| Title: | Screening of Breast Cancer from Sweat Samples Analyzed by 2-Dimensional Gas Chromatography-Mass Spectrometry: A Preliminary Study |
| Author(s): | Leemans M; Cuzuel V; Bauer P; Baba Aissa H; Cournelle G; Baelde A; Thuleau A; Cognon G; Pouget N; Guillot E; Fromantin I; Audureau E; |
| Address: | "Clinical Epidemiology and Ageing Unit, Institut Mondor de Recherche Biomedicale, Paris-Est University, 94010 Creteil, France. Forensic Institute of the French Gendarmerie, Caserne Lange, 5 Boulevard de l'Hautil, Cedex, 95001 Cergy-Pontoise, France. Wound Care and Research Unit 26, Curie Institute, Rue d'Ulm, 75005 Paris, France. Baelde & Cournelle Analytics, 130 Allee Reysa Bernson, 59800 Lille, France. Department of Surgical Oncology, Curie Institute, 35 Rue Dailly, 92210 Saint-Cloud, France. Public Health Department, Henri-Mondor Hospital, Assistance Publique des Hopitaux de Paris, 94010 Creteil, France" |
| ISSN/ISBN: | 2072-6694 (Print) 2072-6694 (Electronic) 2072-6694 (Linking) |
| Abstract: | "Breast cancer (BC) remains one of the most commonly diagnosed malignancies in women. There is increasing interest in the development of non-invasive screening methods. Volatile organic compounds (VOCs) emitted through the metabolism of cancer cells are possible novel cancer biomarkers. This study aims to identify the existence of BC-specific VOCs in the sweat of BC patients. Sweat samples from the breast and hand area were collected from 21 BC participants before and after breast tumor ablation. Thermal desorption coupled with two-dimensional gas chromatography and mass spectrometry was used to analyze VOCs. A total of 761 volatiles from a homemade human odor library were screened on each chromatogram. From those 761 VOCs, a minimum of 77 VOCs were detected within the BC samples. Principal component analysis showed that VOCs differ between the pre- and post-surgery status of the BC patients. The Tree-based Pipeline Optimization Tool identified logistic regression as the best-performing machine learning model. Logistic regression modeling identified VOCs that distinguish the pre-and post-surgery state in BC patients on both the breast and hand area with sensitivities close to 1. Further, Shapley additive explanations and the probe variable method identified the most important and pertinent VOCs distinguishing pre- and post-operative status which are mostly of distinct origin for the hand and breast region. Results suggest the possibility to identify endogenous metabolites linked to BC, hence proposing this innovative pipeline as a stepstone to discovering potential BC biomarkers. Large-scale studies in a multi-centered VOC analysis setting must be carried out to validate obtained findings" |
| Keywords: | breast cancer chromatography machine learning mass spectrometry screening sweat; |
| Notes: | "PubMed-not-MEDLINELeemans, Michelle Cuzuel, Vincent Bauer, Pierre Baba Aissa, Hind Cournelle, Gabriel Baelde, Aurelien Thuleau, Aurelie Cognon, Guillaume Pouget, Nicolas Guillot, Eugenie Fromantin, Isabelle Audureau, Etienne eng KDOG project, 2021 project cycle/Royal Canin/ Switzerland 2023/06/10 Cancers (Basel). 2023 May 26; 15(11):2939. doi: 10.3390/cancers15112939" |
