Final Report Summary - CMPTIMSMS (Cancer metabolic profiling through ion mobility and mass spectrometric-based methods)
Along with oxygen/carbon dioxide exchange, exhaled breath contains relatively small biomolecules (i.e. metabolites) at minute concentrations. This biological information was exploited by medical doctors in the ancient Greece and also in China as a diagnostic tool. At that time, the physician nostrils’ were just one more element to determine the root of some health problems by using the sense of smell. For example, liver disease was often determined by a “rank odor”. Moreover, some studies carried out with trained animals with an extraordinary sense of smell (e.g. dogs); suggest that some diseases (e.g. lung cancer) can be diagnosed by simply sniffing out breath of patients.
However, the relevant biochemical information exhaled in breath is no longer used by clinicians nowadays. We, and a number of research groups, are developing new analytical tools to capture as many exhaled metabolites as possible. The potential of these new prototype analytical platforms is afterwards explored in various clinical settings to address different clinical problems (e.g. disease early diagnosis).
During the execution of this project we have worked under the hypothesis that the analysis of breath holds promise to address clinical dilemmas in a totally non-invasive manner. The technique used throughout the execution of this project is termed secondary electrospray-mass spectrometry (SESI-MS). One main advantage of this technique is that it provides instantaneous (i.e. real-time) metabolic breath fingerprints.
During a first phase of the project we explored important issues as for example metabolic variability across healthy subjects and time-of-day sampling. We found that, consistently with previous studies analyzing other biofluids, a stable individual-specific breath signature exists. This result suggests that breath analysis may support current efforts to provide individualized treatments. For example, one could envisage the construction of an individualized breathprint map to monitor disease progression and the (side) effects of therapy.
In an extension of these preliminary investigations with healthy subjects, we sampled the breath of three volunteers in hourly intervals for 24 hours during total sleep deprivation, and found features in the breath of all individuals which showed significant circadian variation. Our data suggest that real-time mass spectrometric breathprinting has high potential to become a useful tool to understand circadian metabolism, and develop new biomarkers to easily and in real-time assess circadian clock phase and function in experimental and clinical settings.
In a second phase, once human (breath) variability was fully understood, we studied the difference in breathprints between cohorts of healthy controls and patients suffering from chronic obstructive pulmonary disease (COPD). We studied 25 COPD patients, 25 non-smoking controls and 11 smoking controls. We analyzed their breath by mass spectrometry and statistically analyzed the resulting data. We identified a panel of discriminating mass spectral features useful to discriminate COPD vs controls. We concluded that this technique may be valuable to support COPD diagnosis and to gain insight into the disease. We are currently validating these observations with a larger independent cohort. Similarly, we are using this approach to investigate other lung diseases. For example, we have succeeded in using SESI-MS technology to diagnose sleep apnea from the simple exhalation of awake patients.
Another interesting area of application is the detection of drugs and its metabolites. During the execution of this project we have successfully adapted our system to analyze the breath from small animals (e.g. mice). In a proof-of-principle study we determined real-time pharmacokinetic profiles of injected substances. This method may be of value to screen drugs rapidly without a need of sacrificing animals.
Similarly, we have used this technique to compare the volatile metabolic signature of human breast cancer cell lines versus normal human mammary cells. We found significant differences and we therefore concluded that cancerous cells can release a characteristic odor whose constituents may be used as disease markers.
All in all, this project has brought SESI-MS technique to the next level, by showing proof-of-principle of applicability in different clinical scenarios.
However, the relevant biochemical information exhaled in breath is no longer used by clinicians nowadays. We, and a number of research groups, are developing new analytical tools to capture as many exhaled metabolites as possible. The potential of these new prototype analytical platforms is afterwards explored in various clinical settings to address different clinical problems (e.g. disease early diagnosis).
During the execution of this project we have worked under the hypothesis that the analysis of breath holds promise to address clinical dilemmas in a totally non-invasive manner. The technique used throughout the execution of this project is termed secondary electrospray-mass spectrometry (SESI-MS). One main advantage of this technique is that it provides instantaneous (i.e. real-time) metabolic breath fingerprints.
During a first phase of the project we explored important issues as for example metabolic variability across healthy subjects and time-of-day sampling. We found that, consistently with previous studies analyzing other biofluids, a stable individual-specific breath signature exists. This result suggests that breath analysis may support current efforts to provide individualized treatments. For example, one could envisage the construction of an individualized breathprint map to monitor disease progression and the (side) effects of therapy.
In an extension of these preliminary investigations with healthy subjects, we sampled the breath of three volunteers in hourly intervals for 24 hours during total sleep deprivation, and found features in the breath of all individuals which showed significant circadian variation. Our data suggest that real-time mass spectrometric breathprinting has high potential to become a useful tool to understand circadian metabolism, and develop new biomarkers to easily and in real-time assess circadian clock phase and function in experimental and clinical settings.
In a second phase, once human (breath) variability was fully understood, we studied the difference in breathprints between cohorts of healthy controls and patients suffering from chronic obstructive pulmonary disease (COPD). We studied 25 COPD patients, 25 non-smoking controls and 11 smoking controls. We analyzed their breath by mass spectrometry and statistically analyzed the resulting data. We identified a panel of discriminating mass spectral features useful to discriminate COPD vs controls. We concluded that this technique may be valuable to support COPD diagnosis and to gain insight into the disease. We are currently validating these observations with a larger independent cohort. Similarly, we are using this approach to investigate other lung diseases. For example, we have succeeded in using SESI-MS technology to diagnose sleep apnea from the simple exhalation of awake patients.
Another interesting area of application is the detection of drugs and its metabolites. During the execution of this project we have successfully adapted our system to analyze the breath from small animals (e.g. mice). In a proof-of-principle study we determined real-time pharmacokinetic profiles of injected substances. This method may be of value to screen drugs rapidly without a need of sacrificing animals.
Similarly, we have used this technique to compare the volatile metabolic signature of human breast cancer cell lines versus normal human mammary cells. We found significant differences and we therefore concluded that cancerous cells can release a characteristic odor whose constituents may be used as disease markers.
All in all, this project has brought SESI-MS technique to the next level, by showing proof-of-principle of applicability in different clinical scenarios.