If you are a drug discovery firm dealing with high failure rates in oncology trials — this project developed a microfluidics-based imaging platform that tests how single cells react to drugs in 3D environments. This allows for more accurate predictions of drug resistance before expensive clinical trials.
AI-Powered Imaging Platform to Predict Cancer Aggressiveness and Drug Resistance
Imagine cancer cells as shapeshifters that change their form to sneak through the body and hide from medicine. This technology acts like a high-tech security camera that watches these cells in a fake 3D environment to see exactly how they move and adapt. By scoring these 'shapeshifting' behaviors, doctors can predict if a tumor is likely to spread or resist treatment.
What needed solving
Current clinical methods cannot analyze cancer cell plasticity at a single-cell level, making it difficult to predict which tumors will metastasize or resist therapy.
What was built
A microfluidics-based imaging platform featuring a contact-free micro-confinement system and a computational pipeline for morphodynamic analysis to generate the PLAST_SCORE.
Who needs this
Who can put this to work
If you are a precision medicine lab dealing with the inability to predict metastasis from standard biopsies — this project developed PLAST_SCORE to quantify tumor plasticity. This provides a new scoring model to predict cancer aggressiveness and enhance clinical decision-making.
If you are a microfluidics manufacturer dealing with the limitations of traditional PDMS-based systems — this project developed a contact-free micro-confinement system. This advancement improves scalability and reproducibility for industrial applications.
Quick answers
What is the cost or pricing for the PLAST_SCORE model?
Based on available project data, no specific pricing or cost structure for the final scoring model has been disclosed.
Can this technology be scaled for industrial use?
Yes, the project developed a contact-free micro-confinement system specifically to resolve the technical and industrial limitations of traditional PDMS-based systems, enhancing scalability.
What is the IP or licensing status of the platform?
Based on available project data, specific patent numbers or licensing terms are not provided, though the project is funded under HORIZON-EIC.
How does this integrate with existing clinical workflows?
The platform requires minimal sample sizes of less than 10k cells, making it potentially compatible with patient samples for diagnosis and prognosis.
What is the timeline for market availability?
The project period runs from 2022-05-01 to 2026-10-31, suggesting the final validated model will be ready toward the end of 2026.
Who built it
The consortium consists of 6 partners across 4 countries (AT, DE, ES, FR). It is heavily weighted toward research and academia, with 4 research organizations and 1 university. However, there is a 17% industry presence via one SME, indicating a bridge between fundamental super-resolution microscopy research and commercial application.
Contact Fundacio Centre de Regulacio Genomica in Spain
Talk to the team behind this work.
Contact us to explore licensing opportunities for the PLAST_SCORE model.