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).
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).
triggers
[{"keywords":["TCGA","GDC","cancer cohort","somatic mutation","Kaplan-Meier","survival analysis","CNV","copy number variation","Progenetix","OncoKB","tumor","cancer genomics","mutation frequency"]},{"patterns":["TCGA-","survival analysis for","mutation frequency in","copy number","GDC project","cancer cases","overall survival"]}]
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)
|
v
Phase 1: Study Selection -- GDC_list_projects, GDC_search_cases
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v
Phase 2: Clinical Data -- GDC_get_clinical_data
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v
Phase 3: Somatic Mutations -- GDC_get_ssm_by_gene, GDC_get_mutation_frequency
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v
Phase 4: CNV Analysis -- Progenetix_cnv_search, Progenetix_search_biosamples
|
v
Phase 5: Survival Analysis -- GDC_get_survival
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v
Phase 6: Variant Interpretation -- OncoKB_annotate_variant
Key Identifiers
Data Type
Format
Example
GDC project
TCGA-{ABBREV}
TCGA-BRCA, TCGA-LUAD, TCGA-SKCM
GDC case
UUID
3c6ef4c1-...
NCIt cancer code
NCIT:C######
NCIT:C4017 (breast), NCIT:C3058 (GBM)
RefSeq chromosome
refseq:NC_######
refseq:NC_000007.14 (chr7)
Common TCGA Project IDs
Cancer
Project ID
NCIt Code
Breast
TCGA-BRCA
NCIT:C4017
Lung adenocarcinoma
TCGA-LUAD
NCIT:C3512
Glioblastoma
TCGA-GBM
NCIT:C3058
Melanoma
TCGA-SKCM
NCIT:C3510
Colorectal
TCGA-COAD
NCIT:C4349
Ovarian
TCGA-OV
NCIT:C4908
Prostate
TCGA-PRAD
NCIT:C7378
Phase 1: Study Selection
GDC_list_projects: No params required. Returns all GDC/TCGA projects with case counts.
Use to browse available projects and map cancer types to project IDs.
GDC_search_cases: project_id (string, e.g., "TCGA-BRCA"), size (int, default 10), offset (int).
Returns case UUIDs and basic metadata.
Use to confirm a project exists and retrieve case counts before deeper queries.
Use to find samples with CNV profiles for a given cancer type.
Progenetix_cnv_search: reference_name (string REQUIRED, RefSeq accession), start (int REQUIRED, GRCh38 1-based), end (int REQUIRED), variant_type ("DUP"/"DEL"), filters (string, NCIt code), limit (int).
Returns biosamples with CNV in the specified genomic region.
variant_type="DUP" for amplification, "DEL" for deletion.
Use filters to restrict to a cancer type.
# EGFR amplifications (chr7:55019017-55211628) in breast cancer
result = tu.tools.Progenetix_cnv_search(
reference_name="refseq:NC_000007.14",
start=55019017, end=55211628,
variant_type="DUP", filters="NCIT:C4017", limit=10
)
Progenetix_list_filtering_terms: No params. Returns all available NCIt codes and labels.
Use when you need to find the NCIt code for a cancer type.
Progenetix_list_cohorts: No params. Returns named cohorts available in Progenetix.
Computational prediction or variant of unknown significance
Low-frequency mutation, no functional data
Interpretation Guidance
Mutation frequency: A gene mutated in >10% of a TCGA cohort is likely a driver candidate (e.g., TP53 in 36% of all TCGA). Mutations at <1% frequency are typically passengers unless they occur at known hotspots. Always cross-reference with OncoKB oncogenicity annotation.
Survival analysis (Kaplan-Meier): A log-rank p-value < 0.05 suggests the gene mutation is associated with differential survival. Hazard ratio (HR) > 1 indicates worse prognosis for the mutated group. Interpret cautiously: TCGA cohorts are retrospective and not treatment-stratified. Small subgroups (n < 20) produce unreliable survival estimates.
Copy number variation: Focal amplifications (narrow peaks) of oncogenes (EGFR, MYC, ERBB2) are more likely functionally relevant than broad arm-level events. Homozygous deletions of tumor suppressors (CDKN2A, PTEN, RB1) are strong loss-of-function signals. DUP count from Progenetix reflects sample frequency, not copy number magnitude.
Synthesis Questions
A complete cancer genomics report should answer:
What are the most frequently mutated genes in this cancer type, and which are known drivers?
Does mutation status of the queried gene associate with survival (p < 0.05)?
Are recurrent CNV events (amplifications or deletions) present at known oncogene/tumor suppressor loci?
What is the OncoKB clinical actionability level for identified variants?
How does the mutation landscape compare across TCGA cancer types (pan-cancer context)?
Programmatic Access (Beyond Tools)
When ToolUniverse tools return truncated results or you need bulk data, use the GDC API directly:
