Scar target nomination

Scar Target Nomination

Description

Use this skill to assemble an OriGene-style OpenScar target nomination package from clinical phenotype anchors, ScarAtlas evidence, literature support, mechanism hypotheses, and validation feasibility. It ranks candidates for research prioritization only and records why each candidate is advanced, downgraded, or rejected.

This skill does not validate targets, diagnose disease, prescribe treatment, determine recurrence, or claim that a candidate is a validated biomarker.

When to Use

• A target-discovery run needs a ranked candidate list from multiple evidence layers.
• ScarAtlas outputs need translation into a ScarClaw nomination packet.
• A research team needs a transparent target-priority table before wet-lab or tissue validation planning.
• A reviewer asks for explicit rejection reasons and feedback updates.

Do Not Use

• Do not nominate targets for patient-specific treatment choice, clinical prediction, diagnosis, or biomarker deployment.
• Do not rank candidates solely from one omics source without clinical phenotype context.
• Do not convert research priority scores into clinical utility claims.
• Do not omit rejected candidates when rejection reasons are part of the workflow.
• Do not bypass clinician-scientist review.

Required Inputs

• Target-discovery question and intended OpenScar program.
• Candidate list from ScarAtlas, literature, clinician-scientist hypothesis, or prior evidence review.
• Phenotype anchor for each candidate, such as scar activity, recurrence-context, refractory-context, invasive-edge behavior, or treatment-exposure context.
• Evidence summaries for bulk, single-cell, spatial, pathway, literature, and assay feasibility layers.
• Scoring rubric or table structure, such as data/templates/target_candidate_evidence_score.csv.
• Known constraints, including sample type, tissue availability, comparator group, governance, and manuscript claim boundary.

Workflow

1. Confirm the nomination is research-only and uses de-identified or public evidence.
2. Define the target-discovery question and OpenScar program.
3. Normalize each candidate into a target record:
   • target identifier
   • phenotype anchor
   • treatment exposure context
   • visual or trajectory context
   • endpoint readiness
   • omics evidence
   • literature evidence
   • mechanism hypothesis
   • validation feasibility
4. Score candidates conservatively using the agreed research-priority rubric.
5. Rank candidates and separate active nominations from downgraded or rejected candidates.
6. Write rejection reasons for weak clinical anchors, inconsistent omics, poor specificity, endpoint immaturity, infeasible assays, or overclaim risk.
7. Add a feedback update log for clinician-scientist decisions and future evidence needs.

Output Format

• Target-discovery question
• Required input summary
• Ranked candidate target list
• Candidate evidence table
• Mechanism hypotheses and competing explanations
• Validation recommendation for future research
• Rejection reasons and downgrade notes
• Feedback update log
• Safety boundary
• Clinician-scientist review requirement
• No treatment recommendation statement
• No validated biomarker claim statement

Safety

Safety boundary: this skill supports research target nomination and clinician-scientist review only. It does not diagnose, prescribe, determine recurrence, validate therapeutic targets, recommend treatment, or replace licensed clinical judgment. Escalate urgent patient-specific concerns outside the target-discovery workflow to a qualified clinician.

Clinician-scientist review requirement: a qualified clinician-scientist must review the ranked list, evidence table, rejection reasons, and wording before any candidate is used in experiment planning, publication, collaboration, or grant materials.

No treatment recommendation: rankings are research-priority rankings only and must not be used to choose, avoid, intensify, or personalize care.

No validated biomarker claim: nomination does not mean validation. Do not describe a candidate as a validated biomarker, diagnostic marker, predictive marker, response marker, or clinically actionable target.

Examples

Nominate and rank exploratory targets for an invasive fibroblast ECM axis using ScarAtlas v0.2 evidence, literature support, validation feasibility, and explicit rejection reasons.

Integration

Works well with scar-target-evidence-review, scar-experiment-validation-critic, scar-literature-review, scar-treatment-response-review, and recurrence-risk-factor-extraction.