cosmic-database

COSMIC Somatic Cancer Mutations Database

Overview

COSMIC (Catalogue Of Somatic Mutations In Cancer) is the world's largest expert-curated database of somatic mutations in cancer, covering 6.7M+ coding mutations, 40,000+ cancer samples, 19,000+ genes across all cancer types. It includes the Cancer Gene Census (critical cancer genes), mutational signatures (SBS, DBS, ID), drug resistance variants, copy number data, gene expression, and methylation. The REST API v3.1 enables programmatic queries; most features are freely accessible after registration.

When to Use

• Checking whether a specific somatic variant in a cancer gene is annotated in COSMIC (frequency, cancer type distribution)
• Retrieving all somatic mutations in a gene of interest across COSMIC cancer samples
• Accessing COSMIC Cancer Gene Census classifications (Tier 1/2, role: oncogene/TSG/fusion)
• Looking up mutational signature attributions for samples or cancer types
• Identifying drug resistance variants (pharmacogenomic data) from COSMIC drug resistance database
• Building cancer driver gene lists for bioinformatic pipelines
• For germline/inherited variants use clinvar-database; for drug-target associations use opentargets-database

Prerequisites

• Python packages: requests, pandas
• Data requirements: gene symbols (HGNC), COSMIC mutation IDs (COSM), sample IDs, or genomic coordinates
• Environment: internet connection; free account registration at https://cancer.sanger.ac.uk/cosmic/register
• Rate limits: authenticated requests only; 10 requests/second max; API key required

pip install requests pandas
# Register at https://cancer.sanger.ac.uk/cosmic/register to obtain API credentials

Quick Start

import requests
import base64

# COSMIC API requires base64-encoded email:password authentication
EMAIL = "your_registered@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

# Get mutations for KRAS gene
r = requests.get(f"{BASE}/mutations",
                 headers=HEADERS,
                 params={"gene_name": "KRAS", "limit": 5})
r.raise_for_status()
data = r.json()
print(f"Total KRAS mutations: {data['meta']['total']}")
for m in data["data"][:3]:
    print(f"  {m['mutation_id']:15s} AA: {m.get('mutation_aa')} | Cancer: {m.get('primary_site')}")

Core API

Query 1: Gene Mutations Search

Retrieve all COSMIC somatic mutations for a gene, with cancer type and amino acid change.

import requests, base64, pandas as pd

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

def get_gene_mutations(gene, limit=100, cancer_site=None):
    params = {"gene_name": gene, "limit": limit}
    if cancer_site:
        params["primary_site"] = cancer_site
    r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params)
    r.raise_for_status()
    return r.json()

data = get_gene_mutations("TP53", limit=20)
print(f"Total TP53 mutations in COSMIC: {data['meta']['total']}")

rows = []
for m in data["data"][:10]:
    rows.append({
        "mutation_id": m.get("mutation_id"),
        "mutation_aa": m.get("mutation_aa"),
        "mutation_cds": m.get("mutation_cds"),
        "primary_site": m.get("primary_site"),
        "histology": m.get("primary_histology"),
        "count": m.get("count"),
    })
df = pd.DataFrame(rows)
print(df.head())

# Filter by cancer site
data_lung = get_gene_mutations("TP53", cancer_site="lung", limit=20)
print(f"\nTP53 mutations in lung cancer: {data_lung['meta']['total']}")

Query 2: Cancer Gene Census

Retrieve the COSMIC Cancer Gene Census — classified cancer driver genes.

import requests, base64, pandas as pd

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

r = requests.get(f"{BASE}/genes", headers=HEADERS, params={"limit": 100})
r.raise_for_status()
data = r.json()
print(f"Total genes in COSMIC: {data['meta']['total']}")

# Get Cancer Gene Census genes
r_cgc = requests.get(f"{BASE}/genes",
                     headers=HEADERS,
                     params={"cgc_tier": "1", "limit": 50})
cgc_data = r_cgc.json()
print(f"\nCGC Tier 1 genes: {cgc_data['meta']['total']}")

rows = []
for g in cgc_data["data"][:15]:
    rows.append({
        "gene": g.get("gene_name"),
        "tier": g.get("cgc_tier"),
        "role": g.get("role_in_cancer"),
        "mutation_types": g.get("mutation_types"),
        "tumour_types": str(g.get("tumour_types_somatic", []))[:80],
    })
df = pd.DataFrame(rows)
print(df.to_string(index=False))

Query 3: Specific Mutation Lookup

Retrieve details for a known COSMIC mutation ID (COSM…).

import requests, base64

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

# KRAS G12D mutation
mutation_id = "COSM521"
r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS)
r.raise_for_status()
m = r.json()

print(f"Mutation ID : {m.get('mutation_id')}")
print(f"Gene        : {m.get('gene_name')}")
print(f"AA change   : {m.get('mutation_aa')}")
print(f"CDS change  : {m.get('mutation_cds')}")
print(f"Substitution: {m.get('mutation_description')}")
print(f"Count       : {m.get('count')} samples")
print(f"Cancer types: {str(m.get('cancer_types', []))[:100]}")

Query 4: Sample-Level Mutation Data

Retrieve all somatic mutations for a specific cancer sample.

import requests, base64, pandas as pd

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

# Search for a specific sample
r = requests.get(f"{BASE}/samples",
                 headers=HEADERS,
                 params={"primary_site": "breast", "limit": 5})
r.raise_for_status()
samples = r.json()["data"]
print(f"Example breast cancer samples:")
for s in samples[:3]:
    print(f"  {s.get('sample_id')}: {s.get('sample_name')} | {s.get('primary_histology')}")

# Get mutations for a specific sample
if samples:
    sample_id = samples[0]["sample_id"]
    r2 = requests.get(f"{BASE}/samples/{sample_id}/mutations", headers=HEADERS)
    if r2.ok:
        muts = r2.json()["data"]
        print(f"\nMutations in sample {sample_id}: {len(muts)}")
        for m in muts[:5]:
            print(f"  {m.get('gene_name'):10s} {m.get('mutation_aa')}")

Query 5: Mutational Signatures

Retrieve COSMIC mutational signature data for cancer types.

import requests, base64, pandas as pd

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

# List available mutational signatures
r = requests.get(f"{BASE}/signatures", headers=HEADERS)
r.raise_for_status()
sigs = r.json()["data"]
print(f"COSMIC mutational signatures: {len(sigs)}")
for s in sigs[:5]:
    print(f"  {s.get('signature_name')}: {s.get('aetiology', '')[:80]}")

# Get signature attributions by cancer type
r2 = requests.get(f"{BASE}/signatures/attributions",
                  headers=HEADERS,
                  params={"cancer_type": "Breast", "limit": 10})
if r2.ok:
    attributions = r2.json()["data"]
    for a in attributions[:5]:
        print(f"  {a.get('signature_name')}: {a.get('attribution_proportion'):.2%} in breast cancer")

Query 6: Drug Resistance Variants

Query the COSMIC drug resistance database for variants conferring drug resistance.

import requests, base64, pandas as pd

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

# Get drug resistance variants
r = requests.get(f"{BASE}/resistance_mutations",
                 headers=HEADERS,
                 params={"gene": "EGFR", "limit": 20})
if r.ok:
    data = r.json()
    print(f"EGFR drug resistance variants: {data['meta'].get('total', 'n/a')}")
    for v in data.get("data", [])[:5]:
        print(f"  {v.get('mutation_aa'):20s} Drug: {v.get('drug')} | Resistance: {v.get('resistance_type')}")
else:
    print(f"Drug resistance API: {r.status_code} - endpoint may require specific access level")

Key Concepts

Cancer Gene Census Tiers

COSMIC's Cancer Gene Census classifies genes into:

• Tier 1: Well-established cancer drivers with documented mutations and molecular mechanisms in cancer
• Tier 2: Genes with strong evidence for roles in cancer but less functional characterization

Mutation ID Stability

COSMIC mutation IDs (COSM…) are stable identifiers for specific amino acid changes in a gene. The same COSM ID appears across all samples with that mutation, allowing cross-study comparison.

Common Workflows

Workflow 1: Gene Hotspot Mutation Analysis

Goal: Identify the most frequently occurring somatic mutations in a cancer gene.

import requests, base64, pandas as pd
from collections import Counter

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

def get_all_gene_mutations(gene, max_records=1000):
    """Paginate through all COSMIC mutations for a gene."""
    all_muts = []
    skip = 0
    limit = 200
    while len(all_muts) < max_records:
        r = requests.get(f"{BASE}/mutations",
                         headers=HEADERS,
                         params={"gene_name": gene, "limit": limit, "skip": skip})
        r.raise_for_status()
        batch = r.json()["data"]
        if not batch:
            break
        all_muts.extend(batch)
        total = r.json()["meta"]["total"]
        skip += limit
        if skip >= total:
            break
    return all_muts

# Get hotspots for KRAS
mutations = get_all_gene_mutations("KRAS", max_records=500)
print(f"Retrieved {len(mutations)} KRAS somatic mutations")

# Rank by amino acid change frequency
aa_counter = Counter(m["mutation_aa"] for m in mutations if m.get("mutation_aa"))
hotspots = pd.DataFrame(aa_counter.most_common(15), columns=["mutation_aa", "sample_count"])
print("\nKRAS hotspot mutations:")
print(hotspots.head(10).to_string(index=False))
hotspots.to_csv("KRAS_hotspots.csv", index=False)

Workflow 2: Cancer Gene Census Export

Goal: Export the full Cancer Gene Census as a structured table for downstream pipeline use.

import requests, base64, pandas as pd, time

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

all_genes = []
for tier in [1, 2]:
    skip = 0
    while True:
        r = requests.get(f"{BASE}/genes",
                         headers=HEADERS,
                         params={"cgc_tier": str(tier), "limit": 100, "skip": skip})
        r.raise_for_status()
        batch = r.json()["data"]
        if not batch:
            break
        all_genes.extend(batch)
        if len(batch) < 100:
            break
        skip += 100
        time.sleep(0.1)

rows = [{
    "gene": g.get("gene_name"),
    "tier": g.get("cgc_tier"),
    "role_in_cancer": g.get("role_in_cancer"),
    "mutation_types": g.get("mutation_types"),
    "somatic_tumours": str(g.get("tumour_types_somatic", [])),
    "germline_tumours": str(g.get("tumour_types_germline", [])),
    "chr": g.get("chromosomal_location"),
} for g in all_genes]

df = pd.DataFrame(rows)
df.to_csv("COSMIC_cancer_gene_census.csv", index=False)
print(f"Exported {len(df)} Cancer Gene Census genes → COSMIC_cancer_gene_census.csv")
print(df.groupby("tier")["gene"].count())

Key Parameters

Parameter	Module	Default	Range / Options	Effect
gene_name	Mutations	—	HGNC symbol	Filter mutations by gene
primary_site	Mutations/Samples	—	tissue type string	Filter by primary tumor site
limit	All	10	1–200	Records per page
skip	All	0	integer	Pagination offset
cgc_tier	Genes	—	"1", "2"	Cancer Gene Census tier
mutation_id	Mutations	—	COSM ID string	Lookup specific mutation

Best Practices

1. Authenticate via Base64: COSMIC uses HTTP Basic Auth with base64-encoded email:password. Store credentials in environment variables, not in code.

2. Paginate large gene queries: Popular cancer genes (TP53, KRAS) have 100,000+ mutation records; use skip/limit pagination and cache results locally.

3. Use COSM IDs for cross-study comparison: Amino acid change strings may have formatting variations (p.G12D vs G12D); use COSMIC mutation IDs (COSM…) for unambiguous references.

4. Check data license for commercial use: COSMIC data is free for academic use but requires a commercial license for industry applications. Verify at https://cancer.sanger.ac.uk/cosmic/license.

5. Complement with clinical data: COSMIC captures somatic mutations from cancer sequencing; complement with clinvar-database for germline pathogenicity and opentargets-database for therapeutic significance.

Common Recipes

Recipe: Top Mutated Genes in a Cancer Type

When to use: Identify frequently mutated genes in a specific cancer type.

import requests, base64, pandas as pd
from collections import Counter

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

HEADERS = {"Authorization": f"Basic {token}"}
r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/mutations",
                 headers=HEADERS,
                 params={"primary_site": "lung", "limit": 200})
data = r.json()["data"]
gene_counts = Counter(m.get("gene_name") for m in data if m.get("gene_name"))
df = pd.DataFrame(gene_counts.most_common(10), columns=["gene", "mutations"])
print(df.to_string(index=False))

Recipe: Check if a Variant Is in COSMIC

When to use: Look up whether a specific amino acid change is recorded in COSMIC.

import requests, base64

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

HEADERS = {"Authorization": f"Basic {token}"}
gene = "KRAS"
aa_change = "p.G12D"

r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/mutations",
                 headers=HEADERS,
                 params={"gene_name": gene, "limit": 200})
all_muts = r.json()["data"]
matches = [m for m in all_muts if aa_change in (m.get("mutation_aa") or "")]
print(f"{gene} {aa_change}: {'FOUND' if matches else 'NOT FOUND'} in COSMIC ({len(matches)} records)")
if matches:
    print(f"  Sample count: {sum(m.get('count', 0) for m in matches)}")

Recipe: Download CGC Tier 1 Gene List

When to use: Get a simple list of Tier 1 cancer driver genes for filtering.

import requests, base64

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

HEADERS = {"Authorization": f"Basic {token}"}
r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/genes",
                 headers=HEADERS,
                 params={"cgc_tier": "1", "limit": 200})
genes = [g["gene_name"] for g in r.json()["data"]]
print(f"CGC Tier 1 genes ({len(genes)}): {', '.join(genes[:10])}...")
with open("cosmic_tier1_genes.txt", "w") as f:
    f.write("\n".join(genes))

Troubleshooting

Problem	Cause	Solution
HTTP 401 Unauthorized	Missing or incorrect API credentials	Check base64 encoding: base64.b64encode(f"{email}:{password}".encode())
HTTP 403 Forbidden	Access requires different tier	Some endpoints need commercial license; check COSMIC license page
Empty data array	No records match filter	Broaden query; check spelling of gene symbol or site name
Very slow for large genes	TP53/KRAS have 100K+ records	Paginate with small limit=200; cache results to local CSV
Rate limit errors	>10 req/s	Add time.sleep(0.15) between requests
Different AA notation format	Various mutation string formats	Normalize with RDKit or use COSM IDs for exact matching

Related Skills

• clinvar-database — Germline pathogenicity classifications complementing COSMIC's somatic focus
• opentargets-database — Drug-target associations for COSMIC cancer driver genes
• ensembl-database — Variant consequence predictions (VEP) for COSMIC variants
• gwas-database — Population-level SNP associations for cancer risk (vs. COSMIC's somatic mutations)

References

• COSMIC website — Official COSMIC database and downloads
• COSMIC REST API v3 documentation — API endpoint reference
• Cancer Gene Census — Curated cancer driver gene catalog
• COSMIC mutational signatures (Alexandrov et al. 2020) — Reference paper for COSMIC v3 signatures