Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma

Area of Science:

• Genomics
• Bioinformatics
• Hematological Malignancies

Background:

• Multiple myeloma (MM) pathogenesis is unclear, necessitating advanced gene profiling.
• Next-generation sequencing (NGS) and Microarrays are key technologies for discovering prognostic markers.
• NGS is increasingly favored for its cost-effectiveness and technical advancements, though Microarrays offer benefits in analysis complexity and standardization.

Purpose of the Study:

• To develop a tool for integrative analysis of MM data from MMRF CoMMpass.
• To leverage existing R packages for efficient data integration and analysis.
• To illustrate an integrative analysis workflow for MM genomic data.

Main Methods:

• Utilized the R/Bioconductor package TC-GABiolinks to create MMRFBiolinks.
• Developed an integrative analysis workflow for MMRF CoMMpass data.
• Performed comparative analysis of RNA-Seq data from NCI-GDC Data Portal and MMRF-RG data.

Main Results:

• Demonstrated a workflow for Kaplan-Meier survival analysis and Differential Gene Expression (DGE) enrichment analysis using RNA-Seq data.
• Showcased analysis of correlations between canonical variants and treatment outcomes/classes using MMRF-RG data.
• Presented two case studies validating the MMRFBiolinks workflow on MM bone marrow samples and variant-outcome correlations.

Conclusions:

• MMRFBiolinks provides a valid tool for integrative analysis of MM genomic data.
• The illustrated workflow facilitates comparative analysis of RNA-Seq data and variant-outcome correlations.
• This approach aids in discovering prognostic markers and understanding MM pathogenesis.