ABSTRACT
Introduction: Human breath can contain thousands of volatile organic compounds (VOCs) and semi-volatile compounds that are related to metabolism and other biochemical processes. The presence of cancer cells can affect the identity and abundances of chemicals in breath when compared to those in healthy control subjects, which can be used to indicate the likelihood of a patient having cancer. Recently, the chemical analysis of exhaled breath from patients has been shown to be promising for diagnosing many different types of cancers, including lung, breast, colon, head, neck, and prostate, along with pre-cancerous conditions (dysplasia).
Areas covered: Here, we reviewed the sampling, analytical and data analysis methods reported in the recent patent literature related to cancer breath testing (2014–2017). In addition, the different types of cancer biomarkers that were disclosed are discussed.
Expert opinion: The major advantages of breath testing compared to conventional X-ray and imaging based methods includes simplicity of use, non-invasiveness, and the potential to detect cancer at a relatively early stage. Such methods are also suitable to perform population screening because of their non-invasiveness. However, the establishment of standard sampling, detection and quantification methods for breath testing is required before the methods can be employed for clinical diagnosis.
Article highlights
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This article reviews the key, recent patents published on the detection of cancer biomarkers from human breath for cancer diagnosis (2014-2017)
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Methods for sampling, detecting and analysing volatile organic compounds from exhaled breath are discussed
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The different types and classes of cancer biomarkers that have been disclosed in the recent patent literature are reviewed
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The current status and future prospects of breath testing for cancer is assessed
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Declaration of interest
W A Donald has received funds from NSW Smart Sensing Network, Australian Research Council, UNSW Sydney and KM Kabir has received funds from NSW Smart Sensing Network, and UNSW Sydney. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose