Cosmic Database

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

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

Core Mutations

Download comprehensive mutation data including point mutations, indels, and genomic annotations.

Common data types:

• mutations - Complete coding mutations (TSV format)
• mutations_vcf - Coding mutations in VCF format
• sample_info - Sample metadata and tumor information

# Download all coding mutations
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz"
)

Cancer Gene Census

Access the expert-curated list of ~700+ cancer genes with substantial evidence of cancer involvement.

# Download Cancer Gene Census
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/cancer_gene_census.csv"
)

Use cases:

• Identifying known cancer genes
• Filtering variants by cancer relevance
• Understanding gene roles (oncogene vs tumor suppressor)
• Target gene selection for research

Mutational Signatures

Download signature profiles for mutational signature analysis.

# Download signature definitions
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="signatures/signatures.tsv"
)

Signature types:

• Single Base Substitution (SBS) signatures
• Doublet Base Substitution (DBS) signatures
• Insertion/Deletion (ID) signatures

Structural Variants and Fusions

Access gene fusion data and structural rearrangements.

Available data types:

• structural_variants - Structural breakpoints
• fusion_genes - Gene fusion events

# Download gene fusions
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/CosmicFusionExport.tsv.gz"
)

Copy Number and Expression

Retrieve copy number alterations and gene expression data.

Available data types:

• copy_number - Copy number gains/losses
• gene_expression - Over/under-expression data

# Download copy number data
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/CosmicCompleteCNA.tsv.gz"
)

Resistance Mutations

Access drug resistance mutation data with clinical annotations.

# Download resistance mutations
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/CosmicResistanceMutations.tsv.gz"
)

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 is updated quarterly (current version: v102, May 2025)
• Specific versions can be used for reproducibility: v102, v101, etc.

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)