Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups.
Eileen M BoyleAdam RosenthalHussein GhamlouchYan WangPhillip FarmerMichael W RutherfordTimothy Cody AshbyMichael A BauerSarah K JohnsonChristopher Paul WardellYubao WangAntje HoeringCarolina D SchinkeSharmilan ThanendrarajanMaurizio ZangariBart BarlogieMadhav V DhodapkarFaith E DaviesGareth J MorganFrits van RheeBrian A WalkerPublished in: Leukemia (2021)
Sequencing studies have shed some light on the pathogenesis of progression from smouldering multiple myeloma (SMM) and symptomatic multiple myeloma (MM). Given the scarcity of smouldering samples, little data are available to determine which translational programmes are dysregulated and whether the mechanisms of progression are uniform across the main molecular subgroups. In this work, we investigated 223 SMM and 1348 MM samples from the University of Arkansas for Medical Sciences (UAMS) for which we had gene expression profiling (GEP). Patients were analysed by TC-7 subgroup for gene expression changes between SMM and MM. Among the commonly dysregulated genes in each subgroup, PHF19 and EZH2 highlight the importance of the PRC2.1 complex. We show that subgroup specific differences exist even at the SMM stage of disease with different biological features driving progression within each TC molecular subgroup. These data suggest that MMSET SMM has already transformed, but that the other precursor diseases are distinct clinical entities from their symptomatic counterpart.
Keyphrases
- multiple myeloma
- cell cycle
- genome wide
- gene expression
- newly diagnosed
- phase iii
- dna methylation
- induced apoptosis
- end stage renal disease
- poor prognosis
- genome wide identification
- big data
- electronic health record
- single molecule
- clinical trial
- ejection fraction
- chronic kidney disease
- signaling pathway
- copy number
- binding protein
- long non coding rna
- mass spectrometry
- deep learning
- transcription factor
- endoplasmic reticulum stress
- study protocol