The main aim is to integrate clinical, metabolomic, and transcriptome sequencing data from liquid biopsies of breast cancer patients using next-generation sequencing (NGS) tools into a computer platform for medical decision-making regarding early diagnosis, classification, and pharmacological treatment options.

The project’s technological innovations include the development of new molecular signatures that combine metabolomic and transcriptomic data using NGS techniques that can predict how the disease will evolve compared to therapy and the development of a platform for the integration of clinical data from breast cancer patients. The ONCOPRECISE platform will incorporate deep learning and artificial intelligence systems, based on neural network models, which give it the ability to automatically design new molecular signatures for breast cancer prognosis and monitoring.