Project description
AI to identify cancer metastasis patterns
The spread of cancer to distant sites within the body, also known as metastasis, is largely responsible for cancer-related mortality. Although the underlying mechanisms of how metastases are formed have not been fully elucidated, circulating tumour cells (CTCs) seem to be implicated. Funded by the European Innovation Council, the 3DSecret project aims to identify stochastic patterns in the formation of metastases. Researchers will isolate CTCs from breast cancer patients, culture them into spheroids and perform transcriptomic, metabolomic, and genomic analysis. The work will contribute to the understanding of tumour heterogeneity and, alongside clinical information, will lead to the development of an AI tool that can identify patterns of metastasis.
Objective
Metastasis remains accountable for 9 out of 10 fatalities within cancer disease. However, the mechanisms governing the onset of metastasis are far from being fully understood. Notably, metastases are predominantly clonal and arise from a single cell. 3DSecret will investigate metastasis from a radically new perspective, with the overarching goal of unravelling stochastic patterns at the single-cell level with predictive and prognostic capacity. Critically, defining the hallmarks of metastasis from holistic studies of single circulating tumour cells (CTCs), thus dissecting tumour heterogeneity, has the power to revolutionise cancer treatment and diagnosis. This will pave the way for game-changing discoveries in what is one of the holy grails of modern clinical science. To achieve our goal, 3DSecret will use a set of key enabling technologies including microfluidics, nanosensors, genomics, and artificial intelligence (AI). Microfluidics will drive the isolation of single CTCs from whole blood samples of 60+ metastatic breast cancer patients. These will be grown on-chip to form 3D spheroids, thus allowing comprehensive genomic and transcriptomic studies of single-cell origin while bypassing the errors typically introduced by single-cell genome amplification. The genomic and transcriptomic data will be combined with clinical information, single-cell growth profiles and dynamic metabolomic analyses obtained by the use of nanosensors and SERS, to develop a multimodal AI analytical tool capable of identifying unknown patterns driving metastasis. The bold assumption that there could be stochastic patterns driving metastasis, cancer evolution and malignancy, makes the approach of 3DSecret exceptionally high-risk, high-gain. We are confident that such a breakthrough would lead to a major paradigm shift with significant implications in biology, physics, disruptive technologies such as AI, and critically, in the medical arena and patient care.
Fields of science
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencesphysical sciencesclassical mechanicsfluid mechanicsmicrofluidics
- natural sciencesbiological sciencescell biologycell metabolism
- medical and health sciencesclinical medicineoncologybreast cancer
- natural sciencesbiological sciencesgeneticsgenomes
Keywords
Programme(s)
- HORIZON.3.1 - The European Innovation Council (EIC) Main Programme
Funding Scheme
HORIZON-EIC - HORIZON EIC GrantsCoordinator
4715-330 Braga
Portugal