SNP ‐based B ayesian networks can predict oral mucositis risk in autologous stem cell transplant recipients

Objective Approximately 40% of patients receiving conditioning chemotherapy prior to autologous hematopoietic stem cell transplants (a HSCT ) develop severe oral mucositis ( SOM ). Aside from disabling pain, ulcerative lesions associated with SOM predispose to poor health and economic outcomes. Our objective was to develop a probabilistic graphical model in which a cluster of single‐nucleotide polymorphisms ( SNP s) derived from salivary DNA could be used as a tool to predict SOM risk. Methods Salivary DNA was extracted from 153 HSCT patients and applied to Illumina B ead C hips. Using sequential data analysis, we filtered extraneous SNP s, selected loci, and identified a predictive SNP network for OM risk. We then tested the predictive validity of the network using SNP array outputs from an independent HSCT cohort. Results We identified an 82‐ SNP B ayesian network ( BN ) that was related to SOM risk with a 10‐fold cross‐validation accuracy of 99.3% and an area under the ROC curve of 99.7%. Using samples from a small independent patient cohort ( n  = 16), we demonstrated the network's predictive validity with an accuracy of 81.2% in the absence of any false positives. Conclusions Our results suggest that SNP ‐based BN developed from saliva‐sourced DNA can predict SOM risk in patients prior to a HSCT .