Periodic Reporting for period 2 - COMBAT-RES (Predicting potent drug combinations by exploiting monotherapy resistance)
Período documentado: 2022-07-01 hasta 2023-12-31
Cancer is a genetic disease driven by somatic mutations, which are accumulated during our lifespan. Mutation rates may be accelerated by environmental factors such as smoking and UV light exposure, however, the question is not if we get cancer, rather when considering increased life expectancies of humans. A challenge in successfully fighting cancer is overcoming drug resistances. For this, we first need to understand the different mechanisms of drug resistance. After gaining insights in these mechanisms, we aim to develop smart drug combinations to overcome / prevent drug resistances in the first place. In essence, the aim of our ERC project is to 1) gain insights in drug resistance and 2) prevent it with smart drug combinations. For achieving this, we are developing advanced biostatistical methods, machine learning and artificial intelligence which are customised to cancer biology.
We have developed novel algorithms to dissect further information from drug high-throughput screens, which give insights in drug resistance. These findings are currently further experimentally validated, and may pave the way for synergistic drug combinations. In addition, we have developed a computational framework based on biostatistics and machine learning to stratify patients based on mutations and tumour subtypes. This computational tool has revealed molecular biomarkers which are currently being validated in clinical settings, ultimately impacting patient treatment decisions in HER2+ metastatic gastric cancers.
We anticipate identifying drug combinations to overcome drug resistances. In addition, we will deliver novel patient stratification strategies, which ultimately will increase treatment success.