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]).

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.

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 (HS-SPME GC-MS). 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.

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.