RNA-Sequencing-Based Transcriptomic Score with Prognostic and Theranostic Values in Multiple Myeloma.

Multiple myeloma (MM) is the second most frequent hematological cancer and is characterized by the clonal proliferation of malignant plasma cells. Genome-wide expression profiling (GEP) analysis with DNA microarrays has emerged as a powerful tool for biomedical research, generating a huge amount of data. Microarray analyses have improved our understanding of MM disease and have led to important clinical applications. In MM, GEP has been used to stratify patients, define risk, identify therapeutic targets, predict treatment response, and understand drug resistance. In this study, we built a gene risk score for 267 genes using RNA-seq data that demonstrated a prognostic value in two independent cohorts (n = 674 and n = 76) of newly diagnosed MM patients treated with high-dose Melphalan and autologous stem cell transplantation. High-risk patients were associated with the expression of genes involved in several major pathways implicated in MM pathophysiology, including interferon response, cell proliferation, hypoxia, IL-6 signaling pathway, stem cell genes, MYC, and epigenetic deregulation. The RNA-seq-based risk score was correlated with specific MM somatic mutation profiles and responses to targeted treatment including EZH2, MELK, TOPK/PBK, and Aurora kinase inhibitors, outlining potential utility for precision medicine strategies in MM.

RNA-sequencing data-driven dissection of human plasma cell differentiation reveals new potential transcription regulators.

Plasma cells (PCs) play an important role in the adaptive immune system through a continuous production of antibodies. We have demonstrated that PC differentiation can be modeled in vitro using complex multistep culture systems reproducing sequential differentiation process occurring in vivo. Here we present a comprehensive, temporal program of gene expression data encompassing human PC differentiation (PCD) using RNA sequencing (RNA-seq). Our results reveal 6374 differentially expressed genes classified into four temporal gene expression patterns. A stringent pathway enrichment analysis of these gene clusters highlights known pathways but also pathways largely unknown in PCD, including the heme biosynthesis and the glutathione conjugation pathways. Additionally, our analysis revealed numerous novel transcriptional networks with significant stage-specific overexpression and potential importance in PCD, including BATF2, BHLHA15/MIST1, EZH2, WHSC1/MMSET, and BLM. We have experimentally validated a potent role for BLM in regulating cell survival and proliferation during human PCD. Taken together, this RNA-seq analysis of PCD temporal stages helped identify coexpressed gene modules with associated up/downregulated transcription regulator genes that could represent major regulatory nodes for human PC maturation. These data constitute a unique resource of human PCD gene expression programs in support of future studies for understanding the underlying mechanisms that control PCD.

Comprehensive characterization of the mutational landscape in multiple myeloma cell lines reveals potential drivers and pathways associated with tumor progression and drug resistance.

Human multiple myeloma tumor cell lines (HMCLs) have been a cornerstone of research in multiple myeloma (MM) and have helped to shape our understanding of molecular processes that drive tumor progression. A comprehensive characterization of genomic mutations in HMCLs will provide a basis for choosing relevant cell line models to study a particular aspect of myeloma biology, or to screen for an antagonist of certain cancer pathways. Methods: We performed whole exome sequencing on a large cohort of 30 HMCLs, representative of a large molecular heterogeneity of MM, and 8 control samples (epstein-barr virus (EBV)-immortalized B-cells obtained from 8 different patients). We evaluated the sensitivity of HMCLs to ten drugs. Results: We identified a high confidence list of 236 protein-coding genes with mutations affecting the structure of the encoded protein. Among the most frequently mutated genes, there were known MM drivers, such as TP53, KRAS, NRAS, ATM and FAM46C, as well as novel mutated genes, including CNOT3, KMT2D, MSH3 and PMS1. We next generated a comprehensive map of altered key pathways in HMCLs. These include cell growth pathways (MAPK, JAK-STAT, PI(3)K-AKT and TP53 / cell cycle pathway), DNA repair pathway and chromatin modifiers. Importantly, our analysis highlighted a significant association between the mutation of several genes and the response to conventional drugs used in MM as well as targeted inhibitors. Conclusion: Taken together, this first comprehensive exome-wide analysis of the mutational landscape in HMCLs provides unique resources for further studies and identifies novel genes potentially associated with MM pathophysiology, some of which may be targets for future therapeutic intervention.