Multiple myeloma: clinical characteristics, current therapies and emerging innovative treatments targeting ribosome biogenesis dynamics

Abstract

Multiple myeloma (MM) is a clinical disorder characterized by aberrant plasma cell growth in the bone marrow microenvironment. Globally, the prevalence of MM has been steadily increasing at an alarming rate. In the United States, more than 30,000 cases will be diagnosed in 2024 and it accounts for about 2% of cancer diagnoses and more than 2% of cancer deaths, more than double the worldwide figure. Both symptomatic and active MM are distinguished by uncontrolled plasma cell growth, which results in severe renal impairment, anemia, hypercalcemia, and bone loss. Multiple drugs have been approved by the FDA and are now widely used in clinical practice for MM. Although triplet and quadruplet induction regimens, autologous stem cell transplantation (ASCT), and maintenance treatment are used, MM continues to be an incurable illness characterized by relapses that may occur at various phases of its progression. MM patients with frailty, extramedullary disease, plasma cell leukemia, central nervous system recurrence, functional high risk, and the elderly are among those with the greatest current unmet needs. The high cost of care is an additional challenge. MM cells are highly protein secretary cells and thus are dependent on the activation of certain translation pathways. MM also has a high chance of altering ribosomal protein-encoding genes like MYC mutation. In this article we discuss the importance of ribosome biogenesis in promoting MM and RNA polymerase I inhibition as an upcoming treatment with potential promise for MM patients.

Keywords: Multiple myeloma; RNA polymerase I; Relapsed–refractory disease; Ribosome biogenesis; Treatment.

Fig. 1

Multiple myeloma progression: this figure illustrates the clinical stages of multiple myeloma progression, beginning with monoclonal gammopathy of undetermined significance (MGUS), progressing through smoldering multiple myeloma (SMM), and culminating in symptomatic multiple myeloma (MM) and plasma cell leukemia (PCL). Key chromosomal abnormalities, including deletions, translocations, and their correlation with disease advancement and prognosis are indicated at each stage. A The schematic (left hand) is designed to aid understanding of the genetic landscape/milieu of multiple myeloma and its impact on clinical progression, potentially guiding therapeutic strategies and prognostic assessments. B The heatmap (right hand) displays differential gene expression associated with ribosome biogenesis across various stages of multiple myeloma, using GSEA to analyze data from the publicly available dataset GSE13591. Each column represents samples from a different disease stage: normal (N), monoclonal gammopathy of undetermined significance (MGUS), and plasma cell leukemia (PCL). Rows correspond to specific genes involved in ribosome biogenesis. Gene expression levels are color-coded, with red indicating higher expression and blue indicating lower expression. The analysis reveals a clear trend of increased expression of ribosome biogenesis genes as the disease progresses from MGUS and becomes most pronounced in PCL. This pattern suggests a potential role ribosome biogenesis in the progression and severity of MM. Figures of cells adapted from BioRender.com (2024). Retrieved from https://app.biorender.com/biorender-templates, Heatmap Morpheus, https://software.broadinstitute.org/morpheus. GEO Clough E, Barrett T (2016) The Gene Expression Omnibus Database. Methods Mol Biol 1418:93–110. GSEA (analysis) Subramanian et al. (2005)

Fig. 2

Ribosome biogenesis related gene expression is enriched in multiple myeloma patients: A Gene Set Enrichment Analysis (GSEA) was conducted on three multiple myeloma patient derived datasets: GSE136337 (N = 160), GSE13591 (N = 158), and GSE6477 (N = 436). The green curve represents enrichment profile. The normalized enrichment score (NES) and GSEA-derived P-value are indicated for each plot. B Venn diagram illustrating the overlap and uniqueness of the most significantly enriched genes identified through the GSEA of three multiple myeloma datasets: GSE6477, GSE13591 and GSE136337. Each circle represents a dataset, with GSE13591 in blue, GSE136337 in green, and GSE6477 in red. The intersections between the circles indicate genes that are commonly enriched across the datasets. This comparative analysis aids in identifying consistent molecular signatures across studies and provides insights into the complex genetic landscape of multiple myeloma. C The common set of 217 genes among the three datasets were used to perform Gene Ontology Analysis using g:profiler.™ to identify the associated biological processes. Citations Heberle H, Meirelles GV, da Silva FR, Telles GP, Minghim R (2015) InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinform 16:169. 10.1186/s12859-015-0611-3