Language
English
Publication Date
11-4-2024
Journal
The Oncologist
DOI
10.1093/oncolo/oyae244
PMID
39250742
PMCID
PMC11639189
PubMedCentral® Posted Date
9-9-2024
PubMedCentral® Full Text Version
Post-print
Abstract
In multiple myeloma (MM), while frequent mutations in driver genes are crucial for disease progression, they traditionally offer limited insights into patient prognosis. This study aims to enhance prognostic understanding in MM by analyzing pathway dysregulations in key cancer driver genes, thereby identifying actionable gene signatures. We conducted a detailed quantification of mutations and pathway dysregulations in 10 frequently mutated cancer driver genes in MM to characterize their comprehensive mutational impacts on the whole transcriptome. This was followed by a systematic survival analysis to identify significant gene signatures with enhanced prognostic value. Our systematic analysis highlighted 2 significant signatures, TP53 and LRP1B, which notably outperformed mere mutation status in prognostic predictions. These gene signatures remained prognostically valuable even when accounting for clinical factors, including cytogenetic abnormalities, the International Staging System (ISS), and its revised version (R-ISS). The LRP1B signature effectively distinguished high-risk patients within low/intermediate-risk categories and correlated with significant changes in the tumor immune microenvironment. Additionally, the LRP1B signature showed a strong association with proteasome inhibitor pathways, notably predicting patient responses to bortezomib and the progression from monoclonal gammopathy of unknown significance to MM. Through a rigorous analysis, this study underscores the potential of specific gene signatures in revolutionizing the prognostic landscape of MM, providing novel clinical insights that could influence future translational oncology research.
Keywords
Humans, Multiple Myeloma, Prognosis, Mutation, Transcriptome, Receptors, LDL, Tumor Suppressor Protein p53, Biomarkers, Tumor, Female, Male, Gene Expression Regulation, Neoplastic, multiple myeloma, TP53, LRP1B, gene signatures, prognostic prediction
Published Open-Access
yes
Recommended Citation
Li, Jian-Rong; Parthasarathy, Abinand Krishna; Kannappan, Aravind Singaram; et al., "Characterization of Driver Mutations Identifies Gene Signatures Predictive of Prognosis and Treatment Sensitivity in Multiple Myeloma" (2024). Faculty, Staff and Students Publications. 6232.
https://digitalcommons.library.tmc.edu/baylor_docs/6232