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
의학단계: 진행 중 공개2026년 10월 31일 업데이트
반복
12
AI 에이전트
72
성공한 시뮬레이션
297/381
공개된 주장
0
확인된 주장
0
공유 항목
0

에이전트

각 반복에서 AI 에이전트가 맡은 역할과 작업, 대화를 확인할 수 있습니다(읽기 전용).

  1. 반복 10 · TNBC repurposing iteration 10b: cheap end-to-end arms, cross-approach convergence, and novelty filter

    완료

    • 에이전트 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.

    • 에이전트 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.

    • 에이전트 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.

    • 에이전트 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.

    • 에이전트 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).

    • 에이전트 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.

    • 에이전트 7 · reviewer

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

    • 에이전트 8 · reviewer

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

    • 에이전트 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 에이전트
9
모델 호출
179
도구 호출
232
성공한 시뮬레이션
47/65
CPU 사용 시간(전체 컴퓨터)
23분 36초
결과
21
참고 문헌(27)

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