DepMap — Cancer Dependency Map

概述

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/

使用時機

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)?

核心概念

Dependency Scores

Effect-size heuristics, not significance thresholds:

• Chronos ≤ −0.5 is a common exploratory filter; its error rate depends on screen quality.
• Chronos ≈ −1 is a normalization anchor, not a universal dependency boundary.

Use the selected release's documented dependency probability/FDR or hit-calling statistics when making binary calls; record their direction and cutoff separately from gene effect. Do not interpret a gene-effect value as a probability or p-value.

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

1. Download CRISPRGeneEffect.csv and sample_info.csv
2. Filter cell lines by cancer type
3. Compute mean gene effect for target gene in cancer vs. all others
4. Calculate selectivity: how specific is the dependency to your cancer type?
5. Cross-reference with mutation, expression, or CNA data as biomarkers

Workflow 2: Synthetic Lethality Screen

1. Identify cell lines with mutation/deletion in gene of interest (e.g., BRCA1-mutant)
2. Compute gene effect scores for all genes in mutant vs. WT lines
3. Identify candidate associations with more negative effect in mutant lines; control lineage/confounding and multiple testing before calling significance
4. Filter by selectivity and effect size; validate candidate synthetic lethality with controlled perturbation experiments

Workflow 3: Compound Sensitivity Analysis

1. Download PRISM compound sensitivity data (primary-screen-replicate-treatment-info.csv)
2. Correlate compound AUC/log2(fold-change) with genomic features
3. Identify predictive biomarkers for compound sensitivity

DepMap Data Files Reference

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

最佳實踐

• 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

延伸資源

• 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