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.

Abstract

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.

• Authors: Gil Alterovitz, Cynthia Tuthill, Israel Rios, Katharina Modelska, Stephen Sonis
• Journal: Oral oncology (Oral Oncol) Vol. 47 Issue 10 Pg. 951-5 (Oct 2011) ISSN: 1879-0593 [Electronic] England
• PMID: 21824803 (Publication Type: Clinical Trial, Journal Article)
• Copyright: Copyright © 2011 Elsevier Ltd. All rights reserved.

Chemical References

• Dipeptides
• golotimod

Topics

• Adult
• Aged
• Aged, 80 and over
• Bayes Theorem
• Chemoradiotherapy (adverse effects)
• Dipeptides (therapeutic use)
• Female
• Genomics (methods)
• Head and Neck Neoplasms (drug therapy, radiotherapy)
• Humans
• Male
• Middle Aged
• Mouth Mucosa (drug effects)
• Multigene Family
• Precision Medicine (methods)
• Predictive Value of Tests
• Radiation Injuries (genetics, prevention & control)
• Stomatitis (genetics, prevention & control)
• Treatment Outcome