# VIBE: an R-package for VIsualization of Bulk RNA Expression data for therapeutic targeting and disease stratification

## TL;DR

VIBE is an R package that helps visualize bulk RNA expression data to support cancer treatment development and disease classification.

## Contribution

VIBE introduces a comprehensive tool for pathway-guided analysis of gene expression, supporting single- and dual-targeting therapeutic strategies.

## Key findings

- VIBE enables visualization of both individual and combined gene expression with pathway analysis.
- The tool aids in disease stratification and therapeutic targeting across various cancer types.
- Case studies demonstrate VIBE's utility in indication selection and target identification.

## Abstract

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.

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.

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
).