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Published on: September 18, 2021
VIBE: an R-package for VIsualization of Bulk RNA Expression data for therapeutic targeting and disease stratification
Indu Khatri1, Saskia D van Asten1, Leandro F Moreno1
1Translational Data Science, Genmab, Utrecht, Netherlands.
Background:
Development of cancer treatments such as antibody-based therapy relies on several factors across the drug-target axis, including the specificity of target expression and characterization of downstream signaling pathways. While existing tools for analyzing and visualizing transcriptomic data offer evaluation of individual gene-level expression, they lack a comprehensive assessment of pathway-guided analysis, relevant for single- and dual-targeting therapeutics. Here, we introduce VIBE (VIsualization of Bulk RNA Expression data), an R package which provides a thorough exploration of both individual and combined gene expression, supplemented by pathway-guided analyses. VIBE's versatility proves pivotal for disease stratification and therapeutic targeting in cancer and other diseases.
Results:
VIBE offers a wide array of functions that streamline the visualization and analysis of transcriptomic data for single- and dual-targeting therapies. Its intuitive interface allows users to evaluate the expression of target genes and their associated pathways across various cancer indications, aiding in target and disease prioritization. Metadata, such as treatment or number of prior lines of therapy, can be easily incorporated to refine the identification of patient cohorts hypothesized to derive benefit from a given drug. We demonstrate how VIBE can be used to assist in indication selection and target identification in three user case studies using both simulated and real-world data. VIBE integrates statistics in all graphics, enabling data-informed decision-making.
Conclusions:
VIBE facilitates detailed visualization of individual and cohort-level summaries such as concordant or discordant expression of two genes or pathways. Such analyses can help to prioritize disease indications that are amenable to treatment strategies such as bispecific or monoclonal antibody therapies. With this tool, researchers can enhance indication selection and potentially accelerate the development of novel targeted therapies with the goal of precision, personalization, and ensuring treatments align with an individual patient's disease state across a spectrum of disorders. Explore VIBE's full capabilities using the vignettes on the GitLab repository (https://gitlab.com/genmab-public/vibe ).
Insights
VIBE is a new R package for analyzing gene expression data to aid in developing targeted cancer therapies. It visualizes gene and pathway expression, facilitating disease stratification and drug target identification for personalized medicine.
Area of Science:
- Bioinformatics
- Genomics
- Cancer Research
Background:
- Antibody-based cancer therapies require understanding gene expression and signaling pathways.
- Existing transcriptomic tools lack comprehensive pathway-guided analysis for targeted therapies.
- VIBE (VIsualization of Bulk RNA Expression data) is introduced as a solution.
Purpose of the Study:
- To introduce VIBE, an R package for comprehensive transcriptomic data analysis.
- To enable pathway-guided analysis for single- and dual-targeting cancer therapies.
- To aid in disease stratification and therapeutic target identification.
Main Methods:
- VIBE provides functions for visualizing and analyzing transcriptomic data.
- It allows evaluation of individual gene and pathway expression.
- Incorporates metadata for patient cohort refinement and uses statistics in graphics.
Main Results:
- VIBE streamlines visualization and analysis for targeted therapies.
- Enables evaluation of target genes and associated pathways across cancer indications.
- Demonstrates utility in indication selection and target identification via case studies.
Conclusions:
- VIBE facilitates detailed visualization of gene and pathway expression summaries.
- Prioritizes indications for bispecific or monoclonal antibody therapies.
- Enhances indication selection and accelerates development of novel targeted therapies.
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