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 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