Drug Repurposing Candidates for Triple-Negative Breast Cancer

Find approved or clinical-stage drugs that could be repurposed to treat triple-negative breast cancer (TNBC), the breast cancer subtype with no targeted therapy. This is the computational first stage of drug discovery: the goal is a ranked, evidence-backed shortlist of drug candidates and the reasons behind each one, ready for lab validation (cell lines, then animal models and clinical trials). Use only public data: 1) Drug response: GDSC2 (https://www.cancerrxgene.org/downloads) IC50/AUC values for breast cancer cell lines; label TNBC lines (ER-, PR-, HER2-, e.g. MDA-MB-231, MDA-MB-468, HCC1937, BT-549) and find drugs significantly more effective in TNBC than in other breast lines (effect size, FDR-corrected p-values). 2) Genetic dependencies: DepMap CRISPR gene effect (https://depmap.org/portal/download) to find genes TNBC lines depend on more than other breast lines; check whether the drugs from step 1 hit those genes (targets from ChEMBL https://www.ebi.ac.uk/chembl/api/data and DGIdb). 3) Structure check: for the top 5 drug-target pairs, dock the drug into the target structure (PDB or AlphaFold DB) with AutoDock Vina and compare against a known inhibitor of the same target as a baseline. 4) Drug-likeness and safety: approval status, known toxicities (ChEMBL), and RDKit properties. Report a ranked table of candidates with the evidence for each (response effect, dependency of its target, docking score vs baseline, approval stage), what is already known in the literature for each candidate, and clearly separate confirmed results from exploratory ones and from what needs lab validation.