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.

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 2. Gene Dependency Scores 3. Download Based Analysis (Recommended for Large Queries) For large scale analysis, download DepMap data files and analyze locally: 4. Identifying Selective Dependencies 5. Biomarker Analysis (Gene Effect vs. Mutation) 6. Co Essentiality Analysis 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 genes significantly more essential in mutant lines (synthetic lethal partners) 4. Filter by selectivity and effect size 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 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