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Salivary markers and risk factor data: a multivariate modeling approach for head and neck squamous cell carcinoma
Lutécia H Mateus Pereira1, Islamiyat Nancy Adebisi, Aymee Perez
1Sylvester Comprehensive Cancer Center, University of Miami Leonard Miller School of Medicine, Miami, FL 33136, USA.
Background:
Head and neck squamous cell carcinoma (HNSCC) is a debilitating and deadly disease largely due to late stage diagnosis. Prior work indicates that soluble CD44 (solCD44) and total protein may be useful diagnostic markers for HNSCC. In this study we combine the markers solCD44, IL-8, HA, and total protein with demographic and risk factor data to derive a multivariate logistic model that improves HNSCC detection as compared to our previous data using biomarkers alone.
Methods:
We performed the solCD44, IL-8, HA, and total protein assays on oral rinses from 40 HNSCC patients and 39 controls using ELISA assays. Controls had benign diseases of the upper aerodigestive tract and a history of tobacco or alcohol use. All subjects completed a questionnaire including demographic and risk factor data.
Results:
Depending on cancer subsite, differences between cases and controls were found for all markers. A multivariate logistic model including solCD44, total protein and variables related to smoking, oral health and education offered a significant improvement over the univariate models with an AUC of 0.853. Sensitivity ranged from 75-82.5% and specificity from 69.2-82.1% depending on predictive probability cut points.
Conclusion:
A multivariate model, including simple and inexpensive molecular tests in combination with risk factors, results in a promising tool for distinguishing HNSCC patients from controls.
Impact:
In this case-control study, the resulting observations led to an unprecedented multivariate model that distinguished HNSCC cases from controls with better accuracy than the current gold standard which includes oral examination followed by tissue biopsy. Since the components are simple, noninvasive, and inexpensive to obtain, this model combining biomarkers, risk factor and demographic data serves as a promising prototype for future cancer detection tests.
