SNP-Based Bayesian Networks Can Predict Oral Mucositis Risk in Autologous Stem Cell Transplant Recipients

Abstract:
Objective: Approximately 40% of patients receiving conditioning chemotherapy prior to autologous hematopoietic stem cell transplants (aHSCT) 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 (SNPs) 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 BeadChips. Using sequential data analysis, we filtered extraneous SNPs, 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 Bayesian 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 aHSCT.

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