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tooluniverse-cancer-genomics-tcga

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TCGA/GDC cancer genomics analysis — cohort construction, clinical metadata retrieval, somatic mutation frequencies, survival analysis, and multi-omics integration. Use for TCGA-BRCA-style cohort studies, mutation prevalence by cancer type, survival-by-mutation analysis, and pan-cancer driver discovery. Always cancer-type-specific (don't use pan-cancer counts without cohort context).

skills/tooluniverse-cancer-genomics-tcga/SKILL.md

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# Cancer Genomics / TCGA Analysis

**TCGA analysis starts with: what cancer type? what data type?** Build your cohort FIRST (GDC filters), then analyze. Don't query mutations without defining the cohort — pan-cancer counts from `GDC_get_mutation_frequency` are uninformative without cancer-type context. A mutation frequency of 10% in one cancer type may be 0.5% in another; always specify `project_id`. Survival analysis (Kaplan-Meier) is hypothesis-generating in retrospective TCGA data — always report sample size and p-value, and note that TCGA cohorts are not treatment-stratified.

**LOOK UP DON'T GUESS**: never assume TCGA project IDs, NCIt codes, or gene coordinates — use `GDC_list_projects` to confirm project IDs and `Progenetix_list_filtering_terms` for NCIt codes.

Systematic TCGA/GDC analysis: define cohorts, retrieve clinical data, profile somatic
mutations, query copy number variations, run survival analysis, and interpret variants
with OncoKB.

## When to Use

- "What is the mutation frequency of TP53 in TCGA-BRCA?"
- "Get survival data for TCGA-LUAD patients"