Objective
Patients suffering from triple-negative breast cancer (TNBC) have a poor prognosis as these tumors frequently confer resistance against chemotherapeutic agents and lack drug targets such as estrogen receptor, progesterone receptor, and epidermal growth factor receptor 2. Insufficient knowledge on the biology of this specific breast tumor type and its heterogeneity hinder the identification of potential novel drug targets. Lethality enhancer screening is an ideal approach to identify new drug targets in tumors with specific genetic aberrations. We plan to adapt this concept of synthetic lethality by anticipating that while TNBC cells confer resistance to available anticancer drugs, specific knock down of particular genes by RNA-interference (RNAi) may result in a synergistic cell killing. Another important aspect of our approach is that we will concentrate in our screens on the top 500 candidate genes shown to be crucial in TNBC for cellular processes. The genes will be prioritized by Bayesian network analysis on prior knowledge on clinical TNBC from our own extensive genomics and proteomics studies, the literature, next generation sequencing efforts, and databases listing drugability of targets. We will employ RNAi-based knock down of drugable targets in 22 cell lines to reveal genes essential for drug resistance in TNBC. In addition to 2D cultures, screens will also be applied to 3D cultures, which are thought to better reflect the in vivo situation. The most effective combinations for each TNBC subtype will further be functionally investigated in vitro and in vivo to unravel the molecular nature of the synthetic lethality. Finally, translational studies will be performed to establish the potential clinical relevance of the identified targets/pathways in large numbers of human TNBC and non-TNBC tumors on tissue microarrays. It is expected that the newly designed (combination) therapies result in a decline in TNBC mortality and reduction of healthcare costs.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesgenetics
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsproteomics
- social sciencessociologydemographymortality
- medical and health sciencesbasic medicinepharmacology and pharmacydrug resistance
- medical and health sciencesclinical medicineoncologybreast cancer
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Call for proposal
ERC-2012-ADG_20120314
See other projects for this call
Funding Scheme
ERC-AG - ERC Advanced GrantHost institution
3015 GD Rotterdam
Netherlands