The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications

Abstract

Background: Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids.

Aim of review: This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted.

Key scientific concepts of review: We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Keywords: Biofluids; GC-MS; Oncological diseases; Sensors; VOMs.