ArticleCancer letters2017
Improving pharmacological targeting of AKT in melanoma.
Article in Cancer letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- AKT2 Loss Impairs BRAF-Mutant Melanoma Metastasis.Cancers · 2023Article
- Targeting WEE1/AKT Restores p53-Dependent Natural Killer-Cell Activation to Induce Immune Checkpoint Blockade Responses in "Cold" Melanoma.Cancer immunology research · 2022Article
- Targeting Protein Translation in Melanoma by Inhibiting EEF-2 Kinase Regulates Cholesterol Metabolism though SREBP2 to Inhibit Tumour Development.International journal of molecular sciences · 2022Article
- Inhibition of Axl Promotes the Therapeutic Effect of Targeted Inhibition of the PI3K/Akt Pathway in NRAS Mutant Melanoma Cells.Journal of oncology · 2022Article
- Development of a Novel Multi-Isoform ALDH Inhibitor Effective as an Antimelanoma Agent.Molecular cancer therapeutics · 2020Article
- Salubrinal in Combination With 4E1RCat Synergistically Impairs Melanoma Development by Disrupting the Protein Synthetic Machinery.Frontiers in oncology · 2020Article
- Nanoliposomal delivery of cytosolic phospholipase ANanomedicine : nanotechnology, biology, and medicine · 2018Article
- Moving Synergistically Acting Drug Combinations to the Clinic by Comparing Sequential versus Simultaneous Drug Administrations.Molecular pharmacology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Targeting AKT with pharmacological agents inhibiting this protein in the melanoma clinic is ineffective. This is a major contradiction considering the substantial preclinical data suggesting AKT as an effective target. Various approaches have been undertaken to unravel this contradiction and drug combinations sought that could resolve this concern. We have shown that genetic targeting AKT3 or WEE1 can be effective for inhibiting tumor growth in preclinical animal models. However, no one has examined whether combining pharmacological agents targeting each of these enzymes could be more effective than inhibiting each alone and enhance the efficacy of targeting AKT in melanoma. This report shows that combining the AKT inhibitors (AZD5363 or MK1775) with the WEE1 inhibitor, AZD5363, can synergistically kill cultured melanoma cells and decrease melanoma tumor growth by greater than 90%. Co-targeting AKT and WEE1 led to enhanced deregulation of the cell cycle and DNA damage repair pathways by modulating the transcription factors p53 and FOXM1, as well as the proteins whose expression is regulated by these two proteins. Thus, this study identifies a unique combination of pharmacological agents and the ratio needed for efficacy that could be used to potentially improve the therapeutic effectiveness of targeting AKT in the clinic.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.