Assessment, origin, and implementation of breath volatile cancer markers

Abstract

A new non-invasive and potentially inexpensive frontier in the diagnosis of cancer relies on the detection of volatile organic compounds (VOCs) in exhaled breath samples. Breath can be sampled and analyzed in real-time, leading to fascinating and cost-effective clinical diagnostic procedures. Nevertheless, breath analysis is a very young field of research and faces challenges, mainly because the biochemical mechanisms behind the cancer-related VOCs are largely unknown. In this review, we present a list of 115 validated cancer-related VOCs published in the literature during the past decade, and classify them with respect to their "fat-to-blood" and "blood-to-air" partition coefficients. These partition coefficients provide an estimation of the relative concentrations of VOCs in alveolar breath, in blood and in the fat compartments of the human body. Additionally, we try to clarify controversial issues concerning possible experimental malpractice in the field, and propose ways to translate the basic science results as well as the mechanistic understanding to tools (sensors) that could serve as point-of-care diagnostics of cancer. We end this review with a conclusion and a future perspective.

Figures

Fig. 1

Hypothetical basis of the breath test for lung cancer: lung cancer could result from the interaction of hereditary and environmental factors. Several cytochrome p450 mixed oxidases are activated by exposure to environmental toxins such as tobacco smoke. The induced phenotype may increase the risk of lung cancer by increased conversion of precursors to carcinogens. An altered pattern of cytochrome p450 mixed oxidase activity could potentially modulate catabolism of endogenous VOC products of oxidative stress and generate an altered pattern of breath VOCs. Reprinted from ref. .

Fig. 2

Simulation scheme of the two main thermodynamic parameters responsible for the diffusion of cancer VOCs between “breath–blood–fat”: λf:b – partition coefficient between fat and blood, which simulates the diffusion of VOC from the (cancer or healthy) tissue to the blood; and λb:a – partition coefficient between blood and air, which simulates the diffusion of VOC from the blood to the exhaled air.

Fig. 3

Estimated equilibrium concentrations in blood and fat for candidates of volatile cancer biomarkers published during the past decade.,,,,,,,,–,,,,, These equilibrium concentrations have been estimated under the assumption that the concentration in alveolar breath is 1 part-per-billion (ppb), based on the λb:a (partition coefficient between blood and air) and λf:a (partition coefficient between fat and blood) from Table 1. Hence, for different VOCs showing the same concentration in exhaled breath, the concentration in fat and blood may be very different (up to a factor of 108). Different VOCs, therefore, carry distinctive information on the various compartments of the human body. In the figure, various chemical classes of compounds (such as hydrocarbons or sulfides) are indicated by different symbols and colors.

Fig. 4

λb:a as a function of the VOCs from different types of cancer: (a) lung cancer; (b) breast cancer; (c) colon cance ; (d) liver cancer; (e) head and neck cancer; (f) gastric cancer. Data show that lung cancer, gastric cancer and liver cancer have rather similar values as can be seen from the median line. On the other hand, breast cancer and head and neck cancer are similar and colon cancer is different from the rest.

Fig. 5

Schematic illustration of the selective sensing approach versus the cross-reactive sensing approach. Reconstructed from ref. 39.

Fig. 6

Schematic illustration of different nanomaterial-based sensors: (a) chemiresistor based on monolayer-capped metal nanoparticles; (b) chemiresistor based on single-walled carbon nanotubes; (c) chemiresistor based on conducting polymers; (d) chemiresistor or chemicapacitor based on metal-oxide films; (e) quartz microbalance (QMB) with selective coating; (f) colorimetric sensor; and (g) surface acoustic wave (SAW) sensor. Reconstructed from ref. 39.

Fig. 7

Overview of the processes involved in breath testing: exhaled breath is a complex mixture of gases, water vapor, and thousands of VOCs in which only a small number of specific VOCs and gases comprise the clinically significant breath print. In order to perform the breath test, a sample is prepared from the complex mixture of exhaled breath by “trapping” the breath components on a sorbent material (followed by thermal desorption for their release), within a collection container (for example, a bag, vial, or canister), a dehumidification unit, or a channeling unit for direct delivery. The sample is then delivered to a measurement chamber through a simple delivery channel or a microfluidic system. In the measurement chamber, the breath components interact with the recognition element of the NMVS, inducing a measurable change (that is, electrical or optical) in the transducer that is translated into an output signal. Data analysis is then performed on the output signals in order to make the clinical prediction of the breath test. Reconstructed from ref. 138.

Fig. 8

Nanomaterial-based VOC sensors can be divided into sensors based on nanomaterial transducers (left column, a and c) or conventional transducers (right column, b and d), with the recognition elements being either semi-selective (upper row, a and b) or specific (lower row, c and d), with the latter types typically more sensitive than the former. (a) Top right: schematic of a Si-NW FET configuration functionalized and passivated with an organic self-assembled monolayer of hexyltrichlorosilane. Bottom right: optical micrograph of a Si-NW FET with an inset showing a TEM image of a representative Si-NW. Left: semi-selectivity of the device shown by the relative surface-state density change (Δns/ns0) as extracted from three different devices exposed to three different nonpolar VOCs (hexane, octane, and decane) at increasing concentrations. (b) Top: schematic of a QCM oscillator coated with a sensing layer of polyethyleneimine functionalized TiO2 (PEI–TiO2) nanoporous fibers. Bottom left: SEM image of a representative PEI–TiO2 nanoporous fiber. Right: responses of QCM-based PEI–TiO2 sensors on exposure to 20 ppmv formaldehyde. The inset shows the frequency shift of the sensor versus 20 ppmv of various VOCs demonstrating the increased selectivity (semi-selectivity) of the sensor towards formaldehyde. (c) Top: a computational modeling predicting the specific binding of TNT to a peptide–CNT hybrid through a H-bond with Trp17 of the peptide and π–π interaction with the CNT surface (as part of a SWCNT-FET sensor for TNT vapor). Bottom left and right: response of a bare and peptide-coated (respectively) CNT-FET sensor to vapors of TNT (red circles), RDX (blue triangles), and HPT (black squares) showing the specific response to TNT. The arrow indicates when the vapor is introduced into the device. (d) Top: schematic of the surface modification of a gold-coated cantilever end with multi-walled CNTs functionalized with TNT-specific AHFP molecules. Right: SEM image of amicro-cantilever sensor immobilized with multi-walled CNTs. The inset shows a magnification of the random network of immobilized CNTs. Bottom left: response of a surface modified cantilever sensor, with HFIP functionalized multi-walled CNTs, to various interfering gases (all in about 10 ppmv concentration) in comparison with its response to 4.6 ppbv TNT vapor and demonstrating the specific response to TNT. Reconstructed from ref. 138.

Fig. 9

Illustration showing the two main sensing approaches (specific vs. cross-reactive approaches) and how they can be coupled to the different types of VOC prints originating from different types of clinical states. When the detection of a single or a few target breath markers is required, maximal selectivity is required from the NMVSs, so a lock-and-key approach is most suitable. This approach is especially important for compounds that tend to appear in breath at low concentrations, such as unvolatile (high boiling point) compounds. If the targeted breath print is composed of many compounds or their identity is unknown, an array of more semi-selective NMVSs should be used. Such a setup is especially suitable for volatile (low boiling point) compounds that tend to appear at more elevated levels. Reconstructed from ref. 138.

Fig. 10

Means for tackling the implications of real-world confounding factors. (a) Top left: schematic diagram of a µ-preconcentrator chip that utilizes an array of solid-phase microextraction (SPME) needles coated with an in situ-grown carbon adsorbent film (as the sorbent material). Right: cross-section SEM image of an array of µ-SPME needles coated with the carbon film. Bottom left: schematic diagram of the heater and temperature sensors of the thermal desorption (TD) unit of the µ-preconcentrator chip. (b) A topographic plot of an ion mobility spectrometer (IMS) coupled to a multi-capillary column (MCC) from the breath of a patient suffering from lung infection. The plot shows on the bottom left hand side that the moisture of the breath sample is separated from the signals of the other breath components. The inset shows a micrograph of a transverse section of a MCC with ~1400 capillaries having a diameter of ~40 µm. (c) A comparison between the response patterns of an array of four gold-nanoparticle (Au-NP) based chemiresistors to clean moist air samples (blue and green closed circles) and air samples contaminated by ~40 ppm of 2-ethylhexanol before humidity compensation (left) and after humidity compensation (right). The plot shows the major improvement in the performance of the sensor array resulting from the humidity compensation procedure. (d) A schematic view of the different layers composing a CNT-FET sensor integrated with an embedded heating layer situated between the substrate and the dielectric layer, which is useful for reducing the recovery time of the sensor by desorbing the bound molecules more rapidly. (e) Left: a plot showing the major improvement in the stability of the sensitivity to toluene of Au-NP based sensors capped by trithiols instead of monothiols, which is explained to be a result of slower oxidation of the thiolate groups in the case of the trithiol capping layer. Right: a schematic drawing showing the differences between the trithiol capped Au-NPs (top) and the monothiol capped Au-NPs (bottom). (f) A plot of the sensitivity of three identically fabricated Au-NP based chemiresistors towards water vapor over a period of ~124 days, which shows that their sensitivity drastically drifts down over the first few weeks and stabilizes after an aging period of ~40 days. The inset shows the resistance response profiles of the three sensors that become almost identical towards the end of the experiment.