PredAlgoBC · Machine learning prediction for breast cancer therapy

Machine learning prediction for breast cancer therapy

Breast cancer is a leading cause of cancer death worldwide. In Europe, it caused about 138,000 deaths in 2018. This high death rate is mainly due to metastatic cancers for which treatment is less performant than for non-metastatic cancer (27% survival rate at 5 years for metastatic cancer versus 90% for all breast cancer, respectively). Metastatic cancers are a dissemination of the initial breast tumors cells all over the body. Some patients have already metastatic cancer when they are first diagnosed, but for most of them, it is an evolution of the initial disease that escapes treatment. In order to reduce tumor escape from treatment, it is necessary to give patients drugs that are the best adapted to their own tumor characteristics. To do that, we need to find which tumor characteristic we will have to measure before treatment that will help defining which treatment is more appropriated for each person. It is what is called personalized/precision medicine. The search for these measurable characteristics (called biomarkers) is a discipline where we use large databases built from collected patient tumor information. Most precisely, we have to search among several thousands of tumor characteristics (measured before treatment) which ones can help predicting patient treatment response. To be able to analyze these thousands of characteristics issued from thousands of patients, we need to use adapted mathematical tools that will extract pertinent information from the large amount of not-pertinent ones. These tools are machine learning (ML) algorithms. The search of biomarker for response to treatment is a growing field where only a few biomarker "signatures" have reached the clinic. One of the reasons of this mellow success is called the curse of dimensionality: data in health are composed of a large amount of measured characteristics, but there is often not enough patient samples to represent correctly the variation in population, and ML algorithms do not perform well in this situation. The goal of this project is to use mathematical approaches combined with biological thorough analysis to reach a setting where the information given by the algorithms will be usable in the clinic. PredAlgoBC reached its objectives since we obtained two signatures (assemblage of biomarkers) ready to use that can predict response to hormonotherapy in breast cancer.

Data: CORDIS, © European Union