responders and non-responders for an OM intervention. (C) 2011 Elsevier Ltd. All rights reserved.