Query cBioPortal for cancer genomics data including somatic mutations, copy number alterations, gene expression, and survival data across hundreds of cancer studies. Essential for cancer target validation, oncogene/tumor suppressor analysis, and patient-level genomic profiling.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Query cBioPortal for cancer genomics data including somatic mutations, copy number alterations, gene expression, and survival data across hundreds of cancer studies. Essential for cancer target validation, oncogene/tumor suppressor analysis, and patient-level genomic profiling.
license
LGPL-3.0
metadata
{"skill-author":"Kuan-lin Huang"}
cBioPortal Database
Overview
cBioPortal for Cancer Genomics (https://www.cbioportal.org/) is an open-access resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. It hosts data from The Cancer Genome Atlas (TCGA), AACR Project GENIE, MSK-IMPACT, and hundreds of other cancer studies — covering mutations, copy number alterations (CNA), structural variants, mRNA/protein expression, methylation, and clinical data for thousands of cancer samples.
defget_all_studies():
"""List all available cancer studies."""return cbioportal_get("studies", {"pageSize": 500})
# Each study has:# studyId: unique identifier (e.g., "brca_tcga")# name: human-readable name# description: dataset description# cancerTypeId: cancer type abbreviation# referenceGenome: GRCh37 or GRCh38# pmid: associated publication
studies = get_all_studies()
print(f"Total studies: {len(studies)}")
# Common TCGA study IDs:# brca_tcga, luad_tcga, coadread_tcga, gbm_tcga, prad_tcga,# skcm_tcga, blca_tcga, hnsc_tcga, lihc_tcga, stad_tcga# Filter for TCGA studies
tcga_studies = [s for s in studies if"tcga"in s["studyId"]]
print([s["studyId"] for s in tcga_studies[:10]])
3. Molecular Profiles
Each study has multiple molecular profiles (mutation, CNA, expression, etc.):
defget_molecular_profiles(study_id):
"""Get all molecular profiles for a study."""return cbioportal_get(f"studies/{study_id}/molecular-profiles")
profiles = get_molecular_profiles("brca_tcga")
for p in profiles:
print(f" {p['molecularProfileId']}: {p['name']} ({p['molecularAlterationType']})")
# Alteration types:# MUTATION_EXTENDED — somatic mutations# COPY_NUMBER_ALTERATION — CNA (GISTIC)# MRNA_EXPRESSION — mRNA expression# PROTEIN_LEVEL — RPPA protein expression# STRUCTURAL_VARIANT — fusions/rearrangements
4. Mutation Data
defget_mutations(molecular_profile_id, entrez_gene_ids, sample_list_id=None):
"""Get mutations for specified genes in a molecular profile."""
body = {
"entrezGeneIds": entrez_gene_ids,
"sampleListId": sample_list_id or molecular_profile_id.replace("_mutations", "_all")
}
return cbioportal_post(
f"molecular-profiles/{molecular_profile_id}/mutations/fetch",
body
)
# BRCA1 Entrez ID is 672, TP53 is 7157, PTEN is 5728
mutations = get_mutations("brca_tcga_mutations", entrez_gene_ids=[7157]) # TP53# Each mutation record contains:# patientId, sampleId, entrezGeneId, gene.hugoGeneSymbol# mutationType (Missense_Mutation, Nonsense_Mutation, Frame_Shift_Del, etc.)# proteinChange (e.g., "R175H")# variantClassification, variantType# ncbiBuild, chr, startPosition, endPosition, referenceAllele, variantAllele# mutationStatus (Somatic/Germline)# alleleFreqT (tumor VAF)import pandas as pd
df = pd.DataFrame(mutations)
print(df[["patientId", "mutationType", "proteinChange", "alleleFreqT"]].head())
print(f"\nMutation types:\n{df['mutationType'].value_counts()}")
defget_alteration_frequency(study_id, gene_symbols, alteration_types=None):
"""Compute alteration frequencies for genes across a cancer study."""import requests, pandas as pd
# Get sample list
samples = requests.get(
f"{BASE_URL}/studies/{study_id}/sample-lists",
headers=HEADERS
).json()
all_samples_id = next(
(s["sampleListId"] for s in samples if s["category"] == "all_cases_in_study"), None
)
total_samples = len(requests.get(
f"{BASE_URL}/sample-lists/{all_samples_id}/sample-ids",
headers=HEADERS
).json())
# Get gene Entrez IDs
gene_data = requests.post(
f"{BASE_URL}/genes/fetch",
json=[{"hugoGeneSymbol": g} for g in gene_symbols],
headers=HEADERS
).json()
entrez_ids = [g["entrezGeneId"] for g in gene_data]
# Get mutations
mutation_profile = f"{study_id}_mutations"
mutations = get_mutations(mutation_profile, entrez_ids, all_samples_id)
freq = {}
for g_symbol, e_id inzip(gene_symbols, entrez_ids):
mutated = len(set(m["patientId"] for m in mutations if m["entrezGeneId"] == e_id))
freq[g_symbol] = mutated / total_samples * 100return freq
# Example
freq = get_alteration_frequency("brca_tcga", ["TP53", "PIK3CA", "BRCA1", "BRCA2"])
for gene, pct insorted(freq.items(), key=lambda x: -x[1]):
print(f" {gene}: {pct:.1f}%")
7. Clinical Data
defget_clinical_data(study_id, attribute_ids=None):
"""Get patient-level clinical data."""
params = {"studyId": study_id}
all_clinical = cbioportal_get(
"clinical-data/fetch",
params
)
# Returns list of {patientId, studyId, clinicalAttributeId, value}# Clinical attributes include:# OS_STATUS, OS_MONTHS, DFS_STATUS, DFS_MONTHS (survival)# TUMOR_STAGE, GRADE, AGE, SEX, RACE# Study-specific attributes varydefget_clinical_attributes(study_id):
"""List all available clinical attributes for a study."""return cbioportal_get(f"studies/{study_id}/clinical-attributes")
Query Workflows
Workflow 1: Gene Alteration Profile in a Cancer Type
import requests, pandas as pd
defalteration_profile(study_id, gene_symbol):
"""Full alteration profile for a gene in a cancer study."""# 1. Get gene Entrez ID
gene_info = requests.post(
f"{BASE_URL}/genes/fetch",
json=[{"hugoGeneSymbol": gene_symbol}],
headers=HEADERS
).json()[0]
entrez_id = gene_info["entrezGeneId"]
# 2. Get mutations
mutations = get_mutations(f"{study_id}_mutations", [entrez_id])
mut_df = pd.DataFrame(mutations) if mutations else pd.DataFrame()
# 3. Get CNAs
cna = get_cna(f"{study_id}_gistic", [entrez_id])
cna_df = pd.DataFrame(cna) if cna else pd.DataFrame()
# 4. Summary
n_mut = len(set(mut_df["patientId"])) ifnot mut_df.empty else0
n_amp = len(cna_df[cna_df["value"] == 2]) ifnot cna_df.empty else0
n_del = len(cna_df[cna_df["value"] == -2]) ifnot cna_df.empty else0return {"mutations": n_mut, "amplifications": n_amp, "deep_deletions": n_del}
result = alteration_profile("brca_tcga", "PIK3CA")
print(result)
Workflow 2: Pan-Cancer Gene Mutation Frequency
import requests, pandas as pd
defpan_cancer_mutation_freq(gene_symbol, cancer_study_ids=None):
"""Mutation frequency of a gene across multiple cancer types."""
studies = get_all_studies()
if cancer_study_ids:
studies = [s for s in studies if s["studyId"] in cancer_study_ids]
results = []
for study in studies[:20]: # Limit for demotry:
freq = get_alteration_frequency(study["studyId"], [gene_symbol])
results.append({
"study": study["studyId"],
"cancer": study.get("cancerTypeId", ""),
"mutation_pct": freq.get(gene_symbol, 0)
})
except Exception:
pass
df = pd.DataFrame(results).sort_values("mutation_pct", ascending=False)
return df
Workflow 3: Survival Analysis by Mutation Status
import requests, pandas as pd
defsurvival_by_mutation(study_id, gene_symbol):
"""Get survival data split by mutation status."""# This workflow fetches clinical and mutation data for downstream analysis
gene_info = requests.post(
f"{BASE_URL}/genes/fetch",
json=[{"hugoGeneSymbol": gene_symbol}],
headers=HEADERS
).json()[0]
entrez_id = gene_info["entrezGeneId"]
mutations = get_mutations(f"{study_id}_mutations", [entrez_id])
mutated_patients = set(m["patientId"] for m in mutations)
clinical = cbioportal_get("clinical-data/fetch", {"studyId": study_id})
clinical_df = pd.DataFrame(clinical)
os_data = clinical_df[clinical_df["clinicalAttributeId"].isin(["OS_MONTHS", "OS_STATUS"])]
os_wide = os_data.pivot(index="patientId", columns="clinicalAttributeId", values="value")
os_wide["mutated"] = os_wide.index.isin(mutated_patients)
return os_wide
Key API Endpoints Summary
Endpoint
Description
GET /studies
List all studies
GET /studies/{studyId}/molecular-profiles
Molecular profiles for a study
POST /molecular-profiles/{profileId}/mutations/fetch
Get mutation data
POST /molecular-profiles/{profileId}/discrete-copy-number/fetch
Get CNA data
POST /molecular-profiles/{profileId}/molecular-data/fetch
Get expression data
GET /studies/{studyId}/clinical-attributes
Available clinical variables
GET /clinical-data/fetch
Clinical data
POST /genes/fetch
Gene metadata by symbol or Entrez ID
GET /studies/{studyId}/sample-lists
Sample lists
Best Practices
Know your study IDs: Use the Swagger UI or GET /studies to find the correct study ID
Use sample lists: Each study has an all sample list and subsets; always specify the appropriate one
TCGA vs. GENIE: TCGA data is comprehensive but older; GENIE has more recent clinical sequencing data
Entrez gene IDs: The API uses Entrez IDs — use /genes/fetch to convert from symbols
Handle 404s: Some molecular profiles may not exist for all studies
Rate limiting: Add delays for bulk queries; consider downloading data files for large-scale analyses
Data Downloads
For large-scale analyses, download study data directly:
# Download TCGA BRCA data
wget https://cbioportal-datahub.s3.amazonaws.com/brca_tcga.tar.gz
