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 when you need to find the NCIt code for a cancer type.

Progenetix_list_cohorts: No params. Returns named cohorts available in Progenetix.

Phase 5: Survival Analysis

GDC_get_survival:

project_id

(string REQUIRED, e.g., "TCGA-BRCA"),

gene_symbol

(string, optional -- filters to mutated cases). Returns

{status, data: {donors: [{id, time, censored, survivalEstimate}], overallStats: {pValue}}}

Each donor has
```
time
```
(days),
```
censored
```
(bool: False=death event, True=censored), and
```
survivalEstimate
```
.
```
overallStats.pValue
```
: log-rank p-value (present when
```
gene_symbol
```
splits cohort).
Without
```
gene_symbol
```
: returns full-cohort survival curve.
With
```
gene_symbol
```
: returns survival split by mutation status (mutated vs. wild-type).

python

# Survival for TCGA-BRCA split by TP53 mutation
surv = tu.tools.GDC_get_survival(project_id="TCGA-BRCA", gene_symbol="TP53")
pval = surv["data"]["overallStats"]["pValue"]

Phase 6: Variant Interpretation (OncoKB)

OncoKB_annotate_variant:

gene

(string, alias

gene_symbol

variant

(string, alias

alteration

, e.g., "V600E"),

tumor_type

(string, OncoTree code e.g., "MEL"). Returns

{status, data: {oncogenic, mutationEffect, highestSensitiveLevel, treatments: [{drugs, level, indication}]}}

```
oncogenic
```
: "Oncogenic", "Likely Oncogenic", "Neutral", "Inconclusive", "Unknown".
```
highestSensitiveLevel
```
: FDA approval level ("LEVEL_1"=FDA-approved, "LEVEL_2"=standard of care, etc.).
Demo mode available for BRAF, TP53, ROS1 without API key.
Set ONCOKB_API_TOKEN for full access.

python

# Annotate KRAS G12C in lung adenocarcinoma
result = tu.tools.OncoKB_annotate_variant(
    gene="KRAS", variant="G12C", tumor_type="LUAD"
)

Tool Quick Reference

Tool	Key Params	Returns
GDC_list_projects	(none)	All TCGA/GDC projects with counts
GDC_search_cases	`project_id` , `size` , `offset`	Case UUIDs + metadata
GDC_get_clinical_data	`project_id` , `vital_status` , `gender` , `size`	Demographics + diagnoses + treatments
GDC_get_mutation_frequency	`gene_symbol` (alias: `gene` )	Pan-cancer SSM count
GDC_get_ssm_by_gene	`gene_symbol` , `project_id` , `size`	Per-mutation records with aa_change
GDC_get_survival	`project_id` , `gene_symbol` (optional)	Kaplan-Meier donor array + pValue
Progenetix_search_biosamples	`filters` (NCIt code), `limit`	Biosample records
Progenetix_cnv_search	`reference_name` , `start` , `end` , `variant_type` , `filters`	Biosamples with CNV in region
Progenetix_list_filtering_terms	(none)	All NCIt codes in Progenetix
OncoKB_annotate_variant	`gene` , `variant` , `tumor_type`	Oncogenicity + treatments

Example Workflows

Workflow 1: Gene-Centric Mutation + Survival Analysis

1. GDC_get_mutation_frequency(gene_symbol="KRAS")
   -> Pan-cancer mutation count

2. GDC_get_ssm_by_gene(gene_symbol="KRAS", project_id="TCGA-LUAD", size=50)
   -> Specific amino acid changes in lung adenocarcinoma

3. GDC_get_survival(project_id="TCGA-LUAD", gene_symbol="KRAS")
   -> Survival split by KRAS mutation status + p-value

4. OncoKB_annotate_variant(gene="KRAS", variant="G12C", tumor_type="LUAD")
   -> Clinical significance + approved therapies (sotorasib)

Workflow 2: Cohort Clinical Summary

1. GDC_list_projects()  -> confirm TCGA-OV exists

2. GDC_get_clinical_data(project_id="TCGA-OV", size=100)
   -> Demographics, tumor stage, treatment history

3. GDC_get_survival(project_id="TCGA-OV")
   -> Baseline overall survival curve for the cohort

Workflow 3: CNV Analysis for a Gene

1. Progenetix_search_biosamples(filters="NCIT:C3058", limit=10)
   -> GBM biosamples with CNV data

2. Progenetix_cnv_search(
       reference_name="refseq:NC_000007.14",
       start=55019017, end=55211628,
       variant_type="DUP", filters="NCIT:C3058"
   )
   -> GBM samples with EGFR amplification

Reasoning Framework

Evidence Grading

Tier	Description	Example
T1	FDA-recognized biomarker with approved therapy	BRAF V600E in melanoma (vemurafenib)
T2	Well-powered clinical study, standard-of-care relevance	KRAS G12C in NSCLC (sotorasib), OncoKB Level 2
T3	Preclinical/small cohort evidence, biological plausibility	Recurrent hotspot in TCGA but no approved therapy
T4	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:

python

import requests, pandas as pd

# Bulk clinical data for a TCGA project
filters = {"op":"and","content":[
    {"op":"=","content":{"field":"project.project_id","value":"TCGA-BRCA"}}
]}
all_cases = []
offset = 0
while True:
    resp = requests.post("https://api.gdc.cancer.gov/cases", json={
        "filters": filters, "size": 500, "from": offset,
        "fields": "submitter_id,demographic.vital_status,demographic.days_to_death,diagnoses.tumor_stage"
    }).json()
    hits = resp["data"]["hits"]
    if not hits: break
    all_cases.extend(hits)
    offset += len(hits)
df = pd.json_normalize(all_cases)

# Download MAF mutation file by UUID
file_uuid = "abc123-..."  # from GDC_list_files result
url = f"https://api.gdc.cancer.gov/data/{file_uuid}"
content = requests.get(url, headers={"Content-Type": "application/json"}).content

# Gene expression: query files endpoint for HTSeq counts
expr_filters = {"op":"and","content":[
    {"op":"=","content":{"field":"cases.project.project_id","value":"TCGA-BRCA"}},
    {"op":"=","content":{"field":"data_type","value":"Gene Expression Quantification"}}
]}

See

tooluniverse-data-wrangling

skill for pagination, error handling, and format parsing patterns.

Limitations

```
GDC_get_survival
```
with
```
gene_symbol
```
splits on mutation presence only; no multi-gene or stage-based stratification.
```
GDC_get_mutation_frequency
```
returns pan-cancer total only; per-cancer frequencies require
```
GDC_get_ssm_by_gene
```
per project.
```
GDC_get_clinical_data
```
returns up to 100 cases per call; use
```
offset
```
for pagination.
Progenetix uses GRCh38 coordinates; provide GRCh38 positions for
```
Progenetix_cnv_search
```
.
```
OncoKB_annotate_variant
```
without ONCOKB_API_TOKEN operates in demo mode (limited to BRAF, TP53, ROS1).
Progenetix
```
filters
```
param requires NCIt CURIE format (e.g., "NCIT:C4017"), not free text.

tooluniverse-cancer-genomics-tcga

NPX Install

Tags

SKILL.md Content

Cancer Genomics / TCGA Analysis

When to Use

NOT for (use other skills instead)

Workflow Overview

Key Identifiers

Common TCGA Project IDs

Phase 1: Study Selection

Phase 2: Clinical Data

Phase 3: Somatic Mutations

Phase 4: CNV Analysis (Progenetix)

Phase 5: Survival Analysis

Phase 6: Variant Interpretation (OncoKB)

Tool Quick Reference

Example Workflows

Workflow 1: Gene-Centric Mutation + Survival Analysis

Workflow 2: Cohort Clinical Summary

Workflow 3: CNV Analysis for a Gene

Reasoning Framework

Evidence Grading

Interpretation Guidance

Synthesis Questions

Programmatic Access (Beyond Tools)

Limitations