| Title: | Prediction of breast cancer risk with volatile biomarkers in breath |
| Author(s): | Phillips M; Cataneo RN; Cruz-Ramos JA; Huston J; Ornelas O; Pappas N; Pathak S; |
| Address: | "Breath Research Laboratory, Menssana Research Inc, 211 Warren St, Newark, NJ, 07103, USA. mphillips@menssanaresearch.com. Department of Medicine, New York Medical College, Valhalla, NY, USA. mphillips@menssanaresearch.com. Breath Research Laboratory, Menssana Research Inc, 211 Warren St, Newark, NJ, 07103, USA. Universidad de Guadalajara & Instituto Jalisciense de Cancerologia, 44280, Guadalajara, Mexico. Formerly Hackensack UMC Mountainside, Montclair, NJ, USA. Grupo Mexlab, Paseos Del Sol, 45070, Zapopan, Jalisco, Mexico. Saint Michael's Medical Center, Newark, NJ, USA" |
| DOI: | 10.1007/s10549-018-4764-4 |
| ISSN/ISBN: | 1573-7217 (Electronic) 0167-6806 (Linking) |
| Abstract: | "BACKGROUND: Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk. METHODS: We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV. RESULTS: Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%. CONCLUSION: Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer" |
| Keywords: | Age Factors Algorithms *Biomarkers Biopsy Breast Neoplasms/*diagnosis/epidemiology/*metabolism Female Gas Chromatography-Mass Spectrometry Humans Prognosis ROC Curve Reproducibility of Results Risk Assessment Risk Factors Sensitivity and Specificity Volat; |
| Notes: | "MedlinePhillips, Michael Cataneo, Renee N Cruz-Ramos, Jose Alfonso Huston, Jan Ornelas, Omar Pappas, Nadine Pathak, Sonali eng Grant Number: 5R44CA203019 - 02/National Institutes of Health/ Netherlands 2018/03/24 Breast Cancer Res Treat. 2018 Jul; 170(2):343-350. doi: 10.1007/s10549-018-4764-4. Epub 2018 Mar 23" |
