Overview
What It Is
The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. PubMed
The core concept is to identify which genes a given cancer cell line depends on for survival — its "dependencies" — and to link those dependencies to the underlying molecular features of the cell line, enabling the matching of targeted therapies to specific cancer genotypes.
Scale
The DepMap Portal offers results from pooled genome-wide CRISPR-Cas9 screens for over 990 cell lines from multiple cancer types. When combined with the Wellcome Sanger Institute's parallel effort, the integrated datasets encompass dependency profiles of 17,486 genes across 908 cell lines spanning 26 tissues and 42 different cancer types. biorxivnih
Core Data Types
DepMap integrates several major data layers across its cell line models:
1. CRISPR-Cas9 Gene Essentiality Screens (Project Achilles): Genome-wide pooled knockout screens that score every gene's essentiality in each cell line. This is the heart of the dependency map — a score near 0 means the gene is non-essential; a strongly negative score indicates the cell line depends on that gene to survive.
2. RNAi Screens: Earlier loss-of-function screens using RNA interference, now largely superseded by CRISPR but retained for historical context and cross-validation.
3. Drug Sensitivity (PRISM): The PRISM platform employs barcoded multiplexed viability assays and has screened approximately 4,500 compounds — including many non-oncology agents — across hundreds of cell lines, identifying selective vulnerabilities such as the alcohol-aversion drug disulfiram being cytotoxic in certain cancer contexts. PubMed Central
4. Cancer Cell Line Encyclopedia (CCLE) Omics: Each cell line is deeply characterized with whole-exome sequencing (mutations), copy number analysis, RNA-seq (gene expression), DNA methylation, RPPA protein expression, and mass spectrometry-based proteomics.
5. Proteomics: A proteomic dataset contains quantitative profiling of proteins via mass spectrometry, added in the 20Q2 release. More recently, the Olink platform has been used for targeted proteomics on additional cell lines. Uclouvain-cbio
Open Data & Release Cadence
The Cancer Dependency Map project is committed to open science, making all data available to the public under a Creative Commons license, with new datasets released on a quarterly basis ahead of publication via the DepMap portal.
Broad–Sanger Collaboration
DepMap is a joint effort between the Broad Institute and the Wellcome Sanger Institute in the UK. The Cancer Dependency Map is a strategic collaboration between the Broad and Sanger Institutes aimed at accelerating discoveries, with one of its cornerstones being cancer genome-wide CRISPR knockout viability screens in hundreds of cancer cell lines to identify selective genetic dependencies. The two institutes use different but harmonizable protocols, and the combined dataset is the most comprehensive cancer dependency resource available.
Future Directions
The newest iteration of the DepMap Consortium will expand to include underrepresented genotypes, rare cancers, and 3D patient-derived models, using large-scale functional genomics and PRISM therapeutic agent response profiling to create a systematic overview of genetic dependencies and compound sensitivities, along with predictive biomarkers pointing to patient populations most likely to benefit.
Relevance to Ovarian Cancer
DepMap includes a panel of ovarian cancer cell lines (primarily HGSOC, clear cell, and other subtypes) with full CRISPR, drug sensitivity, and omics data, making it a key resource for identifying therapeutic vulnerabilities specific to ovarian cancer genotypes — particularly for rare alterations that are difficult to study in clinical cohorts alone.
Notes
Cell line data only — not primary tumors. CCLE multi-omics and GDSC drug sensitivity fully integrated.