Public PRISM reference data sets
In collaboration with the Broad’s Dependency Map team, public PRISM data has been generated for thousands of commercially available drugs. These repurposing screens are accessible on the DepMap portal, which features 6000 drugs with diverse mechanisms of action and indications. Enter the DepMap portal
Data deliverables
We provide a detailed report for the quality of the assay, viability of all 900 cell lines, feature set correlation analysis, and validation compound reports as well as all the raw data from your test agents. See below for more details. Standard data release includes Collaborator data Report files Summary of analyzed data for each […]
PRISM predictive modeling
Predictive models are run on available feature sets using CCLE genomic data Gene expression data Mutation and copy number data Loss-of-function genetic perturbation data from the Dependency Map Viability data from the PRISM drug repurposing project PRISM identifies the expected biomarkers for validation agents. The example below shows the identification of BRAF as a top […]
PRISM viability assay
PRISM includes validation agents in each screen with known mechanisms of action (MOA). These well-characterized, commercial reagents produce robust, reproducible sensitivity profiles and feature correlations which are used to ensure high cross-screen reproducibility. Here we provide an example of PRISM data for two frequently screened compounds: AZ628, a BRAF inhibitor Imatinib, an inhibitor of PDGFRA […]
PRISM quality control
PRISM provides QC data for every cell line used in the assay. PRISM quantifies the separation of results obtained from the negative control (vehicle only) and positive control (a high-dose of the pan-cytotoxic drug bortezomib for DMSO screens or puromycin for aqueous screens) using error rate and dynamic range. Cell lines that do not meet […]
Data analysis
Barcode abundance is compared to vehicle control conditions to generate relative cell line sensitivity signatures. These sensitivity profiles are then correlated to baseline feature sets using univariate and multivariate analyses to identify features that are associated with drug response.
