depmap

DepMap — Cancer Dependency Map Query Tool

Skill Overview

The DepMap skill helps researchers query gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles in cancer cell lines. It is used to identify cancer-specific vulnerabilities, discover synthetic lethal interactions, and validate therapeutic targets in tumors.

Use Cases

1. Anticancer Drug Target Validation

When you need to determine whether a gene is essential in cell lines from a specific cancer type, DepMap can provide dependency scores for that gene across hundreds of cancer cell lines. For example, you can query the dependency of the KRAS gene in lung cancer cell lines to assess whether it is a suitable therapeutic target.

2. Synthetic Lethal Gene Pair Screening

When you want to identify genes that have a synthetic lethal relationship with a particular mutated gene, such as BRCA1, DepMap can compare gene dependency between mutant and wild-type cell lines to identify selectively essential genes, providing candidate targets for precision medicine.

3. Biomarker Discovery

When you need to determine which genomic features—such as mutations, expression levels, or copy-number alterations—can predict cell-line sensitivity to a specific gene knockout or compound, DepMap’s multi-omics data and biomarker analysis capabilities can help identify predictive biomarkers.

Core Functions

1. Gene Dependency Score Queries

Through the DepMap API or downloaded data files, obtain CRISPR Chronos scores, RNAi DEMETER2 scores, or standardized gene effect values for any gene across all cancer cell lines. Results can be filtered by cancer type and cell-line characteristics, helping quickly identify cell lines that depend on the gene.

2. Selective Dependency Identification

Using a Chronos score threshold, such as ≤ -0.5, automatically screen for cell lines that depend on the target gene and associate them with metadata such as cancer type and lineage. The selectivity of the gene across different cancer types can then be analyzed to distinguish pan-essential genes from cancer-selective essential genes.

3. Biomarker Association Analysis

Use statistical tests, such as the Mann–Whitney U test, to evaluate whether specific gene mutations are significantly associated with dependency on the target gene and to identify predictive biomarkers. Gene co-essentiality correlation analysis is also supported to identify functionally related gene modules.

Frequently Asked Questions

How should DepMap Chronos scores be interpreted?

Chronos scores generally range from approximately -3 to above 0. More negative values indicate that a gene is more essential. In general, a Chronos score ≤ -0.5 suggests possible dependency, while a score ≤ -1 indicates strong dependency, within the range commonly observed for essential genes. Standardized gene effect values use -1 as the median effect for commonly essential genes, facilitating comparisons across datasets.

How can DepMap be used to validate anticancer drug targets?

First, download the CRISPRGeneEffect.csv and sample_info.csv files and load them into Pandas DataFrames. Then filter for cell lines from the target cancer type, calculate the average gene effect of the target gene within that cancer type and across other cancer types, and evaluate selectivity and effect strength. Finally, integrate mutation, expression, and other data to identify predictive biomarkers.

What types of cancer vulnerabilities can DepMap help discover?

DepMap primarily helps identify three types of vulnerabilities: (1) cancer-selective dependencies—genes that are essential in a specific cancer type but not in others; (2) synthetic lethal interactions—genes that become essential only in the context of a particular gene mutation; and (3) sensitivity predicted by biomarkers—responses to drugs or gene knockouts predicted based on genomic features.