tooluniverse-cancer-variant-interpretation

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 43be0fac747b · 31 Oct 2026Report an issue or request a re-scan

• Publishermims-harvard GitHub organizationOwner of the repository this skill is read from
• Source foldermims-harvard/tooluniverse/skills/tooluniverse-cancer-variant-interpretationThe exact folder we audited
• LicenceApache-2.0As the repository states it
• Commit pinned for this verdict43be0fac747bA new audit runs when the source changes
• Repository statusNot archivedLast push 3 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Clinical interpretation of somatic cancer mutations for precision oncology. Transforms a gene + variant + cancer-type input into an actionable report: clinical evidence tier (CIViC, OncoKB), therapeutic options…

---
name: tooluniverse-cancer-variant-interpretation
description: "Clinical interpretation of somatic cancer mutations for precision oncology. Transforms a gene + variant + cancer-type input into an actionable report: clinical evidence tier (CIViC, OncoKB), therapeutic options (FDA-approved + investigational), resistance mechanisms, prognosis, and matching clinical trials. Use for tumor-board variant calls, somatic-mutation actionability assessment, and treatment selection. Always cancer-type-specific."
disable-model-invocation: true
---

# Cancer Variant Interpretation for Precision Oncology

Comprehensive clinical interpretation of somatic mutations in cancer. Transforms a gene + variant input into an actionable precision oncology report covering clinical evidence, therapeutic options, resistance mechanisms, clinical trials, and prognostic implications.

**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Evidence-graded** - Every recommendation has an evidence tier (T1-T4)
3. **Actionable output** - Prioritized treatment options, not data dumps
4. **Clinical focus** - Answer "what should we treat with?" not "what databases exist?"
5. **Resistance-aware** - Always check for known resistance mechanisms
6. **Cancer-type specific** - Tailor all recommendations to the patient's cancer type when provided
7. **Source-referenced** - Every statement must cite the tool/database source
8. **English-first queries** - Always use English terms in tool calls (gene names, drug names, cancer types), even if the user writes in another language. Respond in the user's language

---

## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

---

## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

## When to Use

Apply when user asks:
- "What treatments exist for EGFR L858R in lung cancer?"
- "Patient has BRAF V600E melanoma - what are the options?"
- "Is KRAS G12C targetable?"
- "Patient progressed on osimertinib - what's next?"
- "What clinical trials are available for PIK3CA E545K?"
- "Interpret this somatic mutation: TP53 R273H"

---

## Input Parsing

**Required**: Gene symbol + variant notation (e.g., "EGFR L858R", "BRAF p.V600E", "EML4-ALK fusion", "HER2 amplification")
**Optional**: Cancer type (improves specificity)

Parse the gene symbol and variant separately. For fusions, use the kinase partner as the primary gene. For amplifications/deletions, use the gene name directly. Normalize common aliases: HER2 -> ERBB2, PD-L1 -> CD274, VEGF -> VEGFA.

---

## Phase 0: Tool Parameter Verification (CRITICAL)

**BEFORE calling ANY tool for the first time**, verify its parameters.

| Tool | WRONG Parameter | CORRECT Parameter |
|------|-----------------|-------------------|
| `OpenTargets_get_associated_drugs_by_target_ensemblID` | `ensemblID` | `ensemblId` (camelCase) |
| `OpenTargets_get_drug_chembId_by_generic_name` | `genericName` | `drugName` |
| `OpenTargets_target_disease_evidence` | `ensemblID` | `ensemblId` + `efoId` |
| `MyGene_query_genes` | `q` | `query` |
| `search_clinical_trials` | `disease`, `biomarker` | `condition`, `query_term` (required) |
| `civic_get_variants_by_gene` | `gene_symbol` | `gene_id` (CIViC numeric ID) |
| `drugbank_*` | any 3 params | ALL 4 required: `query`, `case_sensitive`, `exact_match`, `limit` |
| `ChEMBL_get_drug_mechanisms` | `chembl_id` | `drug_chembl_id__exact` |
| `ensembl_lookup_gene` | no species | `species='homo_sapiens'` is REQUIRED |

---

## Workflow Overview

```
Input: Gene symbol + Variant notation + Optional cancer type

Phase 1: Gene Disambiguation & ID Resolution
  - Resolve gene to Ensembl ID, UniProt accession, Entrez ID
  - Get gene function, pathways, protein domains
  - Identify cancer type EFO ID (if cancer type provided)

Phase 2: Clinical Variant Evidence (CIViC)
  - Find gene in CIViC (via Entrez ID matching)
  - Get all variants for the gene, match specific variant
  - Retrieve evidence items (predictive, prognostic, diagnostic)

Phase 3: Mutation Prevalence (cBioPortal)
  - Frequency across cancer studies
  - Co-occurring mutations, cancer type distribution

Phase 4: Therapeutic Associations (OpenTargets + ChEMBL + FDA + DrugBank)
  - FDA-approved targeted therapies
  - Clinical trial drugs (phase 2-3), drug mechanisms
  - Combination therapies

Phase 5: Resistance Mechanisms
  - Known resistance variants (CIViC, literature)
  - Bypass pathway analysis (Reactome)

Phase 6: Clinical Trials
  - Active trials recruiting for this mutation
  - Trial phase, status, eligibility

Phase 7: Prognostic Impact & Pathway Context
  - Survival associations (literature)
  - Pathway context (Reactome), Expression data (GTEx)

Phase 8: Report Synthesis
  - Executive summary, clinical actionability score
  - Treatment recommendations (prioritized), completeness checklist
```

For detailed code snippets and API call patterns for each phase, see `ANALYSIS_DETAILS.md`.

---

## Clinical Reasoning Strategies

### Driver vs Passenger Reasoning

Not every mutation in a tumor is driving the cancer. Before querying databases, form a hypothesis:

- **Is this gene a known oncogene or tumor suppressor?** Genes like EGFR, BRAF, KRAS, TP53, PIK3CA are well-established cancer drivers. A mutation in one of these warrants deep investigation. A mutation in a gene with no known cancer role is likely a passenger.
- **Is this specific mutation recurrent across tumors (hotspot)?** Use cBioPortal to check prevalence across specific studies, or `GenomeNexus_get_cancer_hotspots`

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/mims-harvard/tooluniverse
git -C tooluniverse checkout 43be0fac747b

Works in

1. 2026-10-31
   ✓SAFE B 89 / 100

   first audit · source 43be0fac747b

Questions about tooluniverse-cancer-variant-interpretation

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