DepMap — Cancer Dependency Map

Overview

The Cancer Dependency Map (DepMap) project, run by the Broad Institute, systematically characterizes genetic dependencies across hundreds of cancer cell lines using genome-wide CRISPR knockout screens (DepMap CRISPR), RNA interference (RNAi), and compound sensitivity assays (PRISM). DepMap data is essential for:

• Identifying which genes are essential for specific cancer types

• Finding cancer-selective dependencies (therapeutic targets)

• Validating oncology drug targets

• Discovering synthetic lethal interactions

Key resources:

• DepMap Portal: https://depmap.org/portal/

• DepMap data downloads: https://depmap.org/portal/download/all/

• Python package: depmap (or access via API/downloads)

• API: https://depmap.org/portal/api/

When to Use This Skill

Use DepMap when:

• Target validation: Is a gene essential for survival in cancer cell lines with a specific mutation (e.g., KRAS-mutant)?

• Biomarker discovery: What genomic features predict sensitivity to knockout of a gene?

• Synthetic lethality: Find genes that are selectively essential when another gene is mutated/deleted

• Drug sensitivity: What cell line features predict response to a compound?

• Pan-cancer essentiality: Is a gene broadly essential across all cancer types (bad target) or selectively essential?

• Correlation analysis: Which pairs of genes have correlated dependency profiles (co-essentiality)?

Core Concepts

Dependency Scores

Score Range Meaning

Chronos (CRISPR) ~ -3 to 0+ More negative = more essential. Common essential threshold: −1. Pan-essential genes ~−1 to −2

RNAi DEMETER2 ~ -3 to 0+ Similar scale to Chronos

Gene Effect normalized Normalized Chronos; −1 = median effect of common essential genes

Key thresholds:

• Chronos ≤ −0.5: likely dependent

• Chronos ≤ −1: strongly dependent (common essential range)

Cell Line Annotations

Each cell line has:

• DepMap_ID : unique identifier (e.g., ACH-000001 )

• cell_line_name : human-readable name

• primary_disease : cancer type

• lineage : broad tissue lineage

• lineage_subtype : specific subtype

Core Capabilities

1. DepMap API

import requests import pandas as pd

BASE_URL = "https://depmap.org/portal/api"

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

2. Gene Dependency Scores

def get_gene_dependency(gene_symbol, dataset="Chronos_Combined"): """Get CRISPR dependency scores for a gene across all cell lines.""" url = f"{BASE_URL}/gene" params = { "gene_id": gene_symbol, "dataset": dataset } response = requests.get(url, params=params) return response.json()

Alternatively, use the /data endpoint:

def get_dependencies_slice(gene_symbol, dataset_name="CRISPRGeneEffect"): """Get a gene's dependency slice from a dataset.""" url = f"{BASE_URL}/data/gene_dependency" params = {"gene_name": gene_symbol, "dataset_name": dataset_name} response = requests.get(url, params=params) data = response.json() return data

3. Download-Based Analysis (Recommended for Large Queries)

For large-scale analysis, download DepMap data files and analyze locally:

import pandas as pd import requests, os

def download_depmap_data(url, output_path): """Download a DepMap data file.""" response = requests.get(url, stream=True) with open(output_path, 'wb') as f: for chunk in response.iter_content(chunk_size=8192): f.write(chunk)

DepMap 24Q4 data files (update version as needed)

FILES = { "crispr_gene_effect": "https://figshare.com/ndownloader/files/...", # OR download from: https://depmap.org/portal/download/all/ # Files available: # CRISPRGeneEffect.csv - Chronos gene effect scores # OmicsExpressionProteinCodingGenesTPMLogp1.csv - mRNA expression # OmicsSomaticMutationsMatrixDamaging.csv - mutation binary matrix # OmicsCNGene.csv - copy number # sample_info.csv - cell line metadata }

def load_depmap_gene_effect(filepath="CRISPRGeneEffect.csv"): """ Load DepMap CRISPR gene effect matrix. Rows = cell lines (DepMap_ID), Columns = genes (Symbol (EntrezID)) """ df = pd.read_csv(filepath, index_col=0) # Rename columns to gene symbols only df.columns = [col.split(" ")[0] for col in df.columns] return df

def load_cell_line_info(filepath="sample_info.csv"): """Load cell line metadata.""" return pd.read_csv(filepath)

4. Identifying Selective Dependencies

import numpy as np import pandas as pd

def find_selective_dependencies(gene_effect_df, cell_line_info, target_gene, cancer_type=None, threshold=-0.5): """Find cell lines selectively dependent on a gene."""

# Get scores for target gene
if target_gene not in gene_effect_df.columns:
    return None

scores = gene_effect_df[target_gene].dropna()
dependent = scores[scores <= threshold]

# Add cell line info
result = pd.DataFrame({
    "DepMap_ID": dependent.index,
    "gene_effect": dependent.values
}).merge(cell_line_info[["DepMap_ID", "cell_line_name", "primary_disease", "lineage"]])

if cancer_type:
    result = result[result["primary_disease"].str.contains(cancer_type, case=False, na=False)]

return result.sort_values("gene_effect")

Example usage (after loading data)

df_effect = load_depmap_gene_effect("CRISPRGeneEffect.csv")

cell_info = load_cell_line_info("sample_info.csv")

deps = find_selective_dependencies(df_effect, cell_info, "KRAS", cancer_type="Lung")

5. Biomarker Analysis (Gene Effect vs. Mutation)

import pandas as pd from scipy import stats

def biomarker_analysis(gene_effect_df, mutation_df, target_gene, biomarker_gene): """ Test if mutation in biomarker_gene predicts dependency on target_gene.

Args:
    gene_effect_df: CRISPR gene effect DataFrame
    mutation_df: Binary mutation DataFrame (1 = mutated)
    target_gene: Gene to assess dependency of
    biomarker_gene: Gene whose mutation may predict dependency
"""
if target_gene not in gene_effect_df.columns or biomarker_gene not in mutation_df.columns:
    return None

# Align cell lines
common_lines = gene_effect_df.index.intersection(mutation_df.index)
scores = gene_effect_df.loc[common_lines, target_gene].dropna()
mutations = mutation_df.loc[scores.index, biomarker_gene]

mutated = scores[mutations == 1]
wt = scores[mutations == 0]

stat, pval = stats.mannwhitneyu(mutated, wt, alternative='less')

return {
    "target_gene": target_gene,
    "biomarker_gene": biomarker_gene,
    "n_mutated": len(mutated),
    "n_wt": len(wt),
    "mean_effect_mutated": mutated.mean(),
    "mean_effect_wt": wt.mean(),
    "pval": pval,
    "significant": pval < 0.05
}

6. Co-Essentiality Analysis

import pandas as pd

def co_essentiality(gene_effect_df, target_gene, top_n=20): """Find genes with most correlated dependency profiles (co-essential partners).""" if target_gene not in gene_effect_df.columns: return None

target_scores = gene_effect_df[target_gene].dropna()

correlations = {}
for gene in gene_effect_df.columns:
    if gene == target_gene:
        continue
    other_scores = gene_effect_df[gene].dropna()
    common = target_scores.index.intersection(other_scores.index)
    if len(common) < 50:
        continue
    r = target_scores[common].corr(other_scores[common])
    if not pd.isna(r):
        correlations[gene] = r

corr_series = pd.Series(correlations).sort_values(ascending=False)
return corr_series.head(top_n)

Co-essential genes often share biological complexes or pathways

Query Workflows

Workflow 1: Target Validation for a Cancer Type

• Download CRISPRGeneEffect.csv and sample_info.csv

• Filter cell lines by cancer type

• Compute mean gene effect for target gene in cancer vs. all others

• Calculate selectivity: how specific is the dependency to your cancer type?

• Cross-reference with mutation, expression, or CNA data as biomarkers

Workflow 2: Synthetic Lethality Screen

• Identify cell lines with mutation/deletion in gene of interest (e.g., BRCA1-mutant)

• Compute gene effect scores for all genes in mutant vs. WT lines

• Identify genes significantly more essential in mutant lines (synthetic lethal partners)

• Filter by selectivity and effect size

Workflow 3: Compound Sensitivity Analysis

• Download PRISM compound sensitivity data (primary-screen-replicate-treatment-info.csv )

• Correlate compound AUC/log2(fold-change) with genomic features

• Identify predictive biomarkers for compound sensitivity

DepMap Data Files Reference

File Description

CRISPRGeneEffect.csv

CRISPR Chronos gene effect (primary dependency data)

CRISPRGeneEffectUnscaled.csv

Unscaled CRISPR scores

RNAi_merged.csv

DEMETER2 RNAi dependency

sample_info.csv

Cell line metadata (lineage, disease, etc.)

OmicsExpressionProteinCodingGenesTPMLogp1.csv

mRNA expression

OmicsSomaticMutationsMatrixDamaging.csv

Damaging somatic mutations (binary)

OmicsCNGene.csv

Copy number per gene

PRISM_Repurposing_Primary_Screens_Data.csv

Drug sensitivity (repurposing library)

Download all files from: https://depmap.org/portal/download/all/

Best Practices

• Use Chronos scores (not DEMETER2) for current CRISPR analyses — better controlled for cutting efficiency

• Distinguish pan-essential from cancer-selective: Target genes with low variance (essential in all lines) are poor drug targets

• Validate with expression data: A gene not expressed in a cell line will score as non-essential regardless of actual function

• Use DepMap ID for cell line identification — cell_line_name can be ambiguous

• Account for copy number: Amplified genes may appear essential due to copy number effect (junk DNA hypothesis)

• Multiple testing correction: When computing biomarker associations genome-wide, apply FDR correction

Additional Resources

• DepMap Portal: https://depmap.org/portal/

• Data downloads: https://depmap.org/portal/download/all/

• DepMap paper: Behan FM et al. (2019) Nature. PMID: 30971826

• Chronos paper: Dempster JM et al. (2021) Nature Methods. PMID: 34349281

• GitHub: https://github.com/broadinstitute/depmap-portal

• Figshare: https://figshare.com/articles/dataset/DepMap_24Q4_Public/27993966