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.

Key resources:

• cBioPortal website: https://www.cbioportal.org/

• REST API: https://www.cbioportal.org/api/

• API docs (Swagger): https://www.cbioportal.org/api/swagger-ui/index.html

• Python client: bravado or requests

• GitHub: https://github.com/cBioPortal/cbioportal

When to Use This Skill

Use cBioPortal when:

• Mutation landscape: What fraction of a cancer type has mutations in a specific gene?

• Oncogene/TSG validation: Is a gene frequently mutated, amplified, or deleted in cancer?

• Co-mutation patterns: Are mutations in gene A and gene B mutually exclusive or co-occurring?

• Survival analysis: Do mutations in a gene associate with better or worse patient outcomes?

• Alteration profiles: What types of alterations (missense, truncating, amplification, deletion) affect a gene?

• Pan-cancer analysis: Compare alteration frequencies across cancer types

• Clinical associations: Link genomic alterations to clinical variables (stage, grade, treatment response)

• TCGA/GENIE exploration: Systematic access to TCGA and clinical sequencing datasets

Core Capabilities

1. cBioPortal REST API

Base URL: https://www.cbioportal.org/api

The API is RESTful, returns JSON, and requires no API key for public data.

import requests

BASE_URL = "https://www.cbioportal.org/api" HEADERS = {"Accept": "application/json", "Content-Type": "application/json"}

def cbioportal_get(endpoint, params=None): url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers=HEADERS) response.raise_for_status() return response.json()

def cbioportal_post(endpoint, body): url = f"{BASE_URL}/{endpoint}" response = requests.post(url, json=body, headers=HEADERS) response.raise_for_status() return response.json()

2. Browse Studies

def get_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.):

def get_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

def get_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()}")

5. Copy Number Alteration Data

def get_cna(molecular_profile_id, entrez_gene_ids): """Get discrete CNA data (GISTIC: -2, -1, 0, 1, 2).""" body = { "entrezGeneIds": entrez_gene_ids, "sampleListId": molecular_profile_id.replace("_gistic", "_all").replace("_cna", "_all") } return cbioportal_post( f"molecular-profiles/{molecular_profile_id}/discrete-copy-number/fetch", body )

GISTIC values:

-2 = Deep deletion (homozygous loss)

-1 = Shallow deletion (heterozygous loss)

0 = Diploid (neutral)

1 = Low-level gain

2 = High-level amplification

cna_data = get_cna("brca_tcga_gistic", entrez_gene_ids=[1956]) # EGFR df_cna = pd.DataFrame(cna_data) print(df_cna["value"].value_counts())

6. Alteration Frequency (OncoPrint-style)

def get_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 in zip(gene_symbols, entrez_ids):
    mutated = len(set(m["patientId"] for m in mutations if m["entrezGeneId"] == e_id))
    freq[g_symbol] = mutated / total_samples * 100

return freq

Example

freq = get_alteration_frequency("brca_tcga", ["TP53", "PIK3CA", "BRCA1", "BRCA2"]) for gene, pct in sorted(freq.items(), key=lambda x: -x[1]): print(f" {gene}: {pct:.1f}%")

7. Clinical Data

def get_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 vary

def get_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

def alteration_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"])) if not mut_df.empty else 0
n_amp = len(cna_df[cna_df["value"] == 2]) if not cna_df.empty else 0
n_del = len(cna_df[cna_df["value"] == -2]) if not cna_df.empty else 0

return {"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

def pan_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 demo
    try:
        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

def survival_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

Additional Resources

• cBioPortal website: https://www.cbioportal.org/

• API Swagger UI: https://www.cbioportal.org/api/swagger-ui/index.html

• Documentation: https://docs.cbioportal.org/

• GitHub: https://github.com/cBioPortal/cbioportal

• Data hub: https://datahub.cbioportal.org/

• Citation: Cerami E et al. (2012) Cancer Discovery. PMID: 22588877

• API clients: https://docs.cbioportal.org/web-api-and-clients/