Howe, Michael J. A.

Abstract Introduction: Suboptimal response to therapy, early disease relapse, and limited long-term survival remain critical challenges in a substantial number of patients with multiple myeloma (MM). While cross-sectional imaging studies are commonly obtained in clinical practice, only limited information is currently incorporated into risk stratification efforts and clinical decision making. Here, we investigate the use of novel image-based biomarkers to identify patients at high risk for adverse outcomes. Methods: We obtained the pretreatment 18F-FDG PET/CT scans of patients with newly diagnosed MM with available clinical and laboratory data seen at Mayo Clinic between 2006 and 2022. In this analysis, we included 637 patients with detectable metabolic tumor volume on their pretreatment scan. The native DICOM images were pre-processed and subjected to automated image segmentation using nnU-Net (nnU-Net v1.0, inference using a pre-trained hematologic malignancies model). Limitations of this automated segmentation approach include heterogeneity introduced by varying image quality, technical variability in PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. PyRadiomics was used to extract quantitative radiomics features for clinical prediction modeling. We evaluated the predictive power of tumor size-, shape-, and texture-related radiomics features for identifying high-risk disease (advanced ISS stage at diagnosis, the presence of high-risk FISH abnormalities, failure to achieve a stringent complete response [sCR] to first-line therapy by IWMG criteria, and early mortality). All radiomics features were quantile-normalized for downstream modeling. Forward and backward feature selection as well as testing for multicollinearity were used to remove redundant features. Results: Median age at diagnosis of the 637 myeloma patients was 64 years (range 22–90), and 387 patients (61%) were male. After a median follow-up of 4.4 years (95% CI 0.2–11.7), median overall survival (OS) was 7.3 years (95% CI 6.3–8.3). Increased total tumor volume (upper tertile vs lower tertiles), measured by the total tumor surface area, was associated with advanced ISS stage (III vs I+II; OR 1.73, 95% CI 1.24–2.42, p=0.001, n=637). Increased tumor density (dichotomized at the median), measured by the median gray level intensity (OR 1.93, 95% CI 1.94–3.12, p=0.007, n=303), and decreased tumor heterogeneity (lower tertile vs upper tertiles), measured by the variability of gray-level intensities (OR 1.88, 95% CI 1.49–3.12, p=0.012, n=303), were associated with the presence of high-risk cytogenetics. Both increased total tumor volume (4.9% increase per tertile, 95% CI 2.2–7.6, p<0.001, n=600) and tumor density (8.9% increase per tertile, 95% CI 4.5–13.3, p<0.001, n=600) were independently associated with more extensive bone marrow involvement at diagnosis. Increased tumor heterogeneity (upper tertiles vs lower tertile), as measured by rapid changes of gray level intensities, was associated with failure to achieve a stringent complete response to first-line therapy (OR 0.50, 95% CI 0.30–0.82, p=0.006, n=351). Increased tumor volume was associated with shorter OS (HR 1.35 per tertile, 95% CI 1.11–1.63, p=0.002, n=301), independent of age, sex, ISS stage, and the presence of high-risk cytogenetics. A simple additive score consisting of tertiles of total tumor volume, ISS stage, and plasma cell labeling index ≥2% identified patients at risk of mortality within 3 years of diagnosis (OR 2.00, 95% CI 1.40–2.85, p<0.001, n=126), with moderate discrimination (AUC=0.71). Sensitivity analysis demonstrated total tumor volume and texture as most predictive of overall survival in a random forest survival model (C-index 0.712, 95% CI 0.649–0.775) and tumor texture as most predictive of response to therapy in a random forest classification model (C-index 0.693, 95% CI 0.607–0.779). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans produces biomarkers that capture disease stage, burden, and risk in newly diagnosed MM. Total tumor volume and heterogeneity are strongly associated with important disease characteristics and can be used to identify patients at high risk for therapeutic resistance, early mortality, and inferior long-term outcomes.

Abstract Cyclin D1, encoded by CCND1, is a cell cycle regulator, overexpressed in ~15% of multiple myeloma (MM) cases via CCND1::IGH super-enhancer juxtaposition through t(11;14). Cyclin D1 also plays a non-canonical role in transcriptional regulation, but in t(11;14) MM its function in gene regulation remains undefined. We investigated the global transcriptional and epigenomic consequences of CCND1overexpression in t(11;14) MM. To define a CCND1 signature, RNA-seq from two independent primary MM patient cohorts, CoMMpass and Mayo, was utilized to compare high CCND1 t(11;14) cases (≥300 transcripts-per-million [TPM], n=271) vs. low CCND1 hyperdiploid cases (<30 TPM, n=233). Two CCND1 knockout (KO) clones in t(11;14) MM U266B1 were also generated by CRISPR/Cas9, introducing a frameshift insertion (A) after chr11:69641478 (hg38) in exon 1. ATAC-seq and ChIP-seq (H3K4me3, H3K4me1, and H3K27ac) were performed in duplicates, and RNA-seq in triplicates in U266B1 wild-type (WT) and KO. Libraries were sequenced on Illumina NextSeq 2000 (2x51 bp for ChIP/ATAC, 2x150 bp for RNA-seq). Reads were mapped to hg38 by BWA (ChIP/ATAC) or STAR (RNA-seq), peaks called by MACS2 (FDR< 0.01), peak and gene raw counts estimated by featureCounts. Differential peaks (fold-change ≥1.5) and differentially expressed genes (fold-change ≥2) were identified by edgeR (FDR<0.05), including peaks detected in ≥2 samples and genes with TPM ≥0.1 in ≥1 sample. Interactions between promoters and distal regulatory regions were identified using publicly available chromatin interaction data in blood cell types. Pathway analysis was performed using Enrichr. Co-immunoprecipitation was performed in t(11;14) KMS12BM cells. Differential analysis comparing CCND1-high t(11;14) and CCND1-low MM cases in the two patient cohorts identified 21 upregulated and 21 downregulated genes associated with CCND1 expression which were also captured in the wild-typevs. CCND1 KO U266 cells. Enriched pathways (p<0.05) included Notch signaling, early/late estrogen response, epithelial-mesenchymal transition and reduced mitotic spindle and myogenesis. Dysregulated genes included those related to apoptosis, ubiquitin-proteosome, extracellular matrix, adhesion, and immune response, demonstrating a broader role for cyclin D1 in MM beyond cell cycle control. To explore the epigenomic basis of this transcriptional regulation, for each of the 42 differentially expressed genes we examined the changes of histone marks and chromatin accessibility in promoter and the interacting distal regulatory regions in the U266 KO model. Ten of the 21 genes upregulated in CCND1-high (downloaded in KO; e.g., argininosuccinate synthase 1 [ASS1], PMAIP1, MYEOV, CCR7, and DTX1) and 12 of the 21 genes downregulated in CCND1-high (upregulated in KO; e.g., UACA, RASAL2, PTPRZ1, PPT2, and IL23R) showed concordant changes in histone marks and/or chromatin accessibility, predominantly in distal regulatory regions, suggesting a chromatin-mediated mechanism of gene regulation by cyclin D1. The most prominent changes involved H3K27ac, a mark defining active enhancers. As H3K27ac is deposited by histone acetyltransferases CBP, we tested for the possible cyclin D1-CBP interaction. Co-IP confirmed this interaction, suggesting a role for this complex in chromatin remodeling and transcriptional control in t(11;14) MM. One gene notably upregulated in association with CCND1 is ASS1, which encodes a urea cycle enzyme responsible for endogenous arginine synthesis. Metabolomic studies demonstrated KO cells had elevated aspartate and citrulline and lowered arginine and ornithine compared to WT cells (p<0.01) demonstrating impaired urea cycle function. Further, KO cells displayed significantly reduced ASS1 protein expression, cell growth and S-phase entry compared to WT cells in arginine-replete conditions, with both WT and KO cell types further inhibited by arginine depletion (p<0.05). Together, a cyclin D1-associated gene signature was identified supporting its role in t(11;14) MM beyond the CDK4/6 mediated cell cycle control, including a role in metabolic plasticity and survival under arginine depleted conditions. These findings highlight cyclin D1 as a therapeutic vulnerability and open new avenues for personalized treatment strategies.

Abstract Background Extramedullary disease (EMD) in multiple myeloma (MM) represents an aggressive phenotype with poor outcomes. We performed single-cell RNA sequencing (scRNA-seq) on clonal plasma cells (CPCs) from EMD tissue, solitary plasmacytomas (SPM), and bone marrow aspirates (BMAs) from patients with MM to identify transcriptional states unique to EMD. Methods We processed FFPE tumor samples from EMD and SPMs and cryopreserved CD138+ BMAs from MM, using the 10x Genomics FLEX platform. Cells/nuclei were fixed, hybridized with Human WTA probes, pooled, and sequenced (10,000–40,000 cells/library) on NovaSeq X. After removing low quality cells, data was normalized and integrated using RPCA in Seurat. Plasma cell identity was assigned via Cellarium and canonical markers. Clonality was inferred by light chain restrictions and refined via random forest modeling. Cell cycle effects were regressed out to reduce proliferation bias. Results We analyzed 128, 908 CPCs from 16 samples [8 EMD (62, 669 CPCs), 4 SPM (20, 607 CPCs), 4 BMA (45, 632 CPCs)] and identified eight transcriptionally distinct cell clusters. Differential expression and pathway comparisons were performed between EMD/SPM/BMA within each of these clusters. Integration of gene expression and pathway analyses revealed consistent upregulation of cell cycle and E2F pathways in EMD. We identified two highly proliferative cell cycle clusters (Cluster 2 with S-phase genes and Cluster 4 with a G2/M transcriptional program) that were expanded in EMD compared to BMA and SPM. Cluster 0 comprised a quiescent CPC population enriched for RNA processing pathways alongside downregulation of ER stress and autophagy. EMD cells demonstrated suppressed interleukin signaling [fold change (FC) –33.8), and class I MHC antigen processing (FC –23.5) relative to BMA, suggestive of an immune suppressive transcriptional program. Angiogenesis-related genes [VEGFA (log2FC 2.4), ADM2 (log2FC 2.2)] and EZH2 [log2FC 2.1; q<0.0001)] were upregulated in EMD. Compared to SPM, EMD plasma cells in this cluster demonstrated marked downregulation of CD74 (log2FC –2.6; q<0.0001), a key modulator of MHC class II antigen presentation. Cluster 1 comprised of metabolically stressed secretory CPCs and displayed downregulation of early growth response pathways in EMD compared to BMA, concurrent with upregulation of E2F signaling. Suppression of IL-2/STAT5, IL-6/JAK/STAT3, and TGF-β pathways in EMD suggests reduced reliance on bone marrow–derived cytokines and impaired immunoregulatory signaling. CD44 was upregulated in EMD versus BMA (log2FC 3.2), consistent with its role in EMD spread and marrow independence. Cluster 3 was enriched for extracellular matrix–interacting plasma cells (COL1A1, MMP2) with upregulated epithelial–mesenchymal transition (EMT) pathways in EMD compared to both BMA (FC 7.5) and SPM (FC 7.07). Cluster 6 was defined by a migratory, motility-primed plasma cell population (CD44, AHNAK, LMNA and S100A10). Notably, IL32 was overexpressed in EMD compared to BMA (log2FC 5.1; adj. p = 2.02×10⁻¹⁴), a finding linked to hypoxia-driven proliferation. Compared to SPM, EMD cells also had elevated MYC/E2F activity and DNA repair, hallmarks of a proliferative phenotype. Cluster 5 displayed a strong interferon-stimulated gene signature (ISG15, IFIT1, TNFSF10), with enrichment of type I IFN, JAK–STAT, and TNF pathways. Within this inflammatory cluster, EMD exhibited marked suppression of immune signaling, metabolism (TCA, OXPHOS), and TP53 programs, consistent with an immune-evasive, metabolically suppressed, and deregulated state. Finally, within Cluster 7, a smaller subset defined by NFκB–driven anti-apoptotic transcriptional activity, EMD exhibited significant downregulation of KLF2 (log2FC –3.7; adj. p = 1.77×10⁻²⁷), likely reflecting disruption of the KDM3A–KLF2–IRF4 axis, a transcriptional network critical for bone marrow homing and plasma cell survival. We consistently observed downregulation of the MAPK pathway in EMD compared to BMA, aligning with prior bulk RNA-seq data and contrasting with the near-universal presence of MAPK mutations, highlighting a disconnect between mutational status and pathway activation. Conclusion In the largest yet reported single-cell transcriptional atlas of EMD and SPM, we demonstrate EMD to be highly proliferative with metabolic reprogramming, EMT, and downregulation of key immunologic signaling pathways, indicating immune escape and microenvironmental independence.

Abstract Extramedullary disease (EMD) is associated with an inferior prognosis and lower response rates to conventional multiple myeloma (MM)–directed therapies compared to MM without EMD. A deeper understanding of the molecular landscape and underlying drivers of EMD is essential to identify potential targets for novel therapeutic strategies. To address this, we performed whole-exome sequencing on EMD tumor tissue from 18 unique patients and bone marrow aspirates (BMAs) from 20 patients at the time of EMD development. Notably, paired EMD and BMA samples were collected from 6 patients at the point of EMD diagnosis, allowing for direct comparison of molecular profiles. Our analysis revealed a near-universal presence of mutations within the MAPK pathway in EMD samples (94%), compared to BMAs (60%; odds ratio, 10.7; P = .02; q < 0.1). Additionally, mutations in established driver genes (NRAS, KRAS, and BRAF) were common and frequently clonal, suggesting their central role in EMD pathogenesis. We also identified alterations in genes associated with cell adhesion and migration (ROBO1, ROBO2, and FAT1) and the SWI/SNF complex and epigenetic regulators (ARID1A, KMT2C, KMT2D, and EP300), although these were predominantly subclonal. Furthermore, we frequently detected biallelic alterations in the tumor suppressor genes MAX (22%), a binding partner for MYC, and CDKN2C (17%). Genomic complexity was significantly higher in EMD samples than BMAs, as evidenced by increased tumor mutational burden and the enrichment of 1q gain/amplifications. These findings underscore the distinct molecular profile of EMD compared to BMA and highlight the genomically complex and heterogeneous nature of extramedullary disease in MM.