Bakhtiari, Mojtaba

The bone marrow immune microenvironment (BMME) is a critical factor in multiple myeloma (MM) pathogenesis. Despite higher incidence of MM among self-identified African Americans, the relationship between African genetic similarity and the BMME remains unclear. We present the largest analysis to date assessing the impact of Ancestry on the BMME in myeloma (N=320, including 64 with ≥50% African genetic similarity, AFR-High) leveraging our recently generated Immune Atlas. AFR-High was associated with enrichment of late T-effector cells, driven by rare cytotoxic CD4⁺ T cells, suggesting a distinct adaptive BMME. AFR-High exhibited expansion of late-differentiated adaptive NK cells with high cytotoxic GNLY and GZMH expression. AFR-High depicted reduced CD14⁺ monocytes, increased CD16⁺ non-classical monocytes, and expansion of macrophage-like monocyte populations with enrichment of metabolic adaptation, efferocytosis and phagocytic clearance programs, and immunoregulatory pathways. These findings reveal a shift from an innate inflammatory immune landscape toward adaptive cytotoxic and immunoregulatory remodeling associated with African genetic similarity in MM.

Abstract Although the human genome encodes a vast repertoire of noncoding RNAs that regulate gene expression, the noncoding genome remains underexplored due to technical challenges. Specifically, during transcriptomic sequencing data alignment, the overlap between noncoding and coding loci can create ambiguous read alignments that are subsequently discarded from downstream analysis. For this reason, most of the noncoding genome is excluded from standard genomic annotations used for sequencing alignment. To address this challenge and enable concurrent profiling of the coding and noncoding transcriptome, we systematically integrated standard coding (GENCODE) and noncoding (LncBook) genome annotations, preserving coding gene annotations and removing overlapping noncoding regions. The resulting integrated genome annotation expanded the number of annotated noncoding genes from 40,785 to 138,296 while preserving all coding genes and reducing ambiguous read assignment. To evaluate the utility of our integrated genome annotation for uncovering novel, biologically relevant noncoding RNAs (ncRNAs), we realigned CD138-positive bulk RNA-seq (N = 942) and CD138-negative single-cell RNA-seq (N = 478) data from the MMRF CoMMpass study, generating a comprehensive coding-noncoding atlas of the myeloma bone marrow microenvironment with noncoding genes representing 51% of highly variable genes and displaying significant cell type specificity. Tumor expression profiling based on this integrated profiling identified 15 clusters, including two enriched for amp(1q21) or t(4;14) and associated with shorter progression-free survival (PFS). Differential expression and systematic filtering yielded 19 candidate high-risk ncRNAs, including previously uncharacterized ENSG00000310209 , which was associated with poor PFS (HR = 1.141, P = 0.0025), increased IRF4 activity, Wnt pathway activation, CCL5 signaling, and the accumulation of anergic-like CD8+ T cells. These findings establish integrated coding-noncoding analysis as a strategic approach for discovering functional ncRNAs from transcriptomic sequencing data.

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.