Prediction of clinical outcomes using the pyrolysis, gas chromatography, and differential mobility spectrometry (Py-GC-DMS) system

Biological and molecular heterogeneity of human diseases especially cancers contributes to variations in treatment response, clinical outcome, and survival. The addition of new disease- and condition-specific biomarkers to existing clinical markers to track cancer heterogeneity provides possibilities for further assisting clinicians in predicting clinical outcomes and making choices of treatment options. Ionization patterns derived from biological specimens can be adapted for use with existing clinical markers for early detection, patient risk stratification, treatment decision making, and monitoring disease progression. In order to demonstrate the application of pyrolysis, gas chromatography, and differential mobility spectrometry (Py-GC-DMS) for human diseases to predict the outcome of diseases, we analyzed the ionized spectral signals generated by instrument ACB2000 (ACBirox universal detector 2000, ACBirox LLC, NJ, USA) from the serum samples of Mantle Cell Lymphoma (MCL) patients. Here, we have used mantle cell lymphoma as a disease model for a conceptual study only and based on the ionization patterns of the analyzed serum samples, we developed a multivariate algorithm comprised of variable selection and reduction steps followed by receiver operating characteristic curve (ROC) analysis to predict the probability of a good or poor clinical outcome as a means of estimating the likely success of a particular treatment option. Our preliminary study performed with small cohort provides a proof of concept demonstrating the ability of this system to predict the clinical outcome for human diseases with high accuracy suggesting the promising application of pyrolysis, gas chromatography, and differential mobility spectrometry (Py-GC-DMS) in the field of medicine.