Song, Yizhe

Abstract Multiple myeloma is a malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment (IME). To investigate immune correlates of treatment response and disease progression, we conducted multi-omics profiling including CD138neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138pos bulk RNA and whole-genome sequencing from 209 samples. In longitudinal analyses, interferon-γ signaling associated with markers of impaired T-cell memory after autologous stem cell transplant, whereas naïve B-cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48; P = 2.3e−4). At disease progression, multiple myeloma cells upregulated cancer–testis antigens (CTAg) and immune effector genes, with concurrent B-cell depletion, enrichment of myeloid-derived suppressor cell expression, and phenotypic T-cell exhaustion. These findings highlight dynamic immune–tumor interactions, identifying naïve B-cell reconstitution as a biomarker of durable response and CTAgs as potential targets for high-risk disease at progression. Significance: Longitudinal profiling of multiple myeloma and the IME revealed dynamic immune–tumor interactions across the disease course. Altered expression in CD8+ T cells limited memory phenotype after transplant, whereas naïve B-cell recovery associated with sustained treatment response. At progression, CTAg expression associated with immunosuppression, revealing novel mechanisms of immune dysregulation.

Abstract Introduction: Multiple Myeloma (MM) is a complex hematologic malignancy, where patients often display one or more potentially high risk cytogenetic alterations associated with poor outcomes. Traditional high risk cytogenetics events in myeloma, such as NSD2 t(4;14) translocations, gain of 1q21, or deletion of 17p13, have often been studied in isolation, but emerging evidence suggests the combination of these high risk alterations has a greater negative prognostic impact than any on their own. This has been recently reflected in risk stratification guidelines from IMS and IMWG, which consider certain events, such as t(4;14), as high risk only in combination with gain of 1q21 or deletion of 1p32. Still, the mechanisms through which multi-hit high risk events disrupt the microenvironment and promote poor outcomes remains unclear. We hypothesize that myelomas with multiple high risk alterations remodel the immune environment to promote tumor progression and immune escape. Methods: To study the role of multi-hit high risk cytogenetics on the immune microenvironment, we analyzed CD138-bone marrow aspirate single-cell RNA sequencing (scRNAseq) data from 337 newly diagnosed MM patients from the MMRF CoMMpass study, generated as part of the Immune Atlas initiative. Cytogenetic alterations for these patients were derived from whole-genome and whole-exome sequencing of CD138+ bone marrow aspirates from the MMRF CoMMpass cohort. Patients were classified as multi-hit high-risk MM if they exhibited either (i) two high-risk cytogenetic events, including NSD2 t(4;14), MAF t(14;16), del(17p13), or (ii) one high-risk event with gain of 1q21. Results: Based on cytogenetic information, 33 patients in the Immune Atlas cohort were classified as 'multi-hit’ high-risk, while 34 patients had only a single high-risk alteration. These multi-hit high risk patents displayed significantly worse progression free survival compared to standard risk patients, adjusting for age, therapy, and ASCT (HR=2.34, p<7.2e-5), while single-hit patients showed a non-significant trend towards worse outcomes (HR=1.51, p=0.071). The most frequent multi-hit risk event in the cohort was NSD2 t(4;14) in combination with 1q21, observed in 23 patients. Compositional analysis of the immune populations in the scRNAseq data revealed that relative to both standard and single-hit high risk patients, multi-hit high risk patients displayed significant reductions in IFN-I stimulated CD4+ and CD8+ T cell populations, marked by decreased expression for antiviral response markers such as ISG15, XAF1, and IFI44L. Enrichment of inflammatory CD16+ non-classical monocytes was also observed. Further analysis of the most frequent multi-hit risk event, NSD2 with 1q21, revealed distinct enrichment of late-activated cytotoxic HLA+ CD8+T cells and cytotoxic CD4+.T cells. Neither alteration was observed in patients with single-hit NSD2 or 1q21. Further analysis on the interactions between high risk events revealed distinct transcriptomic profiles dependent on the high risk events involved, particularly in multi-hit events involving mutations or deletions of TP53 or gain of 1q21. Reduction in IFN-I signaling across various immune populations was primarily restricted to multi-hit risk events involving gain of 1q21. Conversely, patients with biallelic loss of TP53 displayed significant enrichment of these same IFN-I populations. Given this contrast in IFN-I signaling spanning multiple immune subtypes, we hypothesized that the malignant myeloma cells might similarly display altered IFN-I activity. Utilizing CD138+ myeloma bulk RNA sequencing data from CoMMpass, we found that IFN-I response scores strongly correlated between paired scRNA immune and bulk RNAseq tumor datasets (R2: 0.75, p=1.63e-32). Across 660 patients with bulk RNAseq and cytogenetic data, we confirmed significant reductions in IFN-I response scores in tumor cells in multi-hit high risk involving 1q21, and significant enrichment in those with complete loss of TP53 relative to standard-risk MM. Conclusions: Overall, we do observe that MM patients with multiple high risk cytogenetic alterations display distinct immunologic traits relative to standard risk or single-hit high risk patients in addition to significantly worse outcomes. Furthermore, we find variability in the immune alterations observed based on the cytogenetic events involved, further illustrating the heterogeneity in how MM shapes the immune microenvironment.

Abstract Multiple myeloma (MM) is an incurable malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment. To investigate immune factors driving treatment response and resistance, we conducted multi-omics profiling including CD138 neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138 pos bulk RNA and whole-genome sequencing from 209 samples. Longitudinal analyses revealed that interferon gamma signaling impairs T cell memory after autologous stem cell transplant, while naïve B cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48, p = 2.3e -4 ). At disease progression, MM cells upregulated cancer-testis antigens and immune effector genes, with concurrent B cell depletion, enrichment of myeloid-derived suppressor cell genes in monocytes, and T cell exhaustion. These findings highlight dynamic immune-tumor interactions, identifying naïve B cell reconstitution as a biomarker of durable response, and cancer-testis antigens as potential targets for high-risk disease at progression. Statement of Significance Longitudinal profiling of multiple myeloma and the immune microenvironment revealed dynamic immune-tumor interactions across the disease course. Dysfunctional CD8⁺ T cells limited memory formation post-transplant, while naïve B recovery associated with sustained treatment response. At progression, cancer-testis antigen expression associated with immunosuppression, revealing novel mechanisms of immune escape.

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 Newly-diagnosed multiple myeloma (MM) patients often share tumor genetic abnormalities associated with a high risk of progression and poor survival. We hypothesized that single-cell transcriptomic characterization of the tumor immune microenvironment (TIME) may be combined with tumor-based risk stratification to uncover novel disease biology mechanisms associated with clinical outcomes and improve risk stratification. Methods We performed single-cell RNA sequencing of 361 CD138 negative sorted bone marrow mononuclear cells (BMMCs) samples from 263 MM patients enrolled at time of diagnosis in the Multiple Myeloma Research Foundation CoMMpass study (NCT01454297). Specimens were analyzed at baseline (n=263) across a network of five collaborating academic research centers following assay harmonization. Genetic factors that determine risk were defined as one or more of the following gain- or loss-of-function events derived from baseline genomic sequencing data: del17p14, translocations of the Ig locus with the MAF/A/B or WHSC1/MMSET/NSD2 loci, and gain of chromosome 1q21. Statistical modeling strategies were used to assess single-cell populations or RNA signatures associations with known risk factors or disease progression (FDR<0.05). Results The study generated high-quality single-cell profiles of 731,643 cells from 123 high-risk and 107 standard-risk MM patients based on genetics. Based on distinct transcriptome profiles, BMMCs formed 106 clusters corresponding to 5 major compartments: NK and T cells, B cells, erythrocytes, myeloid cells, and plasma cells. Our preliminary differential abundance analysis comparing high- vs standard-risk patients at baseline uncovered significant differences in the TIME associated with genetic risk. The high-risk group was identified as enriched in effector CD8+, and CD4+ T cells. In contrast, the high-risk group exhibited depletion of naive CD8+ and interferon-associated CD4+ and CD8+ T as compared to the standard-risk group. Within subclusters in the T and NK cells compartment, a higher cytotoxicity signature was observed in high-risk patients, characterized by the expression of granzymes and perforin. Finally, our preliminary survival and progression analysis associated a higher frequency of IDO1-expressing macrophages in worst progression patients, while better outcomes were associated with CD8+ T and B cells expressing androgen response and IL6/STAT3/EMT programs, respectively. Conclusions Current approaches for risk stratification are based on bulk measurements of tumor's genetic or epigenetics features, which do not capture the diversity and dynamics of the TIME. Through single-cell transcriptomics, we were able to capture, in high granularity, the TIME and correlate specific immune cell populations and phenotypes with relapse risk and poor prognostic outcomes. These results suggest that immune subpopulations may be an essential novel aspect for improving current risk stratification models.

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.