A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

@article{Flobak2019AHD,
  title={A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines},
  author={{\AA}smund Flobak and Barbara Niederdorfer and Vu To Nakstad and Liv Thommesen and Geir Klinkenberg and Astrid L{\ae}greid},
  journal={Scientific Data},
  year={2019},
  volume={6}
}
AbstractWhile 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… 
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References

SHOWING 1-10 OF 44 REFERENCES
Landscape of Targeted Anti-Cancer Drug Synergies in Melanoma Identifies a Novel BRAF-VEGFR/PDGFR Combination Treatment
TLDR
It is found that even the most synergistic drug pairs are effective only in a discrete number of cell lines, underlying a strong context dependency for synergy, with strong, widespread synergies often corresponding to non-specific or off-target drug effects such as multidrug resistance protein 1 (MDR1) transporter inhibition.
Dissecting mechanisms of resistance to targeted drug combination therapy in human colorectal cancer
TLDR
It is found that the addition of low concentrations of the BCL2-family inhibitor navitoclax to the MEK–PI3K inhibitor regimen improves the synergistic interaction and blocks the acquisition of resistance.
An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies
TLDR
An example of one such novel combination, a Wee1 inhibitor (AZD1775) and an mTOR inhibitor (ridaforolimus) is presented, and it is demonstrated that the combination potently and synergistically inhibits cancer cell growth in vitro and in vivo.
The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity.
TLDR
A systematic evaluation of the therapeutic activity of over 5,000 pairs of FDA-approved cancer drugs against a panel of 60 well-characterized human tumor cell lines to uncover combinations with greater than additive growth-inhibitory activity demonstrated value in identifying promising combinations of approved drugs with potent anticancer activity.
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
TLDR
A large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers are reported.
Patient-derived models of acquired resistance can identify effective drug combinations for cancer
TLDR
A pharmacogenomic platform is described that facilitates rapid discovery of drug combinations that can overcome resistance to targeted cancer therapies and could help direct therapeutic choices for individual patients.
Rational development of synergistic combinations of chemotherapy and molecular targeted agents for colorectal cancer treatment
TLDR
Analysis of chemotherapy-induced phosphokinome changes helps to elucidate the mechanisms of drug resistance and to guide the selection of targets for combination therapies with synergistic activity.
Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling
TLDR
A dynamical model representing a cell fate decision network in the AGS gastric cancer cell line is developed, demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells.
In vivo Antitumor Activity of MEK and Phosphatidylinositol 3-Kinase Inhibitors in Basal-Like Breast Cancer Models
TLDR
The studies suggest that single-agent MEK inhibition is a promising therapeutic modality for basal-like breast cancers with intact PTEN, and also provide a basis for rational combination of MEK and PI3K inhibitors in basal- like cancers with both intact and deleted PTEN.
A Landscape of Pharmacogenomic Interactions in Cancer
...
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