The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer
Guruduth Banavar, Oyetunji Ogundijo, Ryan Toma, Sathyapriya Rajagopal, Yen Kai Lim, Kai Dun Tang, Francine R. Camacho, Pedro J. Torres, Stephanie Gline, Matthew Parks, Liz Kenny, Ally Perlina, Hal Tily, Nevenka Dimitrova, Salomon Amar, Momchilo Vuyisich, Chamindie Punyadeera
Abstract
Despite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.