Related Experiment Video
Updated: Apr 26, 2026

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis
Published on: December 23, 2022
Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets
Qiaonan Duan1, Zichen Wang1, Nicolas F Fernandez1
1Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY 10029 and Center for Cancer Research, Massachusetts General Hospital Cancer Center, Harvard Medical School, Charlestown, MA 02129, USA.
Summary:
Recently, several high profile studies collected cell viability data from panels of cancer cell lines treated with many drugs applied at different concentrations. Such drug sensitivity data for cancer cell lines provide suggestive treatments for different types and subtypes of cancer. Visualization of these datasets can reveal patterns that may not be obvious by examining the data without such efforts. Here we introduce Drug/Cell-line Browser (DCB), an online interactive HTML5 data visualization tool for interacting with three of the recently published datasets of cancer cell lines/drug-viability studies. DCB uses clustering and canvas visualization of the drugs and the cell lines, as well as a bar graph that summarizes drug effectiveness for the tissue of origin or the cancer subtypes for single or multiple drugs. DCB can help in understanding drug response patterns and prioritizing drug/cancer cell line interactions by tissue of origin or cancer subtype.
Availability And Implementation:
DCB is an open source Web-based tool that is freely available at: http://www.maayanlab.net/LINCS/DCB CONTACT: avi.maayan@mssm.edu
Supplementary Information:
Supplementary data are available at Bioinformatics online.
More Related Videos
09:13Author Spotlight: Developing Multiplexed Kinetic Assays for Organoid-Based Drug Response Analysis
Published on: January 5, 2024
13:34A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016