Identifying high‐risk multiple myeloma patients: A novel approach using a clonal gene signature

• Published in: International journal of cancer Vol. 155; no. 9; pp. 1684 - 1695
• Main Authors: Li, Jian‐Rong, Wang, Christiana, Cheng, Chao
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2024; Wiley Subscription Services, Inc
• Subjects: Biomarkers, Tumor - genetics; clonal gene signature; computational framework; Computer applications; Copy number; Cytogenetics; Dexamethasone; Dexamethasone - therapeutic use; DNA Copy Number Variations; Female; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genes; Humans; Male; Medical prognosis; Multiple myeloma; Multiple Myeloma - genetics; Prognosis; prognostic prediction; Risk groups; Transcriptome; prognostic prediction; multiple myeloma; clonal gene signature; computational framework
• DOI: 10.1002/ijc.35057
• ISSN: 0020-7136; 1097-0215

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. What's new? Prognostic signatures are usually defined by selecting genes associated with patient survival. However, signatures based on prognostic clonal genes are more likely to be reproducible. Here, the authors developed a novel computational framework to identify a clonal gene signature (CGS) for multiple myeloma (MM) using gene expression data. Comprising 22 genes, the CGS outperformed established prognostic models in identifying high‐risk patients. The signature could not only refine risk stratification but also provide insights into patient subgroups within established risk categories. The novel computational framework provides a useful tool for prognostic assessment in MM and other cancer types.