AI oncology algorithm and dynamic real-world learning health care system for precision oncology.

35 Background: Most tumours harbor multiple driver genetic alterations and many driver alterations are linked to multiple targeted therapies with various level of evidence. In addition, a specific treatment can be linked to multiple genetic alterations in the same tumor. Several public and private databases and software solutions are available to link driver alterations to treatments options, but in clinical practice of precision oncology we need a solution to select the right treatment for our patients based on the highest level of evidence also in case of complex molecular profiles. Methods: We have developed an AI oncology algorithm and rule-engine to prioritise treatment options for every cancer patient based on the individual molecular of their tumor. This IT solution can now prioritise 1200 compounds in clinical use or clinical development based on the computing of 24,000 evidence-based associations (“rules”) between drivers, targets and compounds. The software calculates a numeric score, the “aggregated evidence level” for each driver alterations and compounds. We have linked this decision support software to a dynamic patient case management system, which records responds to therapy to create learning system to provide dynamic decision support through several lines of therapies of each patient and to use real-life evidence to further improve the algorithm. Results: Our first results indicate that system allows individualised decision of diagnostic option between single gene tests to comprehensive 600 gene NGS panels and identification of actionable alterations in 83% of cancer cases. Conclusions: This system can be a first working solution to standardise clinical decisions precision oncology, which also helps the real-life evaluation of novel multigene molecular diagnostic tests and therapies to find their best indications and accelerate their reimbursement by insurance companies and national health funds.