No performant multi-omic data aggregation and analysis tool is currently available on the market and cancer scientists are thus unable to leverage this valuable data for the prioritisation of drug discovery programs. With limited data skills, they can only rely on often disappointing collaborations with teams of bioinformaticians, with whom communication can be difficult, or on the use of suboptimal data science software. While the available solutions are generally based on relevant mathematical frameworks, they are poorly designed, non-user friendly and require computer programming skills. Moreover, available tools do not use AI to assist the user in the harmonisation of clinical data, a very time-consuming step in data aggregation.
We are developing mCUBE, an Artificial Intelligence (AI)-augmented cloud-based precision oncology platform for multi-omic, multi-cancer and multi-source data identification, aggregation and analysis. mCUBE encapsulates Epigene Labs’ deep expertise in oncology, data science, and bioinformatics into a user-friendly solution that will make a difference for cancer patients. Our solution delivers 3 main gains:
- increased productivity for our users (cancer scientists),
- increased profitability for our customers (biopharma companies), and
- improved care for cancer patients ultimately.
The conventional timeline for discovering a new target for a specific indication is 2 years. At Epigene, in the context of our first partnership with the Eyquem’s lab at UCSF, we not only rediscovered known antigens but also prioritised a list of promising targets for relapsed/refractory multiple myeloma in only 6 months. A few months later, in a second program focusing on colorectal and gastric cancer, this same task took only 3 months. Soon, thanks to the support of the EIC Accelerator, we will be able to tackle a new indication in only a few weeks.
