Related Experiment Video
Updated: Aug 28, 2025
A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis
Published on: March 8, 2022
Drepmel-A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor
Zachary J Thompson1, Jamie K Teer2, Jiannong Li1
1Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Abstract:
Although substantial progress has been made in treating patients with advanced melanoma with targeted and immuno-therapies, de novo and acquired resistance is commonplace. After treatment failure, therapeutic options are very limited and novel strategies are urgently needed. Combination therapies are often more effective than single agents and are now widely used in clinical practice. Thus, there is a strong need for a comprehensive computational resource to define rational combination therapies. We developed a Shiny app, DRepMel to provide rational combination treatment predictions for melanoma patients from seventy-three thousand combinations based on a multi-omics drug repurposing computational approach using whole exome sequencing and RNA-seq data in bulk samples from two independent patient cohorts. DRepMel provides robust predictions as a resource and also identifies potential treatment effects on the tumor microenvironment (TME) using single-cell RNA-seq data from melanoma patients. Availability: DRepMel is accessible online.
Insights
This study introduces DRepMel, a computational tool predicting effective melanoma combination therapies. It analyzes multi-omics data to guide treatment strategies against drug resistance.
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- Advanced melanoma treatments like targeted and immuno-therapies face significant de novo and acquired resistance.
- Limited therapeutic options exist post-treatment failure, highlighting the urgent need for novel strategies.
- Combination therapies show greater efficacy than single agents, necessitating rational combination prediction tools.
Purpose of the Study:
- To develop a comprehensive computational resource for predicting rational combination therapies in melanoma.
- To address the unmet need for effective treatment strategies following resistance to current therapies.
- To leverage multi-omics data for personalized drug repurposing in melanoma.
Main Methods:
- Developed DRepMel, a Shiny app utilizing a multi-omics drug repurposing computational approach.
- Analyzed whole exome sequencing and RNA-seq data from two independent melanoma patient cohorts.
- Incorporated single-cell RNA-seq data to predict treatment effects on the tumor microenvironment (TME).
Main Results:
- Generated predictions for over seventy-three thousand potential drug combinations for melanoma.
- Provided robust treatment predictions through a computational drug repurposing strategy.
- Identified potential impacts of predicted combinations on the tumor microenvironment.
Conclusions:
- DRepMel offers a valuable resource for identifying rational combination therapies for melanoma patients.
- The tool aids in overcoming therapeutic resistance by suggesting novel treatment combinations.
- Predictive modeling of TME interactions enhances the potential efficacy of combination therapies.
