Methods to Detect Volatile Organic Compounds for Breath Biopsy Using Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry.

Volatile organic compounds (VOCs) are byproducts from metabolic pathways that can be detected in exhaled breath and have been reported as biomarkers for different diseases. The gold standard for analysis is gas chromatography-mass spectrometry (GC-MS), which can be coupled with various sampling methods. The current study aims to develop and compare different methods for sampling and preconcentrating VOCs using solid-phase microextraction (SPME). An in-house sampling method, direct-breath SPME (DB-SPME), was developed to directly extract VOCs from breath using a SPME fiber. The method was optimized by exploring different SPME types, the overall exhalation volume, and breath fractionation. DB-SPME was quantitatively compared to two alternative methods involving the collection of breath in a Tedlar bag. In one method, VOCs were directly extracted from the Tedlar bag (Tedlar-SPME) and in the other, the VOCs were cryothermally transferred from the Tedlar bag to a headspace vial (cryotransfer). The methods were verified and quantitatively compared using breath samples (n = 15 for each method respectively) analyzed by GC-MS quadrupole time-of-flight (QTOF) for compounds including but not limited to acetone, isoprene, toluene, limonene, and pinene. The cryotransfer method was the most sensitive, demonstrating the strongest signal for the majority of the VOCs detected in the exhaled breath samples. However, VOCs with low molecular weights, including acetone and isoprene, were detected with the highest sensitivity using the Tedlar-SPME. On the other hand, the DB-SPME was less sensitive, although it was rapid and had the lowest background GC-MS signal. Overall, the three breath-sampling methods can detect a wide variety of VOCs in breath. The cryotransfer method may be optimal when collecting a large number of samples using Tedlar bags, as it allows the long-term storage of VOCs at low temperatures (-80 °C), while Tedlar-SPME may be more effective when targeting relatively small VOCs. The DB-SPME method may be the most efficient when more immediate analyses and results are required.

Chemometric Analysis of Urinary Volatile Organic Compounds to Monitor the Efficacy of Pitavastatin Treatments on Mammary Tumor Progression over Time.

Volatile organic compounds (VOCs) in urine are potential biomarkers of breast cancer. Previously, our group has investigated breast cancer through analysis of VOCs in mouse urine and identified a panel of VOCs with the ability to monitor tumor progression. However, an unanswered question is whether VOCs can be exploited similarly to monitor the efficacy of antitumor treatments over time. Herein, subsets of tumor-bearing mice were treated with pitavastatin at high (8 mg/kg) and low (4 mg/kg) concentrations, and urine was analyzed through solid-phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Previous investigations using X-ray and micro-CT analysis indicated pitavastatin administered at 8 mg/kg had a protective effect against mammary tumors, whereas 4 mg/kg treatments did not inhibit tumor-induced damage. VOCs from mice treated with pitavastatin were compared to the previously analyzed healthy controls and tumor-bearing mice using chemometric analyses, which revealed that mice treated with pitavastatin at high concentrations were significantly different than tumor-bearing untreated mice in the direction of healthy controls. Mice treated with low concentrations demonstrated significant differences relative to healthy controls and were reflective of tumor-bearing untreated mice. These results show that urinary VOCs can accurately and noninvasively predict the efficacy of pitavastatin treatments over time.

Loading-induced antitumor capability of murine and human urine.

While urine has been considered as a useful bio-fluid for health monitoring, its dynamic changes to physical activity are not well understood. We examined urine's possible antitumor capability in response to medium-level, loading-driven physical activity. Urine was collected from mice subjected to 5-minute skeletal loading and human individuals before and after 30-minute step aerobics. Six cancer cell lines (breast, prostate, and pancreas) and a mouse model of the mammary tumor were employed to evaluate the effect of urine. Compared to urine collected prior to loading, urine collected post-activity decreased the cellular viability, proliferation, migration, and invasion of tumor cells, as well as tumor weight in the mammary fat pad. Detection of urinary volatile organic compounds and ELISA assays showed that the loading-conditioned urine reduced cholesterol and elevated dopamine and melatonin. Immunohistochemical fluorescent images presented upregulation of the rate-limiting enzymes for the production of dopamine and melatonin in the brain. Molecular analysis revealed that the antitumor effect was linked to the reduction in molecular vinculin-linked molecular force as well as the downregulation of the Lrp5-CSF1-CD105 regulatory axis. Notably, the survival rate for the high expression levels of Lrp5, CSF1, and CD105 in tumor tissues was significantly lowered in the Cancer Genome Atlas database. Collectively, this study revealed that 5- or 10-minute loading-driven physical activity was sufficient to induce the striking antitumor effect by activating the neuronal signaling and repressing cholesterol synthesis. The result supported the dual role of loading-conditioned urine as a potential tumor suppressor and a source of diagnostic biomarkers.

Urinary Volatile Terpenes Analyzed by Gas Chromatography-Mass Spectrometry to Monitor Breast Cancer Treatment Efficacy in Mice.

Urinary volatile terpene (VT) levels are significantly altered with induced models of breast cancer in mice. The question arises whether VTs can detect the efficacy of antitumor treatments. BALB/c mice were injected with 4T1.2 murine tumor cells in the mammary pad or iliac artery to model localized breast cancer and induced bone metastasis. The effect of two dopaminergic antitumor agents was tested by conventional histology and altered VT levels. The headspace of urine specimens was analyzed by gas chromatography-mass spectrometry. In the localized model, the statistical significance (p < 0.05) was identified for 26% of VTs, and in the metastasis model, 19% of VTs. The authors discovered separate VT panels classifying localized/control [area under the curve (AUC) = 1.0] and metastasis/control (AUC = 0.98). Treatment samples were tested using these panels, which showed that mice treated with either agent were statistically significantly different from cancer samples, which is consistent with conventional analysis.

Quantifying exhaled acetone and isoprene through solid phase microextraction and gas chromatography-mass spectrometry.

BACKGROUND: Acetone, isoprene, and other volatile organic compounds (VOCs) in exhaled breath have been shown to be biomarkers for many medical conditions. Researchers use different techniques for VOC detection, including solid phase microextraction (SPME), to preconcentrate volatile analytes prior to instrumental analysis by gas chromatography-mass spectrometry (GC-MS). These techniques include a previously developed method to detect VOCs in breath directly using SPME, but it is uncommon for studies to quantify exhaled volatiles because it can be time consuming due to the need of many external/internal standards, and there is no standardized or widely accepted method. The objective of this study was to develop an accessible method to quantify acetone and isoprene in breath by SPME GC-MS. RESULTS: A system was developed to mimic human exhalation and expose VOCs to a SPME fiber in the gas phase at known concentrations. VOCs were bubbled/diluted with dry air at a fixed flow rate, duration, and volume that was comparable to a previously developed breath sampling method. Identification of acetone and isoprene through GC-MS was verified using standards and observing overlaps in chromatographic retention/mass spectral fragmentation. Calibration curves were developed for these two analytes, which showed a high degree of linear correlation. Acetone and isoprene displayed limits of detection/quantification equal to 12 ppb/37 ppb and 73 ppb/222 ppb respectively. Quantification results in healthy breath samples (n = 15) showed acetone concentrations spanned between 71 ppb and 294 ppb, and isoprene varied between 170 ppb and 990 ppb. Both concentration ranges for acetone and isoprene in this study overlap with those reported in existing literature. SIGNIFICANCE: Results indicate the development of a system to quantify acetone and isoprene in breath that can be adapted to diverse sampling methods and instrumental analyses beyond SPME GC-MS.

Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men.

Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.

Exhaled VOCs can discriminate subjects with COVID-19 from healthy controls.

COVID-19 detection currently relies on testing by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing. However, SARS-CoV-2 is expected to cause significant metabolic changes in infected subjects due to both metabolic requirements for rapid viral replication and host immune responses. Analysis of volatile organic compounds (VOCs) from human breath can detect these metabolic changes and is therefore an alternative to RT-PCR or antigen assays. To identify VOC biomarkers of COVID-19, exhaled breath samples were collected from two sample groups into Tedlar bags: negative COVID-19 (n= 12) and positive COVID-19 symptomatic (n= 14). Next, VOCs were analyzed by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry. Subjects with COVID-19 displayed a larger number of VOCs as well as overall higher total concentration of VOCs (p< 0.05). Univariate analyses of qualified endogenous VOCs showed approximately 18% of the VOCs were significantly differentially expressed between the two classes (p< 0.05), with most VOCs upregulated. Machine learning multivariate classification algorithms distinguished COVID-19 subjects with over 95% accuracy. The COVID-19 positive subjects could be differentiated into two distinct subgroups by machine learning classification, but these did not correspond with significant differences in number of symptoms. Next, samples were collected from subjects who had previously donated breath bags while experiencing COVID-19, and subsequently recovered (COVID Recovered subjects (n= 11)). Univariate and multivariate results showed >90% accuracy at identifying these new samples as Control (COVID-19 negative), thereby validating the classification model and demonstrating VOCs dysregulated by COVID are restored to baseline levels upon recovery.

Evaluating polyvinylidene fluoride - carbon black composites as solid phase microextraction coatings for the detection of urinary volatile organic compounds by gas chromatography-mass spectrometry.

Volatile organic compounds (VOCs) are biomarkers of disease, which can be utilized for accurate diagnostics. The gold standard for VOC identification is gas chromatography-mass spectrometry (GC-MS) as it allows for structure elucidation and quantification. Headspace solid phase microextraction (HS-SPME) is often used in biomarker discovery due to its ability to preconcentrate VOCs prior to GC-MS analysis. However, HS-SPME GC-MS is time-consuming, expensive and requires trained personnel. Gas sensor arrays can detect VOC biomarkers at a point-of-care and therefore are more suitable for disease diagnostics in the clinic. Nevertheless, qualification and optimization of sensing layers is tedious as each VOC of interest needs to be tested individually. Therefore, using SPME fibers to extract VOCs and GC-MS to quantitate the analytes may be an efficient strategy with high throughput to tune sensing layers and increase analyte affinity. To investigate this, suspensions of polyvinylidene fluoride (PVDF) and polyvinylidene fluoride-carbon black (PVDF-CB) fabricated at varying concentration were immobilized on SPME fibers through physical deposition, used to extract urinary VOCs and were subject to GC-MS analysis. The addition of CB shows increased fiber performance in terms of total integrated signal and sensitivity toward individual VOCs. PVDF-CB fibers were compared to a commercial polydimethylsiloxane (PDMS) SPME fiber run using the same method. The PVDF-CB fiber outperformed the commercial fiber in detecting numerous urinary VOCs of interest. Results of this study show not only that custom SPME fiber performance can be evaluated through GC-MS analysis, but the capability of custom fibers to adsorb urinary VOCs can be tuned based on properties of interest. Hence, this method may be utilized as an analytical tool to characterize and tune gas sensing layers with high analytical throughput.

Tracking the Progression of Triple Negative Mammary Tumors over Time by Chemometric Analysis of Urinary Volatile Organic Compounds.

Previous studies have shown that volatile organic compounds (VOCs) are potential biomarkers of breast cancer. An unanswered question is how urinary VOCs change over time as tumors progress. To explore this, BALB/c mice were injected with 4T1.2 triple negative murine tumor cells in the tibia. This typically causes tumor progression and osteolysis in 1-2 weeks. Samples were collected prior to tumor injection and from days 2-19. Samples were analyzed by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry. Univariate analysis identified VOCs that were biomarkers for breast cancer; some of these varied significantly over time and others did not. Principal component analysis was used to distinguish Cancer (all Weeks) from Control and Cancer Week 1 from Cancer Week 3 with over 90% accuracy. Forward feature selection and linear discriminant analysis identified a unique panel that could identify tumor presence with 94% accuracy and distinguish progression (Cancer Week 1 from Cancer Week 3) with 97% accuracy. Principal component regression analysis also demonstrated that a VOC panel could predict number of days since tumor injection (R2 = 0.71 and adjusted R2 = 0.63). VOC biomarkers identified by these analyses were associated with metabolic pathways relevant to breast cancer.

Mechanical tibial loading remotely suppresses brain tumors by dopamine-mediated downregulation of CCN4.

Mechanical loading to the bone is known to be beneficial for bone homeostasis and for suppressing tumor-induced osteolysis in the loaded bone. However, whether loading to a weight-bearing hind limb can inhibit distant tumor growth in the brain is unknown. We examined the possibility of bone-to-brain mechanotransduction using a mouse model of a brain tumor by focusing on the response to Lrp5-mediated Wnt signaling and dopamine in tumor cells. The results revealed that loading the tibia with elevated levels of tyrosine hydroxylase, a rate-limiting enzyme in dopamine synthesis, markedly reduced the progression of the brain tumors. The simultaneous application of fluphenazine (FP), an antipsychotic dopamine modulator, enhanced tumor suppression. Dopamine and FP exerted antitumor effects through the dopamine receptors DRD1 and DRD2, respectively. Notably, dopamine downregulated Lrp5 via DRD1 in tumor cells. A cytokine array analysis revealed that the reduction in CCN4 was critical for loading-driven, dopamine-mediated tumor suppression. The silencing of Lrp5 reduced CCN4, and the administration of CCN4 elevated oncogenic genes such as MMP9, Runx2, and Snail. In summary, this study demonstrates that mechanical loading regulates dopaminergic signaling and remotely suppresses brain tumors by inhibiting the Lrp5-CCN4 axis via DRD1, indicating the possibility of developing an adjuvant bone-mediated loading therapy.

Detection of Volatile Organic Compounds (VOCs) in Urine via Gas Chromatography-Mass Spectrometry QTOF to Differentiate Between Localized and Metastatic Models of Breast Cancer.

Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).