PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies

Abstract

Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.

Figure 1.

Main functionalities of PharmacoDB, displaying (A) the interfaces to query the database through searching or exploring available entities, (B) the five primary data types with respective profile pages, (C) the main visualizations of the aggregated data in PharmacoDB and (D) the link to PharmacoGx for extensive computational analysis of pharmacogenomic data.

Figure 2.

The total number of identifier matches between pairs of datasets for (A) cell lines and (B) compounds before curation, after automatically removing differences in capitalization and white space and manually reviewing matches within edit distance of 2, and after undergoing subsequent manual curation.

Figure 3.

An example profile page from PharmacoDB for a cell line. The page is organized so that the left column contains textual information, and the right column contains plots. Panel (A) is the information card for the cell line (MCF7). Panel (B) contains tables listing the available data profiles for this cell. Panel (C) contains summary plots about the drug screening performed in each dataset and the waterfall plots of the cell response to treatment with compounds.

Figure 4.

An example of drug dose response curve plot with (A) the A549 lung cancer cell line treated with the MEK inhibitor AZD6244, and (B) the corresponding table of summary statistics. IC₅₀: dose of 50% inhibition of cell viability; EC₅₀: dose at which 50% of the maximum response is observed; AAC: area above curve; E_inf: maximum theoretical inhibition; DSS: drug sensitivity score.