Freeze-drying: An alternative method for the analysis of volatile organic compounds in the headspace of urine samples using solid phase micro-extraction coupled to gas chromatography - mass spectrometry

© 2016 Aggio et al. Background: Volatile organic compounds (VOCs) can be intermediates of metabolic pathways and their levels in biological samples may provide a better understanding about diseases in addition to potential methods for diagnosis. Headspace analysis of VOCs in urine samples using solid phase micro extraction (SPME) coupled to gas chromatography - mass spectrometry (GC-MS) is one of the most used techniques. However, it generally produces a limited profile of VOCs if applied to fresh urine. Sample preparation methods, such as addition of salt, base or acid, have been developed to improve the headspace-SPME-GC-MS analysis of VOCs in urine samples. These methods result in a richer profile of VOCs, however, they may also add potential contaminants to the urine samples, result in increased variability introduced by manually processing the samples and promote degradation of metabolites due to extreme pH levels. Here, we evaluated if freeze-drying can be considered an alternative sample preparation method for headspace-SPME-GC-MS analysis of urine samples. Results: We collected urine from three volunteers and compared the performances of freeze-drying, addition of acid (HCl), addition of base (NaOH), addition of salt (NaCl), fresh urine and frozen urine when identifying and quantifying metabolites in 4 ml samples. Freeze-drying and addition of acid produced a significantly higher number of VOCs identified than any other method, with freeze-drying covering a slightly higher number of chemical classes, showing an improved repeatability and reducing siloxane impurities. Conclusion: In this work we compared the performance of sample preparation methods for the SPME-GC-MS analysis of urine samples. To the best of our knowledge, this is the first study evaluating the potential of freeze-dry as an alternative sample preparation method. Our results indicate that freeze-drying has potential to be used as an alternative method for the SPME-GC-MS analysis of urine samples. Additional studies using internal standard, synthetic urine and calibration curves will allow a more precise quantification of metabolites and additional comparisons between methods

Urinary volatile organic compounds for the detection of prostate cancer

© 2015 Khalid et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The aim of this work was to investigate volatile organic compounds (VOCs) emanating from urine samples to determine whether they can be used to classify samples into those from prostate cancer and non-cancer groups. Participants were men referred for a trans-rectal ultrasound-guided prostate biopsy because of an elevated prostate specific antigen (PSA) level or abnormal findings on digital rectal examination. Urine samples were collected from patients with prostate cancer (n = 59) and cancer-free controls (n = 43), on the day of their biopsy, prior to their procedure. VOCs from the headspace of basified urine samples were extracted using solid-phase micro-extraction and analysed by gas chromatography/mass spectrometry. Classifiers were developed using Random Forest (RF) and Linear Discriminant Analysis (LDA) classification techniques. PSA alone had an accuracy of 62-64% in these samples. A model based on 4 VOCs, 2,6-dimethyl-7-octen-2-ol, pentanal, 3-octanone, and 2-octanone, was marginally more accurate 63-65%. When combined, PSA level and these four VOCs had mean accuracies of 74% and 65%, using RF and LDA, respectively. With repeated double cross-validation, the mean accuracies fell to 71% and 65%, using RF and LDA, respectively. Results from VOC profiling of urine headspace are encouraging and suggest that there are other metabolomic avenues worth exploring which could help improve the stratification of men at risk of prostate cancer. This study also adds to our knowledge on the profile of compounds found in basified urine, from controls and cancer patients, which is useful information for future studies comparing the urine from patients with other disease states

The use of a gas chromatography-sensor system combined with advanced statistical methods, towards the diagnosis of urological malignancies

© 2016 IOP Publishing Ltd. Prostate cancer is one of the most common cancers. Serum prostate-specific antigen (PSA) is used to aid the selection of men undergoing biopsies. Its use remains controversial. We propose a GC-sensor algorithm system for classifying urine samples from patients with urological symptoms. This pilot study includes 155 men presenting to urology clinics, 58 were diagnosed with prostate cancer, 24 with bladder cancer and 73 with haematuria and or poor stream, without cancer. Principal component analysis (PCA) was applied to assess the discrimination achieved, while linear discriminant analysis (LDA) and support vector machine (SVM) were used as statistical models for sample classification. Leave-one-out cross-validation (LOOCV), repeated 10-fold cross-validation (10FoldCV), repeated double cross-validation (DoubleCV) and Monte Carlo permutations were applied to assess performance. Significant separation was found between prostate cancer and control samples, bladder cancer and controls and between bladder and prostate cancer samples. For prostate cancer diagnosis, the GC/SVM system classified samples with 95% sensitivity and 96% specificity after LOOCV. For bladder cancer diagnosis, the SVM reported 96% sensitivity and 100% specificity after LOOCV, while the DoubleCV reported 87% sensitivity and 99% specificity, with SVM showing 78% and 98% sensitivity between prostate and bladder cancer samples. Evaluation of the results of the Monte Carlo permutation of class labels obtained chance-like accuracy values around 50% suggesting the observed results for bladder cancer and prostate cancer detection are not due to over fitting. The results of the pilot study presented here indicate that the GC system is able to successfully identify patterns that allow classification of urine samples from patients with urological cancers. An accurate diagnosis based on urine samples would reduce the number of negative prostate biopsies performed, and the frequency of surveillance cystoscopy for bladder cancer patients. Larger cohort studies are planned to investigate the potential of this system. Future work may lead to non-invasive breath analyses for diagnosing urological conditions