Ohlstrom, Denis

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.

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.

Abstract Background: Protein production and homeostasis are critical to life and death decisions of malignant plasma cells in multiple myeloma (MM). While protein homeostasis has been targeted effectively with proteasome inhibitors in this disease, inhibition of protein production has not been clinically tested. Protein translation inhibition represents an immediately available avenue, with the agent omacetaxine mepesuccinate (Oma). Recent in vivo and ex vivo work illustrates the broad sensitivity MM cells have to Oma, irrespective of proteasome inhibitor (PI) or immunomodulatory (IMiD) drug treatment exposure (Walker et al. Clinical Cancer Research, 2021). High levels of baseline protein translation, compared to other cells in the bone marrow, serves as a biomarker of high Oma sensitivity in MM patient samples. Based on this, we hypothesized that Oma treatment in MM may lead to eventual drug resistance through the downregulation of protein synthesis. To test this, we evaluated the effects of long-term Oma treatment on MM cells, by establishing resistance in two human MM cell lines and comparing them to their isogenic parental lines. Methods: Omacetaxine resistance was established by dose escalation in MM cell lines NCI-H929 and MM1S, with concentrations gradually increased over 80 weeks to 84 nM and 52 nM, respectively. Resistance was confirmed via CellTiter-Glo after 96 h incubations. Translation rates were measured by incorporation of OP-Puromycin. Flow cytometry was used for cell cycle analysis. Mass cytometry (CyTOF) was performed on a Fluidigm Helios system with a 35-marker panel. Cell morphology was examined by confocal microscopy using a Zeiss LSM780 spectral microscope with DAPI and Mitotracker stains. Cell line xenograft studies were performed using NSG mice engrafted via IV tail injection. Results: Resistant cell lines H929-R and MM1S-R displayed decreased cytotoxicity to Oma, with EC50 >100 nM after 96 h treatment, compared to parental line sensitivity of 24 nM for MM1S and 54 nM for H929 (Fig 1A and 1B). Next, we evaluated baseline translation rates for both parental and resistant lines. As hypothesized, H929 cells significantly downregulated translation after acquiring Oma resistance, but MM1S cells actually significantly upregulated protein synthesis (n = 3, p < 0.001 for both lines, Fig 1C). Interestingly, MM1S-R cells showed global phenotypic changes from the parental line based on UMAP analysis of CyTOF results, whereas H929-R appeared similar to its isogenic line (Fig 1D). The most altered protein was the transcription factor XBP1s, which was elevated in MM1S-R cells compared to parental (Fig 1E). An increased percentage of aneuploidy cells was observed in both resistant lines compared to the parental lines, indicating genomic pressure on both (Fig 1F). Consistent with this, confocal microscopy showed the emergence of multi-nucleated and pleomorphic cells in the Oma resistant lines compared to parental (Fig 1G). Finally, both resistant lines showed a surprising extended survival in an untreated MM NSG xenograft model. Mice engrafted with either H929-R or MM1S-R had a significant survival advantage over their parental cell lines (p = 0.0019, p = 0.0014, Fig 1G and 1H). Median survival was 119 days for H929-P and unreached for H929-R mice, MM1S was 47 and 68 days respectively. Conclusion: After prolonged exposure to Oma, cell lines H929 and MM1S displayed unique characteristics compared to their parental lines, including changes in protein translation rates, drug sensitivities, aneuploidy, and in vivo growth characteristics. Once resistant to Oma, H929 cells downregulated translation, similar to published patient sample data. Conversely, MM1S cells upregulated translation and displayed a more complex phenotypic shift, including an upregulation of XPB1s, an indication of increased unfolded protein response. Much like PIs, we believe Oma disrupts the delicate proteostasis intrinsic to MM cells, putting them on the defensive. Both Oma resistant cell lines exhibited a stressed cell state evident through their decline of in vivo fitness and an increase in poly-aneuploidy. Ongoing research with these cell lines will evaluate transcriptional changes, continuing to advance our understanding of Oma sensitivity and resistance in multiple myeloma. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.