The overall aim of LIQBIOPSENS project is the further development and validation in real settings of a novel diagnostic platform for the early and fast detection of ctDNA and their KRAS and BRAF mutations associated to colorectal cancer through blood samples. The main features of LIQBIOPSENS are: reliability (detection rates vary from 95–100 %), low-cost (40-50 € per sample analysis), sensitivity (in the zM range), multiplexing capabilities (analysis of 27 KRAS and BRAF mutations simultaneously), short analysis time (30-60 min.), user-friendly interface and flexibility. Solution proposed by the LIQBIOPSENS project relies on the multidisciplinary integration of different Key Enabled Technologies: microelectronics, microfluidics, nanomaterials and genomics. In particular, LIQBIOPSENS platform is based on the integration of two novel complementary technologies. On the one hand, the revolutionary DGL© technology property of DestiNA Genomics Ltd, capable of delivering faster, more error-free detection of nucleic acids and their mutations than current enzyme-based detection systems, making 'false positive' results a thing of the past. On the other hand, a novel high resolution acoustic wave microsensor technology property of AWSensors, that allows an accurate, inexpensive, label-free, direct and real time transduction method to quantitatively evaluate the results of the application of the above mentioned DGL© technique.

Breast cancer is the most common type of cancer affecting woman in the EU. Multidisciplinary Breast Units (BUs) were introduced in order to deal efficiently with breast cancer cases, setting guideline-based quality procedures and a high standard of care. However, daily practice in the BUs is hampered by the complexity of the disease, the vast amount of patient and disease data available in the digital era, the difficulty in coordination, the pressure exerted by the system and the difficulty in deciding on cases that guidelines do not reflect. DESIREE aims to alleviate this situation by providing a web-based software ecosystem for the personalized, collaborative and multidisciplinary management of primary breast cancer (PBC) by specialized BUs. Decision support will be provided on the available therapy options by incorporating experience from previous cases and outcomes into an evolving knowledge model, going beyond the limitations of the few existing guideline-based decision support systems (DSS). Patient cases will be represented by a novel digital breast cancer patient (DBCP) data model, incorporating variables relevant for decision and novel sources of information and biomarkers of diagnostic and prognostic value, providing a holistic view of the patient presented to the BU through specialized visual exploratory interfaces. The influence of new variables and biomarkers in current and previous cases will be explored by a set of data mining and visual analytics tools, leveraging large amounts of retrospective data. Iintuitive web-based tools for multi-modality image analysis and fusion will be developed, providing advanced imaging biomarkers for breast and tumor characterization. Finally, a predictive tool for breast conservative therapy will be incorporated, based on a multi-scale physiological model, allowing to predict the aesthetic outcome of the intervention and the healing process, with important clinical and psychological implications for the patients.