Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors. Issue 7 (26th April 2020)

• Record Type: Journal Article
• Authors: Miller‐Atkins, Galen; Acevedo‐Moreno, Lou‐Anne; Grove, David; Dweik, Raed A.; Tonelli, Adriano R.; Brown, J. Mark; Allende, Daniela S.; Aucejo, Federico; Rotroff, Daniel M.
• Is Part Of: Hepatology communications. Volume 4:Issue 7(2020)
• Page Start: 1041
• Page End: 1055
• Publication Date: 2020-04-26
• Subjects: Hepatology -- Periodicals; Liver -- Diseases -- Periodicals; Liver Diseases; Gastroenterology; Periodicals; Fulltext; Internet Resources; Periodicals; 616.36
• DOI: 10.1002/hep4.1499
• Languages: English
• ISSNs: 2471-254X

Abstract

Abstract : Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer‐related morality. Current biomarkers, such as alpha‐fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors. Abstract : We identified a signature of volatile organic compounds in breath that accurately distinguishes patients with no liver disease, cirrhosis, pulmonary hypertension, hepatocellular carcinoma, or colorectal liver metastasis.