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Discovering Gene-Drug Interactions in Breast Cancer With a Systematic and Genetically Tractable Model (GENES, DRUGS AND BREAST CANCER)
Start date: May 1, 2013, End date: Apr 30, 2018 PROJECT  FINISHED 

Biomedical research has been very successful in finding the individual genes that are deregulated in cancer and it is estimated that ~2% of all genes can be classified as “cancer genes”. However, it has proven challenging to translate this genomics knowledge into effective treatments.One complication is that cancer cells typically carry many molecular aberrations and every tumor displays a unique pattern. This heterogeneity complicates the application of drugs and biologicals as it often interferes with patient response. Thus, unraveling the complex interplay between cancer genes and drugs is of great importance for effective patient-stratified cancer therapy.Besides playing a role in drug resistance, the molecular changes also result in cancer cells becoming uniquely dependent on certain gene products or pathways. These cancer “vulnerabilities” offer opportunities targeted therapies, including those based on oncogene addiction and synthetic lethality. Therefore, these concepts have attracted much interest as they can help bridge the gap between cancer genomics and effective treatments.I propose to identify cancer vulnerabilities and drug resistance mechanisms in breast cancer by developing a genetically tractable model system. Designed to capture relevant constellations of genetic aberrations found in patients, it extends our recently developed, systematic screening platform. Combined with bioinformatics integration of cell line data, this will result in a unique and powerful approach.I aim to identify functional interactions between breast cancer genes and experimental or approved drugs. Detailed characterization of gene-drug interactions will reveal their translational potential for targeting specific cancer vulnerabilities in patients, or as biomarkers. Together, this project aims to improve therapies in breast cancer by identifying patient cohorts that are most likely to benefit from a given drug and revealing novel cancer Achilles’ heels.
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