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Updated: Jan 4, 2026
A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells
Published on: July 3, 2013
A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines
Åsmund Flobak1,2, Barbara Niederdorfer3, Vu To Nakstad4
1Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway. asmund.flobak@ntnu.no.
Abstract:
While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations.
Insights
This study presents a new dataset of 171 drug combinations targeting cancer cell signaling pathways. The data reveals synergistic drug combinations that reduce cancer cell viability, offering potential for future cancer therapy research.
Area of Science:
- Oncology
- Pharmacology
- Bioinformatics
Background:
- Accessible datasets for drug combination responses in cancer therapy are limited.
- Drug combinations are crucial for advancing cancer treatment strategies.
Purpose of the Study:
- To create and present a comprehensive dataset of pairwise drug combinations targeting cancer cell signaling pathways.
- To identify synergistic drug combinations that effectively reduce cancer cell viability.
Main Methods:
- Compiled a dataset of 171 pairwise combinations of 19 individual drugs targeting key signal transduction pathways (PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, MAPK).
- Assessed drug and combination effects on cancer cell viability using metabolic activity measurements.
- Classified drug combinations as synergistic using the Bliss independence synergy metric.
Main Results:
- The dataset includes responses for 171 drug combinations across eight cancer cell lines.
- Identified several drug combinations demonstrating synergistic effects in reducing cancer cell viability.
- Provided quantitative data on cell viability reduction for each drug and combination tested.
Conclusions:
- The presented dataset offers valuable insights into synergistic drug combinations for cancer therapy.
- These findings can guide further pre-clinical investigations and the development of novel combination therapies.
- The dataset enhances the availability of public data for cancer drug combination research.
