Cancer metastasis is the leading cause of death within tumor disease and is currently an unmet clinical need. This dramatic clinical evidence requires immediate action in terms of early diagnosis and then pre-emptive therapies, not only for the primary tumor, but especially at the first stages of metastasis formation. Metastasis is the result of a complex mechanism, still under study, which involves the trafficking of biological materials, in particular extracellular vesicles (EVs), from the primary tumor site towards other tissues. It is thus imperative to develop novel and high-resolution biological techniques to understand the metastatic behaviour of tumour-derived EVs, with the aim to propose early diagnosis of metastasis and set timely pre-emptive therapies , empower metastasis treatments and patients’ outcome. To tackle these challenges, much more knowledge has to be gained on the metastatic potential of tumore-derived EVs and their molecular components, i.e. surface proteins and RNA contents, to be further generalized to other tumors.
For the above reasons, the project AI CUrES has developed novel methodologies for understanding the pathway and mechanism of metastasis spread led by EVs and has successfully implemented Artificial Intelligence algorithms to analyse these mechanisms and the early diagnostic images.
AI CUrES thus started from a Key Research Question: Is it possible to predict with high resolution the role of circulating EVs in cancer metastasis?
The answer was obtained by exploiting innovative research methodologies for high resolution and real-time bioimaging of circ-EVs and obtaining a powerful and novel digital diagnostic tool thanks to the use of Artificial intelligence (AI) algorithms. These breakthrough techniques has allowed gaining a knowledge on the role of EVs secreted by the primary tumor, but also empowering the research with new imaging tools.
This work has the future vision of predicting possible patient’s outcomes and helping to set timely pre-emptive therapies. The potential impact on oncological patient’s health is to reduce cancer-related deaths and treatment costs of the healthcare system, while opening up also breakthrough perspectives at the technological and industrial level for high-tech and high-resolution imaging techniques and e-tools in diagnostics.
In AI CUrES, EVs produced by a metastatic colorectal cancer (CRC) have been tagged by specific nanosized-contrast agents and their trafficking in biological media has been reported unravelling real-time, time-lapsed and molecular characteristics. The collected images of EVs trafficking towards recipient cell were achieved from in vitro cell cultures, 3D organoids and in vivo models, being refined and further elaborated by artificial intelligence algorithms, with the future aim to predict the possible metastasis development from a starting CRC tumor mass.
The main aim was thus to validate a breakthrough technology able to identify circulating EVs originated from a tumor mass of colorectal cancer; understand their trafficking mechanisms in spreading metastasis; early diagnose the possible insurgence of metastasis. These ambitious objectives comprise several goals in different sectors:
1. as scientific end goal, to unravel the circ-EVs trafficking mechanism and thus metastasis insurgence;
2. as a social and economic goal, to reduce cancer-related deaths, saving lives and public health money, empowering Europe in nanomedicine, med-tech tools and digital health;
3. as technological end goal, to develop a diagnostic method of circ-EVs surveillance and an innovative artificial intelligence e-tool to analyse the gained information and use it to obtain predictive values to fight cancer progression and metastasis, enabling early diagnosis;
4. as commercial end goal, to promote a Technology Transfer strategy to rapidly come up with a clinical innovation, introducing this new diagnostic technology in the clinical market.
