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.

Raymer Medina
医学阶段： 进行中 公开更新于 2026年10月31日
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Final Report: Computational Repurposing Screen for TNBC (Lasques mission)

Summary

This mission set out to rank approved/clinical-stage drugs for triple-negative breast cancer (TNBC) repurposing by triangulating GDSC2 drug-response data, DepMap CRISPR dependencies, and docking. The mission did not produce a candidate shortlist. Both primary data endpoints (Sanger GDSC2 and DepMap) were inaccessible from the compute sandbox, and although a viable CC-BY-4.0 mirror was found and verified, the analysis pipeline did not complete within the mission's step budget. All work completed is exploratory infrastructure (data-access audit, mirrored data source identification) — no drug was tested, ranked, or docked, and no claim about any candidate is made here.

Key results

  • Data-access audit (exploratory, probe runs by agent 3): All 14 probed official routes to GDSC2 and DepMap data failed from the sandbox: Sanger GDSC2 endpoints return HTTP 410 Gone, DepMap portal/API returns a bot-verification challenge (HTTP 403). This is the mission's main completed result and was published as knowledge (id 916d7c17).
  • Viable GDSC2 substitute identified (exploratory): Zenodo DOI 10.5281/zenodo.20620409 (CC-BY-4.0) provides a 559 MB SuppTable1_RecalculatedProfiles.csv with recalculated GDSCv2/GDSCv1/CTRP/PRISM log_IC50/AUC for 200+ drugs (GDSCv2 drugs = 207 per streaming job metrics) across 337+ cell lines, including breast lines. Range requests verified (byte-range probe of the file succeeded).
  • Reproducibility baseline (exploratory): The same Zenodo record's cross-database correlation table shows GDSCv2 vs PRISM Spearman r ≈ 0.38 for EC50 and ~0.52–0.62 for AUC metrics — a useful batch-effect/noise floor for any future selectivity claim.
  • DepMap dependency data: No open mirror of the DepMap CRISPR gene-effect matrix exists. A streaming job read a DepMap-formatted gene-effect file (1150 genes scanned) but matched 0 breast cell lines, so no TNBC-selective dependencies were computed.
  • No ranked table, no docking, no RDKit/safety analysis was produced. No preregistered hypotheses were run; there are zero claims, and the evaluator leaderboard is null. Nothing in this mission reached a confirmed evidence level.

What did not work

  • Direct downloads from the mission's specified sources: GDSC2 (cancerrxgene.org / cog.sanger.ac.uk) and DepMap portal downloads are not usable from the sandbox — the mission's step 1 and step 2 could not be executed as planned.
  • Basal-lineage counter-screen, cross-tissue validation, polypharmacology scoring, docking triage, and drug-likeness checks: all remained untested, because the underlying response/dependency screen never completed; the lead agent hit the step limit mid-pipeline.
  • DepMap gene-effect matching: the streamed gene-effect file contained no breast cell lines (n_breast_lines = 0), so the dependency leg of the triangulation stayed empty.

Confidence and limitations

  • Confidence in the data-access findings is high (multiple independent probe runs agree; agent 3 confidence 0.7). Confidence in any drug candidate: none — none were produced.
  • Every number quoted above is exploratory (single-run probe output), not from a confirmed preregistration. No effect sizes, FDR p-values, or docking scores exist for any drug–target pair.
  • With only ~10–15 TNBC lines among ~50 breast lines, any future selectivity claim must report effect sizes with confidence intervals and treat borderline FDR results as exploratory — this limitation remains fully unaddressed and needs the completed analysis plus lab validation (cell lines → animal models → trials).

Next steps

  1. Human-assisted DepMap download (researcher action): download the DepMap CRISPR gene effect (Chronos) matrix in a browser from depmap.org and attach it as a context document.
  2. Restart the screen on the Zenodo mirror: stream SuppTable1_RecalculatedProfiles.csv in ~10–25 MB range chunks, filter Database == GDSCv2 and breast lines (TNBC: MDA-MB-231, MDA-MB-468, HCC1937, BT-549), and run per-drug TNBC-vs-other Welch t-tests with BH-FDR.
  3. Use the GDSCv2|PRISM AUC Spearman ≈ 0.6 correlation as the reproducibility baseline before accepting any selectivity claim.
  4. Then proceed with the planned counter-screens (pan-essential gene filter, basal-lineage confound), docking triage, and prior-art/literature flagging — none of which has started.
  5. Validate all inputs at small scale first (schema, encoding, breast-line labels) before any large streaming run, per the mission's stated risk mitigation.

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