Urinary volatile fingerprint based on mass spectrometry for the discrimination of patients with lung cancer and controls.

Profile signals of urine samples corresponding to patients with lung cancer and controls were obtained using a non-separative methodology. The method is based on the coupling of a headspace sampler, a programed temperature vaporizer and a mass spectrometer (HS-PTV-MS). With only a centrifugation step as prior sample treatment, the samples were subjected to the headspace generation process and the volatiles generated were introduced into the PTV where they were trapped in the Tenax® packed liner while the solvent was purged. Finally, the analytes were introduced directly, without separation, into the mass spectrometer which allows obtaining the fingerprint of the analyzed sample. The mass spectrum corresponding to the mass/charge ratios (m/z) ranging between 35 and 120amu (amu) contains the information related to the composition of the headspace and is used as the analytical signal for the characterization of the samples. Samples of 14 patients with some type of cancer and 24 healthy volunteers were analyzed and the profile signals were subjected to different chemometric techniques, including support vector machines (SVM), linear discriminant analysis (LDA) and partial least squares- discriminant analysis (PLS-DA), with the aim of differentiating the samples of patients with cancer from those of control. Values of 100% were obtained both in sensitivity and specificity in most cases. This methodology has been used previously, as described later, for the analysis of the fingerprint corresponding to saliva samples of patients and controls. However, up to date, the method has not been used in urine samples with the aim of fast discrimination between patients with cancer and controls. The advantages and disadvantages of using urine versus other types of matrices such as saliva are stated. In view of the results obtained in this work, the use of pattern recognition techniques with data corresponding to HS-PTV-MS profile signals is highly suitable as a first screening step to differentiate samples. In addition, it could be applied to a high number of samples in a relatively short period of time due to its high throughput.

Headspace-programmed temperature vaporization-mass spectrometry for the rapid determination of possible volatile biomarkers of lung cancer in urine.

We propose a new method for the rapid determination of five volatile compounds described in the literature as possible biomarkers of lung cancer in urine samples. The method is based on the coupling of a headspace sampler, a programmed temperature vaporizer in solvent-vent injection mode, and a mass spectrometer (HS-PTV-MS). This configuration is known as an electronic nose based on mass spectrometry. Once the method was developed, it was used for the analysis of urine samples from lung cancer patients and healthy individuals. Multivariate calibration models were employed to quantify the biomarker concentrations in the samples. The detection limits ranged between 0.16 and 21 µg/L. For the assignment of the samples to the patient group or the healthy individuals, the Wilcoxon signed-rank test was used, comparing the concentrations obtained with the median of a reference set of healthy individuals. To date, this is the first time that multivariate calibration and non-parametric methods have been combined to classify biological samples from profile signals obtained with an electronic nose. When significant differences in the concentration of one or more biomarkers were found with respect to the reference set, the sample is considered as a positive one and a new analysis was performed using a chromatographic method (HS-PTV-GC/MS) to confirm the result. The main advantage of the proposed HS-PTV-MS methodology is that no prior chromatographic separation and no sample manipulation are required, which allows an increase of the number of samples analyzed per hour and restricts the use of time-consuming techniques to only when necessary. Graphical abstract Schematic diagram of the developed methodology.