An EU Project on Gastric Cancer Screening via Breath Sampling
Decreasing the burden of cancer and curing most of cancer has been set a strategic goal either in the EU or most of the countries globally, including the partnering countries in the current project. The EU Code Against Cancer is recommending the individuals to attend those cancer screening programs that are proven to be effective and cost-effective, screening for gastric cancer is not among those. At the same time, the recommendations to the governments of the EU and Latim America by the Cancer Control joint action project (CanCon) are including the requirement to find an effective gastric cancer screening tool, and intensifying studies in this direction.
Translational nature of technology transfer in the involved areas, including Latin America desperately requiring solutions for decreasing the burden of a disease responsible for major morbidity and mortality.
Strengthening the research capacities of CELAC countries will be addressed by close collaboration either in the technical or clinical tracks.
Multidisciplinary approach to facilitate interaction and communication between the different R&D disciplines; the set goals will be achieved in close collaboration between researchers of technical and medical disciplines as well as social sciences; furthermore, data management will be covering the entire scope of the research activities.
Close collaboration between academia and industry is going to provide important advantages for implementation of project results. Furthermore, the involvement of an SME with outstanding experience in the technology development will ensure rapid advances in reaching the set goals.
Making effective use of resources by running a lab-track and a clinical-track in parallel.
Developing a cloud base system which will continuously collect information allowing further improvements for data interpretation.
Address the origin and metabolism of VOCs characterizing gastric cancer by measuring VOCs in different biological fluids and possible sources. The importance of this objective is defined by the fact that VOCs could be originating from cancer metabolism itself, other related to cancer factors such as microbiome, or unrelated factors (including confounding factors). The objective will be addressed by measuring VOCs from cancer tissue, cancer cell-lines, gastric content, H. Pylori by headspace analysis, and exhaled breath.
Validate the identified VOC pattern and previously described VOCs in the breath samples of current patient series with gastric cancer.
Investigation of the effect of the confounding factors (e.g., age, gender, diet, background disease(s), physical exercise, etc.) on the individual and combined VOCs profile(s).
Compare the breath and cancer tissue VOC patterns in genetically diverse populations, i.e. from Europe and Latin America, to determine ways to neutralize any negative confounding effects on the diagnostic results.
Biochemical modelling of the healthy and cancerous body functions on a cellular level and investigation of VOC-related genetic mutations that would be helpful for offering and monitoring of targeted therapy.
Implementing Responsible Research and Innovation approach throughout the project and beyond Europe.
Development of simple auxiliary equipment for collection of breath that interfaces the subject providing the samples and the sampling unit (e.g. disposable mouthpiece for breath sampling, sorbent phase, etc.).
Adaptation of offline and real-timechemical analysis methods for breath samples to monitor, respectively, composition and dynamic changes of the human volatolome linked with gastric cancer and precancerous lesions.
Development of a heterogeneous array of novel optical (IR) and electronic sensors capable of sensing and discriminating a wide spectrum of VOCs associated with the target lesions.
Integrating the developed VOC sensors into a single array chip with optimized readout microelectronics to deliver a miniaturized and inexpensive diagnostic device, ready to be scaled to industrial production.
Development of suitable and compact data formats, including encryption for data protection, for analysis of the patient’s data.
Development of a data analysis workflow, including advanced multivariate statistical algorithms and machine learning tools for analysing the diverse sensory output that provide predictive models for disease and genetics.
Integration of the sample collection apparatus into a closed, compact stand-alone minilab of the size of a desktop computer.