Language
English
Publication Date
11-1-2024
Journal
International Journal of Cancer
DOI
10.1002/ijc.35057
PMID
38874435
PMCID
PMC11537842
PubMedCentral® Posted Date
11-1-2025
PubMedCentral® Full Text Version
Author MSS
Abstract
Multiple myeloma (MM) is a heterogeneous disease with a small subset of high-risk patients having poor prognoses. Identifying these patients is crucial for treatment management and strategic decisions. In this study, we developed a novel computational framework to define prognostic gene signatures by selecting genes with expression driven by clonal copy number alterations. We applied this framework to MM and developed a clonal gene signature (CGS) consisting of 22 genes and evaluated in five independent datasets. The CGS provided significant prognostic values after adjusting for well-established factors including cytogenetic abnormalities, International Staging System (ISS), and Revised ISS (R-ISS). Importantly, CGS demonstrated higher performance in identifying high-risk patients compared to the GEP70 and SKY92 signatures recommended for prognostic stratification of MM. CGS can further stratify patients into subgroups with significantly differential prognoses when applied to the high- and low-risk groups identified by GEP70 and SKY92. Additionally, CGS scores are significantly associated with patient response to dexamethasone, a commonly used treatment for MM. In summary, we proposed a computational framework that requires only gene expression data to identify CGSs for prognosis prediction. CGS provides a useful biomarker for improving prognostic stratification in MM, especially for identifying the highest-risk patients.
Keywords
Humans, Multiple Myeloma, Prognosis, Gene Expression Profiling, Dexamethasone, Gene Expression Regulation, Neoplastic, Transcriptome, Biomarkers, Tumor, DNA Copy Number Variations, Female, Male, multiple myeloma, computational framework, clonal gene signature, prognostic prediction
Published Open-Access
yes
Recommended Citation
Jian-Rong Li, Christiana Wang, and Chao Cheng, "Identifying High-Risk Multiple Myeloma Patients: A Novel Approach Using a Clonal Gene Signature" (2024). Faculty and Staff Publications. 4398.
https://digitalcommons.library.tmc.edu/baylor_docs/4398
Graphical Abstract