title: 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.affiliation: Children's Hospital Informatics Program at the Harvard/MIT Health Sciences and Technology Division, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Partners HealthCare Center for Personalized Genetic Medicine, United States. Gil@chip.org
authors.forename: Gil
authors.initials: G
authors.lastname: Alterovitz
authors.forename: Cynthia
authors.initials: C
authors.lastname: Tuthill
authors.forename: Israel
authors.initials: I
authors.lastname: Rios
authors.forename: Katharina
authors.initials: K
authors.lastname: Modelska
authors.forename: Stephen
authors.initials: S
authors.lastname: Sonis
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D059248:Chemoradiotherapy / Q000009:adverse effects*; D004151:Dipeptides / Q000627:therapeutic use*; D005260:Female; D023281:Genomics / Q000379:methods; D006258:Head and Neck Neoplasms / Q000188:drug therapy / Q000532:radiotherapy*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009061:Mouth Mucosa / Q000187:drug effects; D005810:Multigene Family; D057285:Precision Medicine / Q000379:methods*; D011237:Predictive Value of Tests; D011832:Radiation Injuries / Q000235:genetics / Q000517:prevention & control; D013280:Stomatitis / Q000235:genetics / Q000517:prevention & control; D016896:Treatment Outcome
chemical_list: D004151:Dipeptides; C470075:golotimod