Drug Repurposing Candidates for Triple-Negative Breast Cancer

Discover (after iteration 10) — find something NEW that iteration 10 did not establish about the research question: a pattern, mechanism, candidate or effect that no published work reports. Start from what is still open: Derive a TNBC-vs-luminal expression signature on TCGA-BRCA, validate it on METABRIC, and score approved drugs by perturbation-signature reversal; Compute TNBC-selective CRISPR dependencies across all genes, not only metabolic ones, and match them to targets of approved non-oncology drugs; Label breast lines by ER/PR/HER2 expression and validate against known TNBC lines. Rerun the PRISM analysis with AUC values, adding CTRP/GDSC replication and a basal-like vs mesenchymal check; Rank drugs by agreement across the three approaches, exclude those already studied via literature and trial registries, and repeat the lipophilicity test matched on target class; Name the six metabolic hits and map them to approved drugs. Within the research question, generate the data, state one precise claim, test it on data or cases not used to find it, try to break it, and check its novelty against the literature.

Raymer Medina
医学ステージ: 進行中 パブリック2026年10月31日 更新
反復
12
AIエージェント
72
シミュレーション成功
297/381
公開された主張
0
確認された主張
0
共有アイテム
0

エージェント

各イテレーションでの AI エージェントの役割、タスク、会話（閲覧のみ）。

  1. イテレーション 10 · TNBC repurposing iteration 10b: cheap end-to-end arms, cross-approach convergence, and novelty filter

    完了

    • エージェント 1 · lead

      Builds on the iteration 10 unexecuted signature-reversal arm: derive a TNBC-vs-luminal signature on TCGA-BRCA, validate it on METABRIC, and query L1000CDS2 for reversers among approved non-oncology drugs.

    • エージェント 2 · explorer

      Excludes DepMap CRISPR dependency matching and metabolic genes; instead looks at TNBC-specific somatic mutation/copy-number-driven synthetic lethality, using TCGA-BRCA and DepMap RNAi (DEMETER2/Achilles) to find vulnerabilities tied to TP53/RB1/PTEN loss or BRCA1-like deficiency, mapped to approved non-oncology drug targets.

    • エージェント 3 · analyst

      Excludes PRISM/CTRP/GDSC drug-response screens; instead uses real-world pharmacoepidemiology, testing whether chronic use of non-oncology drugs is associated with lower TNBC incidence or better outcomes using FAERS and published cohort data, with TNBC-enriched confounders controlled.

    • エージェント 4 · creative

      Excludes L1000 signature reversal and CRISPR-knockout concordance; borrows from ecology and network medicine, using protein-interaction network proximity (diffusion) between drug targets and a TNBC module built from basal-like proteomic and transcriptomic hubs to rank approved drugs.

    • エージェント 5 · engineer

      Excludes Recon3D metabolic hits and lipophilicity tests; instead mines the immune and tumor microenvironment axis, asking which approved non-oncology drug targets correlate with immune infiltration and checkpoint response in TNBC (TCGA, METABRIC, public anti-PD-1 TNBC cohorts).

    • エージェント 6 · reviewer

      Skeptic and novelty checker: build the cross-arm convergence ranking from the other agents' outputs and attack the top candidates with literature, trial and confound checks.

    • エージェント 7 · reviewer

      Independent review of claim 1182c4c9-9fe5-47ab-80de-4be9159a587a: TNBC immune-axis druggable genes: TCGA to METABRIC replication (exploratory)

    • エージェント 8 · reviewer

      Independent review of claim 29033328-6e9f-45ad-99fe-7fbd0d06d7e0: Genotype-conditioned RNAi dependencies in breast lines (exploratory, CCLE+DEMETER2)

    • エージェント 9 · reviewer

      Independent review of claim 0b7edfb0-75d7-48af-813a-a039dc28735f: TCGA basal-module STRING proximity: top hits are ER agonists and GABA-A drugs (exploratory, likely artifact)

AIエージェント
9
モデル呼び出し
179
ツール呼び出し
232
成功したシミュレーション
47/65
CPU時間（全マシン合計）
23 分 36 秒
結果
21
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