tooluniverse-cancer-genomics-tcga

# 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
  |
  v
Phase 2: Clinical Data    -- GDC_get_clinical_data
  |
  v
Phase 3: Somatic Mutations -- GDC_get_ssm_by_gene, GDC_get_mutation_frequency
  |
  v
Phase 4: CNV Analysis     -- Progenetix_cnv_search, Progenetix_search_biosamples
  |
  v
Phase 5: Survival Analysis -- GDC_get_survival
  |
  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.

---

## Phase 2: Clinical Data

**GDC_get_clinical_data**: `project_id` (string), `primary_site` (string, e.g., "Breast"), `disease_type` (string), `vital_status` ("Alive" or "Dead"), `gender` ("female"/"male"), `size` (int, 1-100), `offset` (int).
Returns `{status, data: [{case_id, demographics: {gender, race, ethnicity, vital_status, age_at_index}, diagnoses: [{primary_diagnosis, tumor_stage, age_at_diagnosis, days_to_last_follow_up}], treatments: [{therapeutic_agents, treatment_type}]}]}`.
- Use `project_id` + optional filters to retrieve patient-level clinical attributes.
- `age_at_diagnosis` is in days; divide by 365.25 for years.
- Multiple diagnoses or treatments per case are possible.

```python
# Get clinical data for deceased BRCA patients
result = tu.tools.GDC_get_clinical_data(
    project_id="TCGA-BRCA", vital_status="Dead", size=50
)
```

---

## Phase 3: Somatic Mutations

**GDC_get_mutation_frequency**: `gene_symbol` (string REQUIRED, alias: `gene`). Returns pan-cancer SSM occurrence count.
- Returns TOTAL count across all TCGA; no per-project breakdown.
- For cancer-specific data, use `GDC_get_ssm_by_gene` with `project_id`.

**GDC_get_ssm_by_gene**: `gene_symbol` (string REQUIRED), `project_id` (string, optional), `size` (int, 1-100).
Returns `{status, data: [{ssm_id, mutation_type, genomic_dna_change, aa_change, consequence_type}]}`.
- `mutation_type`: "Single base substitution", "Insertion", "Deletion".
- `aa_change`: amino acid change notation (e.g., "Val600Glu").

```python
# TP53 mutations in lung adenocarcinoma
mutations = tu.tools.GDC_get_ssm_by_gene(
    gene_symbol="TP53", project_id="TCGA-LUAD", size=50
)
```

---

## Phase 4: CNV Analysis (Progenetix)

**Progenetix_search_biosamples**: `filters` (string REQUIRED, NCIt code e.g., "NCIT:C4017"), `limit` (int), `skip` (int).
Returns `{status, data: {biosamples: [{biosample_id, histological_diagnosis, pathological_stage, external_references}]}}`.
- 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.

```python
# 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