Related Experiment Video
Updated: Jan 30, 2026
05:10
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
10.2K
The RANK-RANKL axis: an opportunity for drug repurposing in cancer?
S Peters1, P Clézardin2, I Márquez-Rodas3
1Department of Oncology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.
Summary
Drug repurposing accelerates cancer treatment development. Denosumab, a receptor activator of nuclear factor kappa B ligand (RANKL) inhibitor, shows promise beyond bone cancer, impacting tumor growth, metastasis, and immune response.
Area of Science:
- Oncology
- Pharmacology
- Immunology
Background:
- Drug repurposing offers a cost-effective and faster alternative to traditional drug development.
- Denosumab, a RANKL inhibitor, is approved for skeletal-related events in advanced cancers.
- The role of RANK/RANKL signaling in cancer biology is increasingly understood.
Purpose of the Study:
- To review the role of RANK/RANKL signaling in tumor initiation, progression, and metastasis.
- To explore the impact of RANK/RANKL on tumor immunology and immune evasion.
- To consider future therapeutic strategies involving denosumab.
Main Methods:
- Literature review of studies on RANK/RANKL in cancer.
- Analysis of denosumab's mechanism of action and clinical applications.
- Examination of ongoing clinical trials and potential drug combinations.
Main Results:
- RANK/RANKL signaling influences multiple stages of tumorigenesis.
- RANK/RANKL plays a significant role in tumor immunology and immune evasion.
- Denosumab has demonstrated efficacy in repurposing for giant cell tumor of bone.
Conclusions:
- Denosumab's repurposing potential extends beyond bone-related events.
- Targeting RANK/RANKL may offer novel therapeutic strategies in oncology.
- Combination therapies, including with immune checkpoint inhibitors, warrant further investigation.
Related Concept Videos
01:02Ranks
502
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
502
01:20Spearman's Rank Correlation Test
1.5K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
Spearman's test calculates correlation by...
1.5K
01:37Hypothalamic-Pituitary Axis
66.0K
The response to stress—be it physical or psychological, acute or chronic—involves activation of the Hypothalamic-Pituitary-Adrenal (HPA) axis. The HPA axis is part of the neuroendocrine system because it involves both neuronal and hormonal communication. Its function is to regulate homeostatic systems—metabolic, cardiovascular, and immune—providing the necessary means to respond to a stressor.
66.0K
01:21Wilcoxon Rank-Sum Test
745
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
745
01:21Friedman Two-way Analysis of Variance by Ranks
505
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
505
01:19The Mantel-Cox Log-Rank Test
1.0K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.0K