alterlab-cbioportal

name	alterlab-cbioportal
description	Query cBioPortal via its keyless REST API for cancer genomics across TCGA, GENIE, MSK-IMPACT and hundreds of studies — somatic mutations, copy-number alterations (GISTIC), mRNA/protein expression, structural variants, and patient-level clinical/survival data. Use when asked how often a gene is mutated/amplified/deleted in a tumor type, to profile oncogenes or tumor suppressors across cancers (pan-cancer alteration frequency), to pull patient-level mutations joined to OS/clinical outcomes, or to validate a cancer target from cohort genomics. For germline variant pathogenicity use alterlab-clinvar; for mutational-signature (SBS) decomposition use alterlab-cosmic; for CRISPR/RNAi gene-dependency use alterlab-depmap; for aggregated target-disease evidence use alterlab-opentargets. Part of the AlterLab Academic Skills suite.
license	LGPL-3.0
allowed-tools	Read WebFetch Bash(curl:*) Bash(python:*)
compatibility	Keyless cBioPortal REST API for public data (no authentication required)
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

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/swagger-ui/index.html
• 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. /genes/fetch takes a plain JSON array of identifiers
    # plus a geneIdType query param; symbols WITHOUT the param resolve to [].
    gene_data = requests.post(
        f"{BASE_URL}/genes/fetch",
        params={"geneIdType": "HUGO_GENE_SYMBOL"},
        json=gene_symbols,
        headers=HEADERS
    ).json()
    # Response order is not guaranteed; map by symbol.
    entrez_by_symbol = {g["hugoGeneSymbol"]: g["entrezGeneId"] for g in gene_data}
    entrez_ids = [entrez_by_symbol[g] for g in gene_symbols if g in entrez_by_symbol]

    # Get mutations
    mutation_profile = f"{study_id}_mutations"
    mutations = get_mutations(mutation_profile, entrez_ids, all_samples_id)

    freq = {}
     g_symbol, e_id  entrez_by_symbol.items():
        mutated = ((m[]  m  mutations  m[] == e_id))
        freq[g_symbol] = mutated / total_samples * 

     freq


freq = get_alteration_frequency(, [, , , ])
 gene, pct  (freq.items(), key= x: -x[]):
    ()

7. Clinical Data

The global /clinical-data/fetch endpoint is POST-only (a GET returns HTTP 405). The simplest path for one study is the per-study GET endpoint, which returns a list of {patientId, studyId, clinicalAttributeId, value} records:

def get_patient_clinical_data(study_id, attribute_ids):
    """Patient-level clinical data via the per-study GET endpoint.

    GET /studies/{studyId}/clinical-data?clinicalDataType=PATIENT&attributeId=...
    accepts a single attributeId, so we query each and concatenate.
    """
    records = []
    for attr in attribute_ids:
        records += cbioportal_get(
            f"studies/{study_id}/clinical-data",
            {"clinicalDataType": "PATIENT", "attributeId": attr, "pageSize": 100000},
        )
    return records

# Clinical attributes include:
# OS_STATUS, OS_MONTHS, DFS_STATUS, DFS_MONTHS (survival)
# AJCC_PATHOLOGIC_TUMOR_STAGE, GRADE, AGE, SEX, RACE
# Study-specific attributes vary — list them with get_clinical_attributes().
# GOTCHA: OS_STATUS / DFS_STATUS are encoded "1:DECEASED" / "0:LIVING"
# (event:label), not bare 0/1 — split on ":" before survival analysis.

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 (plain array body + geneIdType param)
    gene_info = requests.post(
        f"{BASE_URL}/genes/fetch",
        params={"geneIdType": "HUGO_GENE_SYMBOL"},
        json=[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 {: n_mut, : n_amp, : n_del}

result = alteration_profile(, )
(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",
        params={"geneIdType": "HUGO_GENE_SYMBOL"},
        json=[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)

    # Patient-level survival via the per-study GET endpoint (clinical-data/fetch
    # is POST-only — a GET there returns HTTP 405).
    clinical = get_patient_clinical_data(study_id, ["OS_MONTHS", "OS_STATUS"])
    clinical_df = pd.DataFrame(clinical)

    os_wide = clinical_df.pivot(index="patientId", columns="clinicalAttributeId", values="value")
    # OS_STATUS is encoded as "1:DECEASED" / "0:LIVING"; split off the 0/1 event flag.
    if "OS_STATUS" in os_wide:
        os_wide["OS_EVENT"] = os_wide["OS_STATUS"].str.startswith("1").astype("Int64")
    os_wide["OS_MONTHS"] = pd.to_numeric(os_wide.get("OS_MONTHS"), errors="coerce")
    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 /studies/{studyId}/clinical-data	Clinical data for one study (one attributeId per call)
POST /clinical-data/fetch?clinicalDataType=PATIENT	Clinical data across studies (POST-only; GET → 405)
POST /genes/fetch?geneIdType=HUGO_GENE_SYMBOL	Resolve symbols → Entrez IDs (body is a plain JSON array, e.g. ["TP53"])
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 — convert from symbols with POST /genes/fetch?geneIdType=HUGO_GENE_SYMBOL. The body must be a plain JSON array (["TP53","KRAS"]); the object form [{"hugoGeneSymbol":...}] returns HTTP 400, and omitting geneIdType silently returns [] for symbols. Response order is not guaranteed — map results back by hugoGeneSymbol.
• 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://www.cbioportal.org/datasets
• Citation: Cerami E et al. (2012) Cancer Discovery. PMID: 22588877
• API clients: https://docs.cbioportal.org/web-api-and-clients/

Scripts

scripts/query_cbioportal.py — runnable helper for the cBioPortal REST API (public, no key):

python scripts/query_cbioportal.py studies --filter tcga
python scripts/query_cbioportal.py profiles brca_tcga
python scripts/query_cbioportal.py mutations brca_tcga_mutations --genes 7157,672