Abstract
Over 95% of head and neck cancers are squamous cell carcinoma (HNSCC). HNSCC is mostly diagnosed late, causing a poor prognosis despite the application of invasive treatment protocols. Tumor-educated platelets (TEPs) have been shown to hold promise as a molecular tool for early cancer diagnosis. We sequenced platelet mRNA isolated from blood of 101 patients with HNSCC and 101 propensity-score matched noncancer controls. Two independent machine learning classification strategies were employed using a training and validation approach to identify a cancer predictor: a particle swarm optimized support vector machine (PSO-SVM) and a least absolute shrinkage and selection operator (LASSO) logistic regression model. The best performing PSO-SVM predictor consisted of 245 platelet transcripts and reached a maximum area under the curve (AUC) of 0.87. For the LASSO-based prediction model, 1,198 mRNAs were selected, resulting in a median AUC of 0.84, independent of HPV status. Our data show that TEP RNA classification by different AI tools is promising in the diagnosis of HNSCC.
Authors
Niles E. Wondergem, Jos B. Poell, Sjors G.J.G. In ‘t Veld, Edward Post, Steven W. Mes, Myron G. Best, Wessel N. van Wieringen, Thomas Klausch, Robert J. Baatenburg de Jong, Chris H.J. Terhaard, Robert P. Takes, Johannes A. Langendijk, Irma M. Verdonck-de Leeuw, Femke Lamers, C. René Leemans, Elisabeth Bloemena, Thomas Würdinger, Ruud H. Brakenhoff
Figure 1
Performance of learned SVMs.
