Mapping Disease Transitions from Premalignant, Asymptomatic to Advanced Myeloma through Integrative Epigenomic and Transcriptional Analyses
Authors: Kurowska Aleksandra , Bantan Azari , Shuwaikan Raghad , Cenzano Itziar , Aguirre-Ruiz Paula , Miranda Estíbaliz , Garate Leire , Vilas-Zornoza Amaia , San-Martin Patxi , Larrayoz Marta , Gómez-Echarte Nahia , Ariceta Beñat , Soto Ortiz Daniel , Sobahy Turki , Martín-Subero José Ignacio , Lagani Vincenzo , Tegnér Jesper , Martinez-Climent Jose A. , Agirre Xabier , Hernáez Arrazola Mikel , Prosper Felipe , Gomez-Cabrero David 
Published: December 2024

Multiple myeloma (MM) is a complex malignancy marked by the abnormal proliferation of plasma cells (PCs) in the bone marrow (BM). This disease progresses from asymptomatic stages, including monoclonal gammopathy of undetermined significance (MGUS) and smouldering multiple myeloma (SMM), to active MM. The transition through these different states requires the acquisition of genetic aberrations and changes in gene expression patterns, which have been well characterized. Beyond genetics, the role of epigenetic modifications has transformed our understanding of malignant cellular transformation, including MM. However, the complete regulatory mechanisms involving PCs dysregulation across disease progression are yet to be characterized. Understanding the molecular alterations driving the transitions from precursor states to symptomatic MM are essential for early therapeutic interventions.
In this study, we performed a comprehensive analysis of gene regulatory dysregulation associated with the transitions from healthy to precursor stages and advanced MM. PCs (CD138+) were isolated from human BM samples from healthy control (HC) individuals (n=19) and newly diagnosed MM patients at various stages of disease progression (MGUS (n=16), SMM (n=33) and MM (n=146)), from both genders with a median age of 77 years old. We profiled 23,529 expressed genes (RNA-seq), 138,969 chromatin regions (ATAC-seq), and 94,852 histone mark regions (ChIP-seq).
Initial joint multi-omics analyses revealed weak coordination between gene expression and chromatin accessibility profiles across disease transitions, prompting us to conduct separate analyses for each disease transition: HC vs. MGUS (Transition 1, T1), MGUS vs. SMM (T2), and SMM vs. MM (T3). We observed that few genes and regions linked gene expression and chromatin accessibility with clinical aberrations from FISH profiles. Differential analysis incorporating clinical aberrations as covariates indicated significant transcriptomic changes primarily at T1, revealing 9,688 differentially expressed genes (DEGs). Only 154 and 489 DEGs were significant for T2 and T3, respectively. In contrast, chromatin accessibility changes were mainly occurring at T2 consisting of 12,166 significantly differential accessible regions, characterized by the opening of chromatin regions. Hence, suggesting that regulatory elements play distinct key roles in disease progression.
To elucidate the role of regulators in MM progression, we employed a comprehensive ranking strategy to prioritize transcription factors (TFs) critical for each transition, integrating various statistical scorings. Each scoring method addressed, respectively, the role of TFs in transcriptional changes, their association with differentially accessible regions, and TFs activity shifts. The observed chromatin accessibility shift at T2, revealed key players involved during chromatin opening, including FOX family, SMAD family (SMAD3 and SMAD5), interferon-regulatory factors (IRF5, IRF8), zinc-fingers (ZNF214, ZNF547) and IKZF2. The majority of revealed TFs were known to be essential in MM and identified previously as pioneers (Sinha, et al., 2023; Ordoñez et al., 2020; Mayran, et al., 2018), which we plan to validate using CRISPR-Cas9 in MM cell lines. Furthermore, gene regulatory networks (GRNs) analyses explore the interplay between epigenetic and transcriptomic changes, and identify crucial enhancers and promoters involved in myeloma development. Single-cell datasets, both in-house and public, are being used to generalize findings and dissect intra-tumor heterogeneity.
In conclusion, our study underscores the importance of integrated multi-omics approaches in unraveling the complex regulatory networks driving MM pathogenesis. By identifying key regulatory elements and pathways, we aim to facilitate the development of targeted therapies and improve early screening for MM.