{
  "version": 3,
  "created_at": "2026-07-11T17:28:59.736Z",
  "task_summary": "End-to-end drug program for esophageal adenocarcinoma (EAC), full disease trajectory",
  "agents": [],
  "phases": [
    {
      "name": "Plan",
      "delegations": [
        {
          "steps": [
            {
              "title": "Stage 1b — Archetype & data triage",
              "description": "Classify EAC's disease archetype (somatic/sporadic cancer, not germline-monogenic) and data density using classify_archetype()/data_triage(), grounded in cBioPortal (mcp-cancer-models), Open Targets & ClinGen (mcp-clinical-genomics). Establish the pathogenesis axis (Barrett's->dysplasia->EAC via TP53 loss, CDKN2A, aneuploidy, ERBB2/GATA/MYC amplifications). Produce an archetype summary table. GATE: ARCHETYPE LOCK (hard)."
            },
            {
              "title": "Stage 2 — Patient priorities / PFDD",
              "description": "Summarize what matters to people living with EAC and Barrett's across the trajectory (dysphagia, reflux burden, surveillance anxiety, survival, quality of life, treatment toxicity) using patient-centered framing. Produce a priorities brief; run people_first_language() over user-facing prose. Soft gate: PRIORITY RANK."
            },
            {
              "title": "Stage 3 — Literature + competitive-pipeline map",
              "description": "Map the EAC therapeutic landscape from ClinicalTrials.gov (mcp-clinical-trials), Drugs@FDA (mcp-drug-regulatory), and ChEMBL (mcp-chembl): approved agents (trastuzumab, pembrolizumab/nivolumab, zolbetuximab/Claudin-18.2, bemarituzumab/FGFR2b) and pipeline by trajectory stage. Identify whitespace, especially interception. Deliver a landscape table + whitespace figure. GATE: WHITESPACE CONFIRM (hard) + DATA SUFFICIENCY (soft)."
            },
            {
              "title": "Stage 4 — Data mining -> target nomination",
              "description": "Mine EAC omics for dysregulated, druggable targets: cBioPortal mutation/CNA frequencies (mcp-cancer-models), Open Targets association scores & tractability (mcp-clinical-genomics), expression/surfaceome for the trajectory. Rank candidates in a target nomination table with evidence columns (genetic, expression, tractability, trajectory-stage relevance). Produce a ranked-target figure. Soft gate: TARGET EVIDENCE."
            },
            {
              "title": "Stage 5 — Target & modality selection",
              "description": "Score top candidates on the target-modality rubric; route modality from protein biology via recommend_modality() (surface receptor->antibody/ADC; secreted->trap; intracellular driver->small molecule/ASO; antigen-specific->vaccine). Present recommendation + alternatives with tractability (mcp-clinical-genomics), constraint, chemical matter (mcp-chembl). GATE: TARGET/MODALITY LOCK (hard) — user confirms target + modality."
            },
            {
              "title": "Stage 6 — Design & computational pressure-test",
              "description": "Route design by locked modality via design_skills_for(). Check list_compute for GPU; if available, run structure fetch -> backbone/binder design -> inverse folding -> fold-back validation with interface metrics (ipTM/pLDDT/pAE). If no GPU, deliver a concrete design specification + CPU-feasible prep (target structure retrieval, epitope/interface analysis, ESM embeddings). GATE: DESIGN GO/NO-GO (hard)."
            },
            {
              "title": "Stage 7 — Scientific development plan",
              "description": "Assemble the scientific plan from the plan template: computational design engine, in-vitro assay cascade (expression/QC->biophysics->functional/cellular), delivery/formulation, preclinical package, CMC & IND strategy, translational PD/biomarker strategy with explicit go/no-go gates. Deliver scientific_plan.md."
            },
            {
              "title": "Stage 8 — Regulatory strategy",
              "description": "Draft the regulatory path per trajectory setting: endpoints (OS/PFS/ORR for advanced; progression/MRD for interception/adjuvant), precedent approvals (CheckMate-577 adjuvant nivolumab), biomarker-defined enrollment, breakthrough/fast-track angles, companion-diagnostic needs. Use mcp-drug-regulatory. Deliver regulatory_brief.md. Soft gate: REG ASSUMPTIONS."
            },
            {
              "title": "Stage 9 — Commercial & financing",
              "description": "Size the EAC opportunity by setting, map competitors' commercial positions, identify pricing/reimbursement and whitespace, and outline a financing plan. Flag all numbers as assumptions for user confirmation. Deliver commercial_brief.md. GATE: FINANCING ASSUMPTIONS (hard)."
            },
            {
              "title": "Stage 10 — Deliverables & synthesis",
              "description": "Synthesize all stages into a final program deliverable: derive the honest verdict via program_verdict() (tractable/conditional/weak/not_yet_tractable) with a patient-useful best-next-step; compile a summary report, key figures, and the completed dossier. GATE: FINAL REVIEW (hard)."
            }
          ]
        }
      ],
      "id": "phase-0"
    }
  ],
  "desired_outputs": [
    "Program dossier (program_dossier.json) threading all decisions",
    "Indication & unmet-need brief for EAC across the trajectory",
    "Competitive pipeline / whitespace map",
    "Ranked target nomination table from omics + genetics",
    "Target + modality lock with rationale",
    "Computational design leads (or design spec if GPU deferred)",
    "Scientific development plan",
    "Regulatory + commercial/financing brief",
    "Final synthesized program deliverable with honest verdict"
  ],
  "feasibility": {
    "rationale": "EAC is a data-rich, somatic-driver cancer with public omics (TCGA-ESCA, ICGC, cBioPortal), a mapped competitive pipeline (HER2, PD-1, Claudin-18.2, FGFR2b), and a defined precursor (Barrett's) enabling an interception arm. Target mining and landscape mapping are highly feasible from connectors already available. The GPU-heavy design stage (Stage 6) depends on a configured compute target; if none is available I'll run CPU-feasible design prep and defer the heavy folding. Clinical efficacy claims will be flagged as hypotheses, never asserted.",
    "confidence": "high"
  }
}