/depmap

name: depmap
description: Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets.
license: CC-BY-4.0
metadata:
  version: "1.0"
  skill-author: Kuan-lin Huang

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 us