COSMIC Database

Overview

COSMIC (Catalogue of Somatic Mutations in Cancer) is the world's largest and most comprehensive database for exploring somatic mutations in human cancer. Access COSMIC's extensive collection of cancer genomics data, including millions of mutations across thousands of cancer types, curated gene lists, mutational signatures, and clinical annotations programmatically.

When to Use This Skill

This skill should be used when:

• Downloading cancer mutation data from COSMIC
• Accessing the Cancer Gene Census for curated cancer gene lists
• Retrieving mutational signature profiles
• Querying structural variants, copy number alterations, or gene fusions
• Analyzing drug resistance mutations
• Working with cancer cell line genomics data
• Integrating cancer mutation data into bioinformatics pipelines
• Researching specific genes or mutations in cancer contexts

Prerequisites

Account Registration

COSMIC requires authentication for data downloads:

• Academic users: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register
• Commercial users: License required (contact QIAGEN)

Python Requirements

uv pip install requests pandas
# pysam is only needed if you read the VCF-format downloads
uv pip install pysam

Quick Start

1. Basic File Download

Use the scripts/download_cosmic.py script to download COSMIC data files:

from scripts.download_cosmic import download_cosmic_file

# Download mutation data
download_cosmic_file(
    email="your_email@institution.edu",
    password="your_password",
    filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz",
    output_filename="cosmic_mutations.tsv.gz"
)

2. Command-Line Usage

# Download using shorthand data type
python scripts/download_cosmic.py user@email.com --data-type mutations

# Download specific file
python scripts/download_cosmic.py user@email.com \
    --filepath GRCh38/cosmic/latest/cancer_gene_census.csv

# Download for specific genome assembly
python scripts/download_cosmic.py user@email.com \
    --data-type gene_census --assembly GRCh37 -o cancer_genes.csv

3. Working with Downloaded Data

import pandas as pd

# Read mutation data
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')

# Read Cancer Gene Census
gene_census = pd.read_csv('cancer_gene_census.csv')

# Read VCF format
import pysam
vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz')

Available Data Types

Every data type downloads through the same download_cosmic_file(...) call shown in Quick Start — only the filepath changes. Use the --data-type shortcut (CLI) or get_common_file_path(...) (Python) to build the path, or pass the filepath directly. See references/cosmic_data_reference.md for full field descriptions.

Data type	Shortcut	File (GRCh38/cosmic/latest/...)
Coding mutations	mutations	CosmicMutantExport.tsv.gz
Coding mutations (VCF)	mutations_vcf	VCF/CosmicCodingMuts.vcf.gz
Cancer Gene Census	gene_census	cancer_gene_census.csv
Resistance mutations	resistance_mutations	CosmicResistanceMutations.tsv.gz
Structural variants	structural_variants	CosmicStructExport.tsv.gz
Gene fusions	fusion_genes	CosmicFusionExport.tsv.gz
Copy number	copy_number	CosmicCompleteCNA.tsv.gz
Gene expression	gene_expression	CosmicCompleteGeneExpression.tsv.gz
Sample metadata	sample_info	CosmicSample.tsv.gz
Mutational signatures	signatures	signatures/signatures.tsv

Notes:

• Cancer Gene Census is the expert-curated list of cancer genes; use its Role in Cancer field to split oncogenes from tumor suppressors (TSG).
• Mutational signatures cover Single Base Substitution (SBS), Doublet Base Substitution (DBS), and Insertion/Deletion (ID) profiles.
• The signatures path is assembly-independent (no GRCh38/ prefix).

Working with COSMIC Data

Genome Assemblies

COSMIC provides data for two reference genomes:

• GRCh38 (recommended, current standard)
• GRCh37 (legacy, for older pipelines)

Specify the assembly in file paths:

# GRCh38 (recommended)
filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz"

# GRCh37 (legacy)
filepath="GRCh37/cosmic/latest/CosmicMutantExport.tsv.gz"

Versioning

• Use latest in file paths to always get the most recent release
• COSMIC ships roughly one to two releases per year; check the release notes for the current version number rather than assuming it
• For reproducible research, pin an explicit version (e.g. v102) in the filepath instead of latest, and record it alongside your results

File Formats

• TSV/CSV: Tab/comma-separated, gzip compressed, read with pandas
• VCF: Standard variant format, use with pysam, bcftools, or GATK
• All files include headers describing column contents

Common Analysis Patterns

Filter mutations by gene:

import pandas as pd

mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
tp53_mutations = mutations[mutations['Gene name'] == 'TP53']

Identify cancer genes by role:

gene_census = pd.read_csv('cancer_gene_census.csv')
oncogenes = gene_census[gene_census['Role in Cancer'].str.contains('oncogene', na=False)]
tumor_suppressors = gene_census[gene_census['Role in Cancer'].str.contains('TSG', na=False)]

Extract mutations by cancer type:

mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
lung_mutations = mutations[mutations['Primary site'] == 'lung']

Work with VCF files:

import pysam

vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz')
for record in vcf.fetch('17', 7577000, 7579000):  # TP53 region
    print(record.id, record.ref, record.alts, record.info)

Data Reference

For comprehensive information about COSMIC data structure, available files, and field descriptions, see references/cosmic_data_reference.md. This reference includes:

• Complete list of available data types and files
• Detailed field descriptions for each file type
• File format specifications
• Common file paths and naming conventions
• Data update schedule and versioning
• Citation information

Use this reference when:

• Exploring what data is available in COSMIC
• Understanding specific field meanings
• Determining the correct file path for a data type
• Planning analysis workflows with COSMIC data

Helper Functions

The download script includes helper functions for common operations:

Get Common File Paths

from scripts.download_cosmic import get_common_file_path

# Get path for mutations file
path = get_common_file_path('mutations', genome_assembly='GRCh38')
# Returns: 'GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz'

# Get path for gene census
path = get_common_file_path('gene_census')
# Returns: 'GRCh38/cosmic/latest/cancer_gene_census.csv'

The accepted data_type shortcuts are the ones in the Available Data Types table above.

Troubleshooting

Authentication Errors

• Verify email and password are correct
• Ensure account is registered at cancer.sanger.ac.uk/cosmic
• Check if commercial license is required for your use case

File Not Found

• Verify the filepath is correct
• Check that the requested version exists
• Use latest for the most recent version
• Confirm genome assembly (GRCh37 vs GRCh38) is correct

Large File Downloads

• COSMIC files can be several GB in size
• Ensure sufficient disk space
• Download may take several minutes depending on connection
• The script shows download progress for large files

Commercial Use

• Commercial users must license COSMIC through QIAGEN
• Contact: cosmic-translation@sanger.ac.uk
• Academic access is free but requires registration

Integration with Other Tools

COSMIC data integrates well with:

• Variant annotation: VEP, ANNOVAR, SnpEff
• Signature analysis: SigProfiler, deconstructSigs, MuSiCa
• Cancer genomics: cBioPortal, OncoKB, CIViC
• Bioinformatics: Bioconductor, TCGA analysis tools
• Data science: pandas, scikit-learn, PyTorch

Additional Resources

• COSMIC Website: https://cancer.sanger.ac.uk/cosmic
• Documentation: https://cancer.sanger.ac.uk/cosmic/help
• Release Notes: https://cancer.sanger.ac.uk/cosmic/release_notes
• Contact: cosmic@sanger.ac.uk

Citation

When using COSMIC data, cite the current database paper: Sondka Z, Dhir NB, Carvalho-Silva D, et al. COSMIC: a curated database of somatic variants and clinical data for cancer. Nucleic Acids Research. 2024;52(D1):D1210-D1217. doi:10.1093/nar/gkad986