image: A: The t-distributed stochastic neighbour embedding (t-SNE) plot demonstrates the cell types in bone marrow (BM). B: The box plot shows the different percentages of each type of BM cell in normal controls and multiple myeloma (MM) patients. C: t-SNE representation of plasma cells in BMbetween normal and MM patients. D: Heat map showing large-scale copy number variations (CNVs) of plasma cells from MM patients and normal controls. The normalised CNV levels are shown. The red colour represents a high CNV level, and blue represents a low CNV level, where each column is a chromosome and each row is a cell. Row side-colour annotated cells originating from each person and classified as normal or tumour. E: Violin plots showing distributions of CNV scores among different stages of plasma cells (***P<0.0001). F: Interphase FISH of cells from stage III patients. Samples had at least 80% tumour purity based on SDC1+. Representative cells are shown. G: Expression patterns of representative markers discriminating normal and malignant plasma cells plotted onto the t-SNE map. The colour key from grey to red indicates relative expression levels from low to high. H: KEGG analysis shows the top upregulated pathways in stage III disease tissues compared with normal tissues. view more
Credit: ©Science China Press
Multiple myeloma (MM) is the second most common blood cancer, characterised by the accumulation and proliferation of terminally differentiated plasma cells with clonal genetic abnormalities. Despite improved survival rates in the era of modern treatment, MM remains largely incurable, with the vast majority of patients eventually experiencing relapse at some point, ranging from a few months to more than ten years. The highly heterogeneous nature of MM represents a significant obstacle to its diagnosis and treatment. Moreover, the interactions between plasma cells and the bone marrow (BM) microenvironment trigger and continuously activate multiple vital proliferative and antiapoptotic signalling pathways. The heterogeneous microenvironment of MM is crucial for myeloma development, treatment and progression. Recent studies have highlighted monocytes in the microenvironment of MM as related to dexamethasone (Dara-VRd) resistance. However, the specific routes through which monocyte–plasma cell crosstalk accelerates progression have not yet been elucidated.
Due to high heterogeneity, the early identification of high-risk individuals by stratification is essential and pivotal. The Revised International Staging System (R-ISS) serves as a simple, reliable and pragmatic standard for the risk stratification of MM patients worldwide, combining the most common prognostic tools (ISS stage, chromosomal abnormalities and serum lactate dehydrogenase level). MM patients defined by the R-ISS are segregated into three subgroups (stages I–III) . Stage III MM patients display notably worse survival and prognosis rates than patients in the other subgroups. However, the extensive underlying genetic landscape and candidate factors contributing to worse survival in stage III MM have not yet been identified clearly.
Single-cell RNA-seq technology enables the elucidation of the complete cellular and molecular composition of a disease, allowing for the detailed characterisation of tumour cells and their microenvironments. Decoding the interstage heterogeneity of malignant plasma cell populations and their partners (e.g., monocytes) would be beneficial in predicting prognosis and designing personalised clinical interventions. Although scRNA-seq has already been applied from pre-MM stages (MGUS, smouldering multiple myeloma) to MM, the broad and deep heterogeneity among diverse R-ISS stages in MM has not been found at the single-cell level.
Herein, we applied scRNA-seq to define newly diagnosed MM patients stratified by the R-ISS strategy, and identified plasma cells were clustered into nine groups (P1–P9) based on gene expression, where P1–P5 were almost enriched in stage III. PDIA6 was significantly upregulated in P3 and LETM1 was enriched in P1, and they were validated to be upregulated in the MM cell line and in 22 other patients’ myeloma cells. Furthermore, in progression, PDIA6 was newly found and verified to be activated by UQCRB through oxidative phosphorylation, while LETM1 was activated by STAT1 via the C-type lectin receptor-signalling pathway. Finally, a subcluster of monocytes was exclusively found in stage III specifically expressed chemokines modulated by ATF3. A few ligand–receptor pairs (CCL3/CCL5/CCL3L1- CCR1) were obviously active in monocyte–plasma communications in stage III. Herein, this study identified novel molecules, networks and crosstalk pairs in different R-ISS stages of MM, providing significant insight for its prognosis and treatment.
Journal
Science China Life Sciences