Characterizing the role of the immune microenvironment in multiple myeloma progression at a single-cell level.

Early alterations within the bone marrow microenvironment that contribute to the progression of multiple myeloma (MM) from its precursor stages could be the key to identifying novel therapeutic approaches. However, the intrinsic variability in cellular populations between patients and the differences in sample processing and analysis methods have made it difficult to identify consistent changes between data sets. Here, we used single-cell RNA sequencing of bone marrow cells from precursor stages, monoclonal gammopathy of unknown significance, smoldering MM, and newly diagnosed MM and analyzed our data in combination with a previously published data set that used a similar patient population and sample processing. Despite the vast interpatient heterogeneity, some alterations were consistently observed in both data sets. We identified changes in immune cell populations as the disease progressed, which were characterized by a substantial decrease in memory and naïve CD4 T cells, and an increase in CD8+ effector T cells and T-regulatory cells. These alterations were further accompanied by an enrichment of nonclonal memory B cells and an increase in CD14 and CD16 monocytes in MM compared with its precursor stages. These results provide crucial information on the immune changes associated with the progression to clinical MM and can help to develop immune-based strategies for patient stratification and early therapeutic intervention.

Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma.

Understanding the profile of oncogene and tumor suppressor gene mutations with their interactions and impact on the prognosis of multiple myeloma (MM) can improve the definition of disease subsets and identify pathways important in disease pathobiology. Using integrated genomics of 1273 newly diagnosed patients with MM, we identified 63 driver genes, some of which are novel, including IDH1, IDH2, HUWE1, KLHL6, and PTPN11 Oncogene mutations are significantly more clonal than tumor suppressor mutations, indicating they may exert a bigger selective pressure. Patients with more driver gene abnormalities are associated with worse outcomes, as are identified mechanisms of genomic instability. Oncogenic dependencies were identified between mutations in driver genes, common regions of copy number change, and primary translocation and hyperdiploidy events. These dependencies included associations with t(4;14) and mutations in FGFR3, DIS3, and PRKD2; t(11;14) with mutations in CCND1 and IRF4; t(14;16) with mutations in MAF, BRAF, DIS3, and ATM; and hyperdiploidy with gain 11q, mutations in FAM46C, and MYC rearrangements. These associations indicate that the genomic landscape of myeloma is predetermined by the primary events upon which further dependencies are built, giving rise to a nonrandom accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.