Dragan Jevremovic

Clonal monocytosis of undetermined significance (CMUS) and clonal cytopenia with monocytosis of undetermined significance (CCMUS) are recently defined precursor states, with unclear progression rates to myeloid neoplasms (MN), including chronic myelomonocytic leukemia (CMML). We evaluated a cohort of 958 patients with sustained monocytosis ( ≥ 0.5 ×10⁹/L, ≥10%) and stringently excluded MN. Among 958 patients, 74 met criteria for CMUS (n = 10) and CCMUS (n = 64), respectively. These patients were compared with a cohort with oligo monocytic-CMML (OM-CMML; n = 47). CMUS patients were younger (median age: 63 years), whereas CCMUS demonstrated lower BM blast %, in comparison to OM-CMML (median blast %: 1 vs. 2). OM-CMML was enriched for TET2 (60% vs. 32%, p = 0.012), ASXL1 (40% vs. 9%, p < 0.001), and SRSF2 (45% vs. 8%, p < 0.001) mutations, while CMUS/CCMUS had higher rates of DNMT3A (36% vs. 9%, p < 0.001) and PPM1D (12% vs. 0, p = 0.029) mutations. Mutation burden and VAF for most mutations (except SRSF2) were significantly higher in OM-CMML. DNMT3A mutations in CMUS/CCMUS often involved the methyltransferase (MT) domain, and MT domain mutations and co-mutations were associated with progression of CCMUS to CMML. With a median follow-up of 31 months, there were no progression events in CMUS patients, while 13% of CCMUS patients progressed to MN, mainly CMML (7/8). Median OS was inferior in OM-CMML (71 months) compared to CMUS/CCMUS (not reached), p < 0.001. Our findings demonstrate that CMUS and CCMUS represent biologically distinct clonal states with variable progression risk and highlight the importance of integrating morphologic and genomic data to refine classification.

BCMA-directed CAR T-cell and T-cell engager (TCE) therapies produce high response rates in relapsed multiple myeloma, yet most patients progress. Prior work identified TNFRSF17 alterations mediating antigen escape in up to 43% of TCE progressions. To evaluate the generalizability of these findings and examine antigen-loss-independent resistance, we performed whole-exome sequencing on 32 paired pre- and post-treatment progression bone marrow samples from 15 patients (5 CAR T, 10 TCE); two patients received both agents. TNFRSF17 alterations were infrequent and restricted to TCE recipients (one concurrent frameshift and missense mutation; two heterozygous copy-number losses); no biallelic loss or CAR T-associated events were observed. In contrast, deleterious/loss-of-function variants in NF-κB, PI3K-AKT-mTOR, and MAPK/ERK pathways were universal in pre-treatment samples and acquired or clonally enriched in 80% of patients at progression. Acquired events included loss-of-function alterations in CYLD , activating mutations in KRAS , NRAS and BRAF , and acquired TP53 mutations. In this series, antigen escape via TNFRSF17 /BCMA alteration was uncommon. Instead, pre-existing, acquired and clonally enriched pathogenic variants in NF-κB, PI3K-AKT-mTOR and MAPK cascades, together with TP53 mutation, emerged as a frequent finding. These alterations suggest that deregulation of BCMA-downstream survival signaling, enhanced proliferative fitness, and elevation of apoptotic threshold constitute antigen-loss-independent mechanisms of resistance.

Measurable residual disease (MRD) assessment has become central to risk-adapted treatment strategies in multiple myeloma, yet the prognostic value of MRD negativity following autologous stem cell transplantation (ASCT) across individual high-risk cytogenetic abnormalities (HRCAs) remains unclear. We analyzed 351 newly diagnosed patients who achieved MRD negativity (sensitivity 2.4 × 10⁻⁶) between day 60-100 following ASCT and had baseline cytogenetic data available. Median follow-up was 50.4 months. Standard-risk cytogenetics were present in 46% (n = 162), while 34% (n = 118) had one HRCA and 20% (n = 71) had ≥2 HRCAs. Despite uniform MRD negativity, several HRCAs continued to confer inferior progression-free survival (PFS). Compared with the standard-risk group (4-year PFS 84%), patients with del(17p), 1q21+, t(4;14), or MAF translocations [t(14;16)/t(14;20)] had significantly lower 4-year PFS rates: 56% (HR 2.89; p = 0.0001), 60% (HR 2.46; p = 0.0001), 43% (HR 3.50; p = 0.0001), and 58% (HR 2.51; p = 0.01), respectively. Isolated del(17p) and 1q21+ also conferred inferior PFS. The 4-year PFS was 65% for patients with one HRCA (HR 2.31; p = 0.0004) and 55% for those with ≥2 HRCAs (HR 2.75; p = 0.0001). In this large MRD-negative post-ASCT cohort, several HRCAs, including isolated HRCAs, were associated with inferior PFS, suggesting a potential persistence of high-risk biology despite early MRD negativity.

Metabolic differences between plasma cell disorders reflect coordinated metabolic reprogramming within clonal plasma cells and the bone marrow microenvironment. We applied high-resolution MALDI–FT-ICR mass spectrometry imaging (MSI) to archived FFPE bone-marrow biopsies from patients with MGUS-like bone marrows (defined as <10% clonal plasma cells, including MGUS and solitary bone plasmacytoma with minimal marrow involvement (SBPmm)) and multiple myeloma (MM), integrated with matched bone marrow plasma metabolomics, to map spatial and systemic metabolic alterations. Spatial clustering delineated plasma-cell-rich niches, while Hill-based diversity and β-diversity metrics quantified intra- and inter-compartment heterogeneity. MM niches exhibited elevated 3-hydroxykynurenine, rewired tryptophan–kynurenine flux, and increased nucleotide and bioactive lipid metabolism associated with proliferation. Notably, SBPmm samples displayed MM-like metabolic niches undetectable in bone marrow plasma alone, underscoring spatial heterogeneity. Cross-compartment integration revealed conserved metabolic signatures and systemic redistribution of key metabolites, consistent with ecological reorganization and niche divergence between MGUS-like and MM. These findings establish spatial metabolomics of biopsies as a framework to dissect intramedullary metabolic heterogeneity and enable metabolite-based risk stratification in plasma cell disorders.

Flow cytometry (FC) is essential for detecting measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL), but its use is limited by the expertise and time required for manual analysis. We developed an artificial intelligence (AI) pipeline, Clustering/Classification of All Events, Dimensionality reduction, Downsampling, and Aberrancy Scaling (CCADDAS), to automatically enhance raw FC files, streamlining CLL MRD detection using a single-tube 10-color panel. FC files from 166 MRD-positive and 61 MRD-negative cases were processed in a cloud environment. Automated steps included error correction (FlowCut), clustering (PARC), dimensionality reduction (UMAP), anomaly detection against negative controls, and cluster-informed downsampling that preserved rare MRD events. A deep neural network trained on expert-defined normal subsets enabled automated gating. AI-enhanced files were analyzed in standard FC software, yielding results highly concordant with conventional expert review (R² = 0.98). Downsampling reduced cellularity by 85% and file size by 78%, while retaining low-level MRD events. An AI-generated aberrancy scale distinguished CLL MRD from background B cells with excellent performance (AUC = 0.98). Manual analysis time decreased from 9.0 to 0.9 min per case (90% reduction). CCADDAS provides a largely unsupervised, software-agnostic method that accelerates and simplifies CLL MRD detection without compromising test performance compared to conventional analysis, enabling broader adoption of FC-based MRD testing.

Multiple myeloma progresses from bone marrow-confined disease to systemic forms through processes linked to loss of adhesion and EMT-related programs. We analyzed baseline plasma cell RNA-seq, microarray, and single-cell datasets to derive and validate a dissemination score. In the newly diagnosed MMRF CoMMpass cohort (n = 754), differential expression analyses of circulating plasma cells (n = 592) and PET-CT-confirmed plasmacytomas (n = 118), integrated with 1114 EMTome genes, identified 1357 dissemination-associated genes. Elastic Net Cox regression derived a 30-gene Dissemination Score (DS) that stratified overall survival (54.5 vs 102.5 mo; C-index 0.71) and improved prognostic performance when added to UAMS70 and EMC92 in bootstrap analyses. DS was associated with Adverse stromal interaction (ASI) and stratified high-risk ASI interactions. In an independent Mayo Clinic cohort (n = 133), DS reproduced prognostic discrimination (52.1 vs 113.6 mo, p < 0.01) and predicted earlier dissemination events (2.1 vs 6.8 yrs, p < 0.01). In GSE117156, circulating plasma cells showed consistently higher DS than marrow plasma cells across MGUS, SMM, MM, and AL, with progressive DS increase from controls to MM. In GSE106218, CD138⁺ extramedullary plasma cells had higher DS than marrow plasma cells (p < 0.01). Collectively, DS quantifies dissemination biology.

Progression from monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM) is driven by coordinated metabolic reprogramming within clonal plasma cells and the bone marrow microenvironment. We applied high-resolution MALDI–FT-ICR mass spectrometry imaging (MSI) to archived FFPE bone-marrow biopsies, integrated with matched bone marrow plasma metabolomics, to map spatial and systemic metabolic alterations. Spatial clustering delineated plasma-cell–rich niches, while Hill-based diversity and β-diversity metrics quantified intra- and inter-compartment heterogeneity. MM niches exhibited elevated 3-hydroxykynurenine, rewired tryptophan–kynurenine flux, and increased nucleotide and bioactive lipid metabolism associated with proliferation. Notably, some MGUS-like samples displayed MM-like metabolic niches undetectable in bone marrow plasma alone, underscoring spatial heterogeneity. Cross-compartment integration revealed conserved metabolic signatures and systemic redistribution of key metabolites, consistent with ecological reorganization and niche divergence during progression. These findings establish spatial metabolomics of biopsies as a framework to dissect intramedullary metabolic heterogeneity and enable metabolite-based risk stratification in plasma-cell disorders.