Acharya, Chaitanya R.

Introduction: In recent years, multiple myeloma (MM) research has increasingly focused on unraveling the complex interactions between malignant plasma cells and their surrounding bone marrow microenvironment (BMME). To further elucidate these interactions, we developed a comprehensive single-cell atlas of the MM BMME, integrating both coding and noncoding (ncRNA) transcriptional profiles. By correlating these profiles with specific cytogenetic abnormalities in myeloma cells, we aim to uncover how ncRNAs regulate the phenotypic and functional states of myeloma cells and, consequently, shape the cellular landscape of the BMME. A detailed understanding of how the noncoding transcriptome affects the cellular components and immune landscape of the BMME is essential for advancing our knowledge of MM pathogenesis and identifying novel biomarkers and therapeutic targets. Methods: Our atlas was constructed using single-cell RNA sequencing data from 481 samples of CD138neg cells sorted from bone marrow aspirates of MM patients enrolled in the MMRF CoMMpass study. This dataset includes accompanying whole-genome sequencing and survival data, enabling correlative analysis between transcriptional profiles, cytogenetic abnormalities, and patient outcomes. To generate a combined coding and non-coding transcriptome, we developed an expanded human reference genome by systematically merging mRNA and ncRNA transcripts from the LncBook2.0 and GENECODEv42 reference genomes. The sequencing data was aligned to this expanded genome, followed by quality control processing, batch correction, clustering, and supervised analysis. Results: The resulting coding and non-coding atlas comprises over 1.9 million cells, spanning immune, plasma, and stromal compartments. Remarkably, ncRNAs constituted 46% of the associated genes, including 22,102 long ncRNAs (lncRNAs) and 3,112 small ncRNAs. Subclustering analysis of the major immune compartments (B lymphoid, myeloid, and NK and T lymphoid) yielded 70 subclusters, with ncRNAs representing 24-38% of the top 100 differentially expressed genes within each compartment. This underscores the significant role of ncRNAs in distinguishing immune subpopulations. Moreover, examining the association between specific cytogenetic abnormalities and the immune composition of the BMME, 30 subclusters were found to be differentially abundant (P < 0.05) between cytogenetic abnormalities, supporting our hypothesis that myeloma cell genotypes significantly shape the immune microenvironment. For instance, patients with CCND1 amplification displayed an increase CD4+ central memory T cells with a decrease in CD8+ cytotoxic T cells. Similarly, patients with 1q21 gain exhibited an increase in TGFβ-stimulated monocytes, while a decrease in cytotoxic CD8+ T and CD56dim NK cells was observed in patients with 17p13 deletion. Next, examining which ncRNAs are associated with specific cytogenetic abnormalities, we identified 14 ncRNAs significantly differentially expressed across cytogenetic abnormalities. Increased expression of six of these ncRNAs were associated with poor overall survival both independently and in combination (P < 0.05, HR = 3.7). Notably, three of these ncRNAs have documented roles in cancer progression but have not been previously studied in the context of MM. Subsequently stratifying patients based on their enrichment of these outcome-associated ncRNAs revealed even stronger associations with immune composition, including an increase in CD4+ regulatory T cells (P < 0.05). This further supports our hypothesis that myeloma cell genotypes shape the BMME. Lastly, by constructing a gene regulatory network to predict the interactions between coding and noncoding RNAs, we illustrate the regulatory networks of ncRNAs modulating the phenotypes of myeloma and immune cell subpopulations. Conclusion: In summary, we present the first high-resolution transcriptomic atlas of the MM BMME, integrating both coding and noncoding RNAs. This study reveals distinct immune subpopulations associated with specific myeloma cell genotypes and highlights the central role of ncRNAs in modulating the phenotypic and functional states of both malignant and non-malignant cells in the MM BMME. Our findings emphasize the potential of ncRNAs as therapeutic targets and prognostic markers for MM, offering new avenues for research and clinical intervention.

Introduction As part of the Multiple Myeloma Research Foundation (MMRF) MyDRUG umbrella clinical trial, we compared immune cell transcriptomic and proteomic signatures of multiple myeloma bone marrow samples from patients assessed by single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF). The goal of our study is to evaluate the effects of cobimetinib on the immune repertoire over the treatment course at single-cell resolution. Methods Bone marrow samples were collected from patients prior to therapy with cobimetinib (baseline/BL), after 2 cycles of cobimetinib therapy (EOC2), after 2 cycles of combination cobimetinib with IPd (EOC4) and at the end of treatment or at disease progression (EOT). All samples were processed into CD138+ myeloma cell and CD138- “immune cell” fractions for single-cell analysis. In this study, the CD138- “immune cell” fractions were transcriptionally profiled by single-cell RNA-sequencing (scRNA) at up to four time points. Results Transcriptome profiles of approximately 50,000 cells were analyzed across 22 samples from 9 patients with tumor MAPK Pathway mutations including: BRAF (p.K483Q, p.V600E, n=2); KRAS (p.Q61H, p.G12V, p.G12R, n=3); NRAS (p.Q61H, p.Q61R, p.Q61K, n=4). A subset of paired samples were subject to CyTOF with two immune related panels to supplement the single-cell transcriptomic findings. Focusing on one immune panel, 4 samples were profiled at baseline and end of cycle 2 while two samples were profiled only at baseline capturing over 600,000 cells spanning both timepoints. Our immune focused CyTOF panel contained 41 markers including T and NK cell markers (CD4,CD8A,CD3, GZMB, CD56), Myeloid lineage markers (CD14), B/Plasma markers (CD19, CD20, SLAMF7, CD38) among others. The largest populations captured by CyTOF included 260,000 neutrophils, 139,000 monocytes, 130,000 CD4/CD8 T cells, 43,000 NK cells, 40,000 dendritic cells and 22,000 B/plasmablasts/myeloma cells. Exhaustion markers, including LAG3 and TIGIT, are increased in CD8 and CD4 T cells after MEK inhibition, while PD1 is downregulated. PD-L1 is upregulated in both T cell compartments (CD4/CD8), monocytes and in residual myeloma cells profiled by both CyTOF and scRNA-seq. Both the IFNG and STAT1 pathways are concurrently upregulated post MEK inhibition in transcriptionally distinct CD8 T cell subsets profiled by scRNA-seq, suggesting a mechanism influencing PD-L1 expression. Conclusions These data indicate IFNG pathway activation by CD8+ T cells post MEK inhibition promotes PD-L1 expression on residual myeloma cells, suggesting the PD-L1 and IFNG signaling axes is activated in relapsed refractory myeloma patients after MEKi treatment. Here we present immunophenotypic and transcriptional profiling of longitudinal samples pre/post-MEK inhibition with multiple myeloma patients which can inform precision guided treatment options.