Cancer Genomics / TCGA Analysis

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"
• "Find clinical data for breast cancer cases in GDC"
• "Which TCGA projects have KRAS G12C mutations?"
• "Show CNV amplifications of EGFR in glioblastoma"
• "Annotate BRAF V600E for clinical significance in melanoma"

NOT for (use other skills instead)

• Precision oncology treatment recommendations -> Use tooluniverse-precision-oncology
• Rare disease gene discovery -> Use tooluniverse-rare-disease-genomics
• GWAS variant interpretation -> Use tooluniverse-gwas-snp-interpretation

Workflow Overview

Input (cancer type / gene / TCGA project ID)
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Phase 1: Study Selection  -- GDC_list_projects, GDC_search_cases
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Phase 2: Clinical Data    -- GDC_get_clinical_data
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Phase 3: Somatic Mutations -- GDC_get_ssm_by_gene, GDC_get_mutation_frequency