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Abstract Background Etentamig is a second-generation B-cell maturation antigen (BCMA) x CD3 bispecific antibody that has shown promising efficacy and a favorable safety profile in heavily pretreated patients with relapsed/refractory multiple myeloma (RRMM). Etentamig possesses a unique combination of 2 high-affinity BCMA binding domains, coupled to a low-affinity CD3 binding domain that have been shown to reduce the negative impact of soluble BCMA (sBCMA), decrease risk for cytokine release syndrome (CRS), and drive sustained T-cell activation with reduced immune exhaustion in preclinical models of MM (Blood 2023;142 [suppl 1]:4666). Here, we describe correlative biomarker analyses from Phase 1 (NCT03933735) and Phase 1b (NCT05650632) clinical trials of etentamig in RRMM designed to assess the impact of sBCMA on clinical response and pharmacodynamic changes in proinflammatory cytokines, and T-cell redistribution, activation, proliferation, and exhaustion. Methods Peripheral blood and serum samples from patients with RRMM enrolled in Phase 1 and Phase 1b open-label studies who received etentamig monotherapy were collected at baseline, on treatment, and at disease progression. The Phase 1b study consisted of dose-optimization (DO) and dose-expansion (DE) phases. During DO, patients received a single step-up dose (SUD) of intravenous etentamig (2 or 4 mg) on Cycle (C) 1 Day (D) 1, followed by the target dose of 60 mg on D4. In DE, based on DO findings, patients received the recommended etentamig SUD (2 mg) on C1D1, followed by 60 mg on D4 with modified dexamethasone predosing. Samples were analyzed by flow cytometry for immune cell populations, Luminex® for cytokines, and electrochemiluminescence for sBCMA. Peripheral blood mononuclear cells and bone marrow mononuclear cells (BMMC) at baseline and on-treatment were subjected to single-cell Cellular Indexing of Transcriptomes and Epitopes sequencing (scCITEseq) and single-cell T-cell receptors (scTCR) / B-cell receptors clonality analyses. Results While baseline serum sBCMA levels did not correlate with clinical response to etentamig at the selected dose level of 60 mg every 4 weeks (Q4W) with 2 mg SUD, a reduction in sBCMA levels over time was associated with best International Myeloma Working Group response. Etentamig treatment led to a rapid and transient increase of proinflammatory cytokines and promoted T-cell redistribution, activation, and proliferation. Maximum reduction in peak levels of CRS-related cytokines, including (interleukin [IL]-6, IL-8, IL-10, tumor necrosis factor–a), were observed in DE compared with DO and 60 mg Q4W without SUD. Peak induction levels of proinflammatory cytokines during C1 correlated with occurrence of CRS (≥Grade [G] 1) as well as clinical response across dose levels. A modified dexamethasone premedication schedule of 2 mg/60 mg Q4W cohort for C1 resulted in significantly reduced baseline levels of IL-6 and key macrophage-associated cytokine/chemokines (eg, monocyte chemoattractant protein-1 and macrophage inflammatory protein-3 beta). Incidence of CRS was lowest in the 2 mg/60 mg Q4W cohort (26% G1, 4% G2, 0% ≥G3). Higher T-cell to myeloma cell ratio in BMMCs at baseline correlated with better response to etentamig. scCITEseq and scTCR analysis revealed that at baseline, responders also had greater CD8+ T-cell cytotoxic activities, increased TCR clonality, and elevated inflammatory signatures in T-cells from both peripheral blood and bone marrow. Etentamig at a dosage of 60 mg Q4W resulted in significant clonal expansion of CD8+ effector T-cells and enhanced gene expression signatures related to activation and cytotoxicity in both CD8+ T effector and natural killer cells. Along treatment cycles, responders maintained their T-cell fitness, high clonality, and low T-cell exhaustion levels. Frequency of baseline CD4+ or CD8+ T-cell exhaustion (programmed cell death receptor 1+/ T cell immunoglobulin and mucin-domain containing-3 +) did not impact clinical response at optimal dose level of 2 mg/60 mg Q4W etentamig. Conclusions Responses to etentamig were independent of baseline sBCMA levels and resulted in rapid and transient proinflammatory cytokine production that promoted robust T-cell redistribution, activation, and proliferation indicating that the optimal dose of etentamig (2 mg/60 mg Q4W) maximizes its clinical potential as a convenient, safe, and effective therapy for MM.

Abstract Background: Flow cytometry (FC) is widely utilized for the identification of measurable residual disease (MRD) in multiple myeloma (MM), providing an essential laboratory indicator for prognostic assessment and therapeutic management. Unfortunately, the high level of expertise required, long manual analysis time, and complexity of FC data files currently limit the offering of this testing to selected reference laboratories. Methods: We developed an artificial intelligence (AI) pipeline to automatically enhance unaltered FC files, minimizing the expertise and manual analysis time needed to detect MM MRD using the 2-tube Euroflow Consortium Panel (Cytognos). Raw FC files corresponding to 150 MRD-positive bone marrow samples (MRD < 5%, median = 0.0022%), and 39 MRD-negative samples were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline, deployed on a custom clinical-grade cloud environment (Biolegend). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), cluster-informed and density-based downsampling with preservation of low-event subsets, dimensionality reduction (UMAP), and cluster-based anomaly detection compared to negative controls (29 bone marrow aspirates). In addition, a deep neural network (DNN) trained on expert-defined subpopulations from negative controls was included for automatic gating of normal subsets (Tensorflow v2). AI-enhanced data files were analyzed using a general-purpose flow cytometry analysis software (Kaluza v 2.3, Beckman Coulter), and %MRD estimates were compared to reported results based on traditional expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells to be manually analyzed from 4.1 million to 196,761 cells per tube, on average (95% cellularity reduction); resulting in a smaller FC data file (from 203.2 MB to 20.5 MB; 90% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 88% and 77% retention, for 0.01-0.001% MRD and < 0.001% MRD, respectively). True number of events were accurately estimated on gated subsets using an “upsampling factor” parameter [observed # events x mean (upsampling factor)]. Gating of normal subsets was completely automated using a DNN classifier parameter. In 96% of positive cases, MRD was rapidly identified as immunoglobulin light chain-restricted plasma cells on a single pre-calculated UMAP plot (tube 2), after dual coloring for kappa and lambda. On the remainder 4% of positive cases, MRD was easily segregated from benign plasma cells using the AI-generated “aberrancy scale” parameter or other strategies. The use of AI-enhanced files reduced manual analysis time from 15.05 minutes (SD = 6.84) to 1.29 minutes (SD = 0.90) per case, on average (p < 0.0001) (91% reduction of manual analysis time). MM MRD was detected in all positive cases above limit of detection for our assay (≥0.0002%), with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 0.9841, P < 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw MM MRD FC data from the Euroflow Consortium panel into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of MM MRD in clinical diagnostics, reducing the number of cells analyzed by 95% and manual analysis time by 91%, without impacting test performance. Moreover, CCADDAS can be utilized for any laboratory-developed MM MRD assay, and its small-sized export is compatible with any clinical FC software, computing platform, and analysis strategy. Adoption of CCADDAS is likely to facilitate the implementation of MM MRD FC analysis by more clinical laboratories.

Abstract Background Central nervous system involvement in multiple myeloma (CNS-MM) is a rare but highly aggressive manifestation. Limited data exists regarding its incidence, biology, treatment patterns, and outcomes. A deeper understanding of the features unique to CNS-MM will help inform risk stratification and management strategies. Methods We conducted a retrospective study of CNS-MM patients at our institution between January 2000 and December 2023. We identified 305 MM patients with pathology-proven extramedullary disease (EMD). CNS involvement was defined as the presence of parenchymal (brain or spine) or leptomeningeal involvement by MM. Univariate analysis was performed to compare the clinical characteristics between EMD patients with and without CNS-MM. Results Out of 305 patients with EMD, we identified 22 patients with CNS-MM. Common symptoms prompting evaluation of CNS-MM were headaches (32%), numbness or tingling (32%), weakness (27%), confusion (23%), falls (23%), and visual disturbances (14%). Among these patients, 17 (77%) had detectable disease in cerebrospinal fluid (CSF), 12 (55%) in the brain parenchyma, 4 (18%) in the spinal cord parenchyma and 6 (27%) in the leptomeninges. Of the CNS-MM patients, the median age at diagnosis of MM was 58.3 years and the median time to CNS-MM involvement was 16.2 months, with 3 (14%) patients having CNS involvement at diagnosis. The predictors of CNS-MM development were high-risk cytogenetics (OR 2.9, p=0.04) and presence of additional visceral organ involvement outside of the CNS (OR 3.4, p=0.03). EMD was multifocal in 20 CNS-MM patients (83%) and visceral organ involvement [most commonly lymphadenopathy (27%), solid organ (18%) and pleural fluid (14%)] was present in 14 (64%) of these patients, compared to 54% in EMD patients without CNS-MM. High-risk cytogenetics [del(17p), 1q gain/amplification, t(4;14), t(14;16), t(14;20)] were seen in 16 (73%) patients, most commonly 1q gain or amplification (40%), t(4;14) (23%), and del(17p) (18%). Five (23%) exhibited a double-hit profile. In comparison, 113 (40%) EMD patients without CNS-MM had high risk cytogenetics. The median overall survival (OS) from diagnosis of CNS-MM was 6 months (95%CI:1.26-36) compared to 15.6 months (95%CI: 12-18) from diagnosis of EMD in EMD patients without CNS-MM (p=0.05). Median OS from diagnosis of MM in the CNS-MM cohort was 36 months (95%CI: 22.8-NA) compared to 55.2 months (95%CI: 45.6-68.4) in EMD patients without CNS-MM (P=0.11). When excluding CAR-T or bispecific therapy recipients, median OS from diagnosis of MM for CNS-MM was 22 months versus 48 months in EMD patients without CNS-MM (P=0.02). Overall response rate in our CNS-MM cohort with first treatment for EMD was 32% compared to 48% in EMD patients without CNS-MM (p=0.04). In patients with CNS-MM, median progression-free survival (PFS) from CNS-MM diagnosis was 3.3 months (95%CI: 0.8-12) versus 3.9 months (95%CI: 3-5.1) in EMD patients without CNS involvement (p=0.2). Six (27%) CNS-MM patients received CAR-T; 3 received cilta-cel, 2 received ide-cel and one received a BCMA CAR-T on trial. Bispecific therapy was used in 4 (18%) patients, with 2 receiving Teclistamab and 2 Talquetamab. Three (14%) CNS-MM patients received both CAR-T and bispecific therapy. The median PFS on CAR-T therapy was 10.5 (95%CI: 5.6-NA) months and 12.7 (95%CI: 10.2-NA) months on bispecific therapy. The median OS from diagnosis of CNS-MM for patients who received CAR-T or bispecific therapy was 36 months (95%CI: 14.0-NA), compared to 1.2 months (95%CI: 0-14.4) for those who did not (p=0.002). Six (27%) patients with CNS-MM, all of whom had CSF involvement of disease, received intrathecal therapy; methotrexate in 3, cytarabine in 2, and one receiving both. Two of these patients demonstrated improvement in cerebrospinal fluid findings following intrathecal chemotherapy, in conjunction with other therapies. Patients who received intrathecal chemotherapy had a median OS measured from development of EMD of 10.8 (95%CI: 3.6-NA) months, compared to 15 months (95%CI: 4.8-NA) in those without IT chemotherapy (p=0.59). Conclusion Central nervous system involvement in MM is relatively uncommon and associated with dismal outcomes. High-risk cytogenetics and involvement of other visceral sites are associated with a higher risk of CNS involvement. Intrathecal therapy offers limited value and immune effector therapies represent efficacious options for treatment.

Abstract Background People of color (POC) and patients (pts) from socioeconomically disadvantaged backgrounds often face financial, geographic, and systemic barriers, limiting access to advanced treatments (Tx). A survey was conducted to better understand how race and socioeconomic factors affect pt awareness of and access to bispecific antibodies (BsAbs) and chimeric antigen receptor T-cell (CAR-T) therapies in the US. Methods This study used 30-minute online surveys (March-June 2024) of 1301 pts with RRMM across 7 countries. Results were analyzed using descriptive statistics and χ2 tests, with P<.01 for all comparisons unless specified; these are presented for the 305 US pts. Results The 305 US pts identified as White (n=227) or POC (n=78), which included Black/African American (n=42); Hispanic/Latino (n=24); and Asian, Pacific Islander, or Arab (n=12). Pts self-reported as having easy/very easy (n=92), neutral (n=117), or difficult/very difficult (n=96) financial burden, being employed (n=142) or not employed (n=163), and having ≤secondary (n=72) or >secondary education (n=232). In the US, more POC reported difficult/very difficult financial circumstances than White pts (47% v 26%). Fewer POC stated they were in remission (28% v 49%) or had consulted an MM specialist (41% v 59%, P=.028), even after adjusting for self-reported good financial circumstances. Among pts with neutral-very easy financial situations, stated remission rates were still lower for POC (n=41) v White pts (n=168) (34% v 59%, P=.026), as were MM specialist visits (41% v 66%, P=.022). Pts with >secondary education (n=232) and those who were employed (n=142) had higher stated remission rates than those with ≤secondary education (n=72) and those who were not employed (n=163) (47% v 32%, P=.025 and 66% v 24%, respectively). Physical burden was experienced by more POC v White pts (91% v 66%), even after adjusting for financial circumstances (90% v 65%, P=.011, for POC v White pts in neutral/very easy financial circumstances). POC and pts with difficult financial circumstances (including POC and White pts) perceived their Tx as less effective than expected across several measures, including emotional and mental health, overall impact on daily life, side effects, Tx costs, and the effect of Tx schedule on everyday life. Awareness of BsAbs and CAR-T was lower among POC v White pts (BsAb: 49% v 77%; CAR-T: 67% v 81%, P=.032), pts who were not employed v employed (BsAb: 63% v 77%, P=.015; CAR-T: 74% v 81%, P=.046), pts with ≤secondary v >secondary education (CAR-T only: 75% v 78%, P=.021), and those in difficult/very difficult v easy/very easy financial circumstances (BsAb: 54% v 90%; CAR-T: 64% v 90%). Among pts aware of BsAbs, 18% (n=39) reported that the Tx was offered by their physician. More employed v not employed pts were offered BsAbs (26% v 10%). There were non-statistically significant differences in the proportion offered BsAbs based on race (POC 8% v White pts 21%, P=.065), financial status (difficult/very difficult 15% v easy/very easy 23%, P=.288), or level of education (≤secondary education 15% v >secondary education 20%, P=.428). Among pts aware of CAR-T, 24% (n=56) reported that the Tx was offered by their physician. Fewer POC than White pts were offered CAR-T (6% v 29%), even after adjusting for financial status (10% v 32%, P=.04 for pts with good finances). Among those aware of CAR-T, fewer pts not employed v employed (12% v 37%), pts with ≤secondary v >secondary education (11% v 27%, P=.015), and pts with financial burden (difficult/very difficult 10% v easy/very easy 31%) were offered CAR-T. Overall, fewer POC received CAR-T than White pts (1.3% v 10.7%, P=.03). Additionally, more employed v not employed pts received CAR-T (15.2% v 2.5%) or BsAbs (23.5% v 4.4%). Conclusions Consistent with existing health equity inequalities (Chen et al, Int J Equity Health, 2025), POC and pts in poorer socioeconomic circumstances had less awareness of and access to BsAbs and CAR-T. To address these disparities, partnering with trusted messengers and pt advocacy groups supporting Black, Hispanic, and Asian pts and those with socioeconomic hardships can enhance healthcare infrastructure, support initiatives to help pts navigate advanced Tx options, and develop pt- and physician-targeted education. These strategies can improve awareness and promote Tx equity, ultimately ensuring that all pts with MM receive optimal care regardless of their socioeconomic status and race.

Abstract Background: Social determinants of health (SDOH) are critical drivers of cancer care access and outcomes. In multiple myeloma (MM), access to cellular therapies including autologous stem cell transplantation and CAR-T therapy often depends on formal or informal caregiver support. Traditional SDOH measures (e.g., race, income, or education) are often used as proxies for access to care; however, these measures may overlook individual realities pertaining to cellular therapies where a caregiver is generally required. In response to feedback from a HealthTree Patient Advisory Board questioning the practice of inferring caregiver support, this study evaluated a simple survey question by a large cross-sectional survey. Methods: After development of a dedicated single-question (“What best describes your current caregiving support?”) through iterative feedback from the Patient Advisory Board and MM experts, four key answers were settled on: (1) cohabitation with dedicated caregiver(s) to support care (“family support”); (2) presence of non-cohabiting caregiver(s) to provide dedicated support (“external support”); (3) lack of dedicated caregiver, hence support is arranged when needed (“arrangeable support ”); or (4) lack of dedicated caregiver, hence finding any support is a struggle (“inadequate support”). We deployed this new demographic question from February through July 2025 through HealthTree. Responses were analyzed across SDOH variables using Fisher exact or chi square tests, and random forest classifiers were employed for predictive modeling. Results: Of 1231 respondents, 71% had dedicated caregivers (68%, n=839, with family support, and 3%, n=41, with external support). In contrast, 23% (n=287) of patients routinely arranged support while 5% (n=64) had inadequate support (struggle to find support). The inadequate-support group had significant demographic differences from the sample as a whole: a higher proportion were women (78% vs 56%, p = 0.04), Black (9% vs 4%, p < 0.01), lived >50 miles from their treatment center (17% vs 6%, p < 0.01), and had an annual income <$50,000 (17% vs 6%, p = 0.02). This group had a numerically higher proportion of patients whose employment status was disabled (12% vs 5%, p=0.08) as well. No proportion difference in age (67 ± 9 yr), military status, or severity of diagnosis - precursor disease (17%) v. multiple myeloma (81%). Rates of inadequate support were overall low in patients who lived with only their spouse (1%) or lived with their spouse & children (3%). In contrast, rates of inadequate support were higher (than 5%) in patients who lived alone (21%), divorced (20%), lived with adult children (14%), lived with friends (13%), separated (11%), or widowed (9%) (p < 0.001). However, the arrangeable-support group comprised 12% of patients who lived only with their spouse, 19% of patients who lived with their spouse and children, and 61% of patients who lived with their adult children (p < 0.001). Finally, a random forest classifier failed to identify any individual in the inadequate-support category, suggesting that unmeasured or more complex patterns may underlie this classification. Conclusions: To our knowledge, this is the largest analysis of caregiver support and its relationship with other SDOH in patients with MM to date. Despite the importance of caregiving to transplantation and CAR-T therapy, we found that 5% of patients with MM struggle to find caregiver support; this figure likely underestimates the true prevalence, given potential selection bias among patients who are digitally engaged through the HealthTree platform. Even among patients who lived with their spouses, an additional 10-20% of patients routinely had to arrange caregiving support on a visit-by-visit basis; for patients who lived with their adult children, this proportion was over 60%. The requirement to arrange caregiving support can create time toxicity and financial toxicity for patients, although these factors were not directly queried in our study. Finally, caregiver availability cannot be routinely inferred from traditional SDOH variables. Our data highlight the importance of including caregiver status as a standard demographic question in clinical and research settings. Further interventions are needed to improve caregiver support for vulnerable patients with MM as well as to reduce the duration of caregiver requirements for procedures such as CAR-T therapy.

Abstract Somatic mutations in hematopoietic stem and progenitor cells (clonal hematopoiesis) are known to alter the immune milieu and can result in autoimmune disorders including VEXAS (Vacuoles, E1 enzyme, X-linked, Autoinflammatory, Somatic) syndrome. There are currently no curative strategies for CH-linked autoimmune disorders besides allogenic hematopoietic stem cell transplantation. Recent work has shown a high prevalence of autoimmune syndromes among patients with IDH1/2 mutant myeloid neoplasms (MN). We thus hypothesized that IDH1/2-mutant CH might drive a subset of autoimmune disorders. To further characterize a possible association between IDH-mutant CH and autoimmune disease, we used data from two observational cohort studies, the UK Biobank (N=454,218) and All of Us (N=414,830). We observed 264 individuals with IDH1/2-mutant CH. IDH1/2 CH was associated with autoimmune disease (OR=1.8, p=4e-3), and, when compared to other CH genes with at least 100 carriers, showed the strongest effect size. When comparing autoimmune subtypes, we observed significant associations with polymyalgia rheumatica (OR=4.0, p=1e-3) and giant cell arteritis (OR=8.1, p=1e-6). IDH1/2 CH carriers had elevated CRP levels compared to other CH-carriers (p=1e-2) and non-carriers (p=8e-3). IDH1/2 CH was associated with lower neutrophil counts and higher monocyte counts. While most (68%) had blood counts outside the normal range, only a minority (11%) had severe cytopenias that met criteria for CCUS. Since IDH1/2 CH is associated with both autoimmune disease and blood count abnormalities, we sought to define the frequency, clinical presentation, and outcomes of IDH1/2-associated autoimmune disease among individuals with bone marrow biopsy confirmed CCUS. We used data from three CCUS cohorts: first, a multi-institutional cohort of CCUS patients with systemic autoinflammatory disease (SAID) who were UBA1-negative, second, a hospital-based cohort at Mayo Clinic, and third, a cohort of IDH1 carriers enrolled on a phase II clinical trial of ivosidenib (NCT05030441). Within 193 SAID patients who were free of myeloid neoplasm at the time of recruitment, IDH1/2 pathogenic variants were the most observed CH driver, occurring in 2% of individuals. These individuals (N=4) all had recurrent fevers and dermatologic manifestations, requiring chronic glucocorticoid treatment with 75% having inflammatory arthritis requiring multiple Disease-Modifying Antirheumatic Drugs (range 1-7, median=5). Within the Mayo Clinic cohort, 275 patients with CCUS were serially ascertained. Among those 8 (2.9%) were IDH1-mutant of whom 50% had autoimmune disease (three with seronegative rheumatoid arthritis and one with Sweet syndrome). At last follow up (median 16 months), 2 (25%) of 8 patients had progressed to MN. Within our open-label decentralized trial of ivosidenib (IDH1 inhibitor) in CCUS, we enrolled 20 patients, of whom 5 (25%) had a diverse array of pre-existing autoimmune disorders, including two cases of seronegative rheumatoid arthritis and one case each of Sweet syndrome, polymyalgia rheumatica, and Sjögren's syndrome. Underlying the clinical presentation of all five cases was episodic joint pain, with half also experiencing episodic dermatologic manifestations. The majority (58%, 11/19) of the IDH1mt-CCUS patients had an elevated hsCRP (>=5; range 6.91–46.2) including all four with co-occurring autoimmune disorders (range 7.97–46.2). Out of the 20 enrolled patients, 17 received at least 8 weeks of therapy, of which 9 had elevated hsCRP at baseline. Most of these (7/9) had normalization of hsCRP (falling below 5 mg/L) while on treatment (median time to hsCRP normalization of 58 days). Among the four patients with autoimmune disease, all had subjective improvement in their autoimmune symptoms following treatment with ivosidenib. In conclusion, we show that IDH1/2 CH is associated with autoimmune disorders and is particularly enriched among IDH1/2 CH carriers with co-occurring cytopenias. IDH1/2-associated autoimmune disease can range from classic rheumatic conditions such as rheumatoid arthritis to more severe VEXAS like presentations. IDH inhibition demonstrates efficacy for improving inflammatory manifestations within an ongoing clinical trial. Together these results nominate IDH mutant CH as a driver of autoimmune diseases with therapeutic implications.

Abstract Outgrowth of a founder mutant clone is an initial event in leukemogenesis. The corresponding somatic hit likely conveys a fitness advantage over a polyclonal background. Several factors may modulate the competitiveness of hematopoietic stem or progenitor cells, including aging, genotoxic stress, repeated cycles of stress hematopoiesis, and inherited genetic fitness deficiency. The nature of these conditions provides a particularly fertile environment in which only certain mutant clones thrive. For instance, chemotherapy facilitates expansion of PPMD1MT or TP53MT clones, aging-related loss of fitness predisposes to outgrowth of TET2MT clones, while many inherited bone marrow failure (BMF) syndromes are associated with specific clonal acquisitions due to somatic gene rescue. Identification of factors enabling the expansion of clones with specific mutations may help to develop targeted preventive, screening, or therapeutic strategies. Recently, high levels of erythropoietin (EPO) induced by frequent blood donation were shown to favor the emergence of DNMT3AR882 clones due to their increased EPO sensitivity1. We theorized that this may be a more general phenomenon operating in other diseases characterized by high EPO levels. Thus, we selected 644 patients in whom endogenous serum EPO levels had been assessed and who underwent mutational NGS analysis. Given that this cohort consisted of patients with hematologic neoplasms, EPO levels were broad with the majority falling outside the normal clinical reference range. For this study we used EPO levels ≥100mU/mL as the threshold to define high and low EPO groups. This threshold laid above the 75th percentile for our cohort (M=31.1; IQR=109.7) to capture outliers more likely to reflect biologically significant erythropoietic stress. DNMT3AMT were present in 14% of high EPO and in 12% of low EPO groups (p=0.5). However, in the high EPO group, R882 constituted 32% of DNMT3AMT while in the low EPO group R882 was found in only 11% of DNMT3AMT cases (p=.02). In a reverse fashion, the mean level of EPO in DNMT3AR882 was 441.7mU/mL while it was 215.3mU/mL in other DNMT3AMT carriers (p=0.03) and 186.2mU/mL in DNMT3AWT cases (p<0.001). To assess whether the effect on DNMT3A was dependent on EPO levels rather than anemia, we determined mean hemoglobin levels in R882 (µ=10.2g/dL), other DNMT3AMT (µ=10.4g/dL), and wild type cases (µ=10.9g/dL) and found no significant differences (p=0.33). To assess whether R882-EPO sensitivity was related to EPO-R expression levels, we compared EPO-R expression in R882 (µ=2.7), non-R882 (µ=2.6) mutations, and WT cases (µ=2.8) and found no significant difference. To assess whether the impact of high EPO states was specific to DNMT3AMT we next analyzed the distribution of other mutations. We observed that frameshift mutations in BCOR (p = 0.013) and BCORL1 (p=0.036) were significantly more frequent in high EPO patients. BCORMT was also overall more prevalent in the high EPO group (p=.001), while BCORL1 frequency was similar across EPO strata (p=0.77) showing a similar trend to DNMT3AMT. Beyond these genes, other patterns emerged, particularly within CH-associated genes. Individuals with U2AF1MT had elevated EPO (p=.0087), with variant-specific enrichment: p.S34F was more common in high EPO (p=.038) whereas p.Q157P predominated in low EPO patients (p=0.038). No significant EPO associations were observed for other CH-related genes such as ASXL1, CBL, and TET2. The limitations of this study notwithstanding, these findings support a biologically meaningful relationship between elevated EPO levels and the acquisition or enrichment of specific CH-associated mutations in patients with hematologic malignancies. The association of high EPO with DNMT3AR882, frameshift mutations in BCOR/L1, and variant-specific enrichment in U2AF1 suggests that EPO-driven stress may contribute to clonal selection or expansion. Our study of high EPO levels and DNMT3AMT analysis illustrates that external conditions may influence selection pressure for specific mutations. However, selection pressure may be a complex combination of factors. Non-malignant conditions with high EPO, such as hypoxia or intrinsic BMF, may also create conditions facilitating the acquisition of specific somatic hits. Recognition of evolutionary pressures and their associations with specific mutations may improve our understanding of CH pathogenesis, and conceptualization of therapeutic or preventative measures.

Abstract Background: Paroxysmal nocturnal hemoglobinuria (PNH) is a clonal hematopoietic stem cell disorder caused by PIGA mutations, resulting in complement-mediated hemolysis. Myeloproliferative neoplasms (MPNs) are also clonal hematopoietic stem cell disorders driven by mutations in JAK2, CALR, or MPL, and include polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). Although the coexistence of PNH and and MPN is rare, both entities are associated with anemia and an increased risk of thrombosis. The main objectives of the current study were to characterize the clinical phenotype, and evaluate treatment outcomes, including response to complement inhibition in patients with treatment-requiring PNH and MPN. Methods A Mayo Clinic database search was conducted following Institutional Review Board approval to identify patients with PNH and MPN. Between January 2000 and June 2025, 11 patients had a pathologic diagnosis of MPN per the International Consensus Classification, and PNH requiring therapy. Clinical features, including hemolysis and/or thrombosis, and therapeutic interventions were recorded. Results The study cohort included 11 patients (median age 63 years; 73% male) with MPN (6 with PMF, 3 ET, 1 post-ET MF, 1 post-PV MF) in whom PNH was diagnosed concurrently (n=5), or at a median of 9 years (range: 2-15) after MPN diagnosis (n=6). In 9 of 11 patients (82%), PNH workup was initiated due to hemolytic anemia, while in 2 patients (18%), it was prompted by splanchnic vein thrombosis. Transfusion-dependent anemia was present in 6 patients (54%). PNH clone size [median (range)] was 0.58% (0–10.96%) in red blood cells, 18.82% (0–91.2%) in granulocytes, and 23.27% (0–98.8%) in monocytes. MPN driver mutations were evaluated in 8 cases and identified mutations in JAK2 (n=3), CALR (n=4), and MPL (n=1); two additional cases were negative for JAK2 mutations. Next-generation sequencing (NGS) was performed in 6 patients and identified ASXL1, ZRSR2, and SF3B1 mutations in one case each. Cytogenetic analysis (n =10) demonstrated an abnormal karyotype in 3 patients (30%), all of which were categorized as favorable risk. First-line treatment for PNH included complement inhibitors in 9 patients (82%), with eculizumab (n=8), or ravulizumab (n=1). Hemolysis was controlled in only 2 of 9 (22%) patients; one patient treated with eculizumab achieved durable anemia response for five years, while another patient achieved transfusion-independence within three months of eculizumab initiation. 3 patients underwent allogeneic stem cell transplant (ASCT); 2 achieved remission with follow up exceeding 5 years, while the third patient experienced PNH recurrence five months after transplant. The latter received donor lymphocyte infusions and eculizumab was re-initiated resulting in eradication of the PNH clone. Among patients treated with complement inhibitor and not transplanted, 4 of 6 (67%) were transfusion-dependent at last follow-up. Notably, one patient with PNH clone size of 91.19 % in granulocytes, had ongoing hemolysis despite dual complement blockade with ravulizumab and danicopan. Treatment for MPN included hydroxyurea (n=5), ruxolitinib (n=4), pacritinib (n=1), and erythropoiesis stimulating agents (n=4). Additionally, two patients each received antiplatelet therapy alone, anticoagulation alone, or a combination of antiplatelet and anticoagulation. During a median follow-up of 8.4 years (range, 0.6-19), 2 patients (18%) experienced venous thrombosis, and 1 patient (9%) had progression to myelodysplastic syndrome. 5 patients (45.5%) died, with sepsis as the cause of death in 3 cases. The incidence rate of thrombosis was 22 events per 1,000 person-years. Recurrent thrombosis was observed in a 68-year-old male with CALR-mutated MF and PNH clone size of 14.65% in granulocytes, with history of portal vein thrombosis. Despite treatment with eculizumab and low-molecular weight heparin, he developed thrombosis involving the inferior vena cava, common iliac, and hepatic veins. The second event occurred in a patient who developed deep venous thrombosis, while in remission and not on anticoagulation. Conclusion The current study represents the largest series of patients with treatment-requiring PNH and MPN. It reveals a predominance of MF patients, particularly among those harboring CALRmutations, and underlines suboptimal efficacy of complement inhibitor therapy in this patient population.

Abstract The importance of bone marrow (BM) microenvironment and its immune interactions within and their role in chronic myelomonocytic leukemia (CMML) disease progression have not been well studied. Our previous work in CMML identified type 1 helper T cell (Th1) and CD4 terminal effector cell suppression in peripheral blood from untreated patients, in comparison to age-matched normal controls (Mangaonkar A et al. Blood Adv 2020). Furthermore, clonal dendritic cell (DC) aggregates in CMML are present in approximately 20-30% patients at diagnosis, display significant cell heterogeneity (plasmacytoid DCs + myeloid DCs and other myeloid/immune cells), express indoleamine 2,3-dioxygenase-1 (IDO), associate with T regulatory cell (Treg) expansion, and transformation to AML [blast transformation or BT, Lucas N et al. Leukemia 2019, Mangaonkar A et al. Blood Adv 2020, and Mangaonkar A et al. Blood 2023 (abstract)]. The purpose of this study was to define the myeloid-immune cell interactions within the BM microenvironment critical for CMML progression to AML. To phenotypically identify cell types and their interactions within the CMML BM, we used single-cell type spatial omics through two distinct modalities on archival formalin-fixed paraffin-embedded (FFPE) BM biopsy specimens. Ten serial CMML samples at chronic phase [CP, n=5, median BM blast% 5 (2-15)] and at BT [n=5, median BM blast% 60 (25-74)] were used for both techniques. Spatial transcriptomics was conducted through GeoMx Digital Spatial Profiler (DSP) platform that uses UV-photocleavable, spatial-barcoded oligo probes to profile the whole transcriptome in defined regions of interest (ROI). CD123 (marker for DC-myeloid aggregates) immunofluorescence antibody was used to identify and segment ROI. 16-color flow cytometry was done to validate cell-specific expression. Spatial cellular phenotyping of immune cells on the same samples (serial cuts) was done through multiplex imaging by co-detection by indexing (CODEX) using 27 markers: CD31, Ki67, CD11b, CD20, CD44, Ecad, CD123, CD107a, CD8, CD11c, CD45RO, FoxP3, CD14, PDL1, CD56, CD68, CD163, PD-1, CD38, HLA-DR, CD4, IDO, CD45, PanCK, Na/K/ATPase, CD3e, and DAPI. CD8T+ cell anergy was demonstrated through CFSE cell proliferation assay. Benjamini-Hochberg correction was used to adjust for multiple hypotheses. After filtering poor quality ROIs [ROIs with total read count < 1000 were removed, ROIs with trim, stitch, alignment < 80% or saturation < 50% were removed], 3rd-quartile normalization [BiocGenerics (v. 0.48.1)] and batch correction [limma (v. 3.58.1)], we compared transcriptomics signatures between CD123+ and CD123- ROIs in all samples. Results showed that LIPA, SLC4A1, HBA2, TIMD4 and HMOX1 werethe top five genes upregulated in CD123+ areas. Among all gene transcripts, TIMD4 expression increased the most (Log2FC=0.6, P-value=8.79E-10, FC=1.52, FDR=1.68E-07) when CD123+ ROIs were compared between CMML-CP and CMML-BT serial samples, suggesting a biological relevance in disease progression. Previous work in metastatic peritoneal cancer has established that TIM-4 expressing macrophages suppress Th1/Th2 ratios and impair CD8+ T cell function (Chow A et al. Cancer Cell 2021). To confirm expression at the protein level, we used a multicolor flow cytometry panel and confirmed that TIM4 is expressed on macrophages (Lineage-negative, HLA-DR+, CD68+ cells) in independent CMML BM aspirate samples (n=3). Next, we conducted spatial phenotyping using CODEX on serial cuts from the same serial samples used for spatial transcriptomics. Anatomical co-localization of macrophages with leukemic cells, monocytes and T cells was visually inspected and confirmed. Cell-interaction analysis (after Benjamini-Hochberg correction) indicated that macrophages-monocyte, macrophage-leukemic cells, and macrophage-memory CD4+T cells were significantly (P<0.05) increased in CMML-BT versus CMML-CP. CD8T+ cell anergy was confirmed through a CFSE cell proliferation assay in untreated CMML PBMC samples. In conclusion, our data suggests that TIM4-expressing macrophages and their interactions with monocytes/monocyte precursors and immune cells within a DC aggregate create a protective leukemic stem cell niche and T cell tolerance, promoting blast transformation in CMML.

Abstract Background The expansion of hematopoietic stem and progenitor cells (HSPCs) in the bone marrow is universally associated with adverse outcomes and disease progression in all chronic myeloid neoplasms, to include chronic myelomonocytic leukemia (CMML). However, HSPCs are a heterogeneous cellular compartment and the impact of its composition on clinical outcomes remains poorly defined. Single cell RNA-sequencing (scRNA-seq) represents an optimal strategy to annotate cellular composition but no large clinically annotated scRNA-seq datasets are currently available. To address this, we have embarked to generate a clinically annotated 500 patient CMML scRNA-seq atlas from CD34+ enriched BMNCs to establish among the largest clinically and genetically annotated single cell references for any chronic myeloid malignancy. Here we report the interim analysis of 113 clinically annotated cases that demonstrates massive reshaping of HSPC composition that is independently a predictor of overall survival. Methods Single-cell RNA expression profiling was performed on 113 CD34-enriched CMML BMNC samples using 10x Genomics GEM-X Universal 3' Gene Expression v4 (4-plex) chemistry. Libraries were sequenced on NovaSeq S4 flow cells in two runs, and data were processed using Cell Ranger to generate gene expression matrices. A total of 502,659 single cells from 113 samples were processed using Seurat. The upper threshold for gene count was defined as the mean number of detected genes plus two standard deviations data normalization was performed using the log-normalization method. Batch correction and integration were conducted with Harmony to mitigate technical variation across pools. Descriptive statistics were used to tabulate clinical and genetic parameters. KM curves, log-rank test, and cox regression were used to calculate survival, test for statistical differences, and perform multivariate analysis, respectively. Results To derive high resolution cell annotations, we employed the SingleR algorithm using a large hematopoietic reference of over 300,000 single cells (Zhang, X. Nature Immunology, 2024). Using this approach a diverse set of cell types were identified that included HSCs, 23 myeloid progenitors, 18 erythroid progenitors, and 18 lymphoid progenitors. This diverse cellular repertoire displayed massive reshaping relative to the normal CD34 enriched reference including a >53x expansion of multilineage GMPs and a >100x depletion of B-cell progenitors consistent with the known myeloid skewing seen in this disease. To understand whether expansion of specific progenitors was associated with outcomes we iteratively evaluated key progenitor populations and observed that expansions of HSC (HR 0.468 p<0.005) and MEP (HR 0.485, p<0.005) were associated with improved survival while GMP (HR 1.729, p=0.029) expansions were associated with inferior survival when using a cut point derived with a maximally selected ranks statistics approach. To achieve a more comprehensive metric of HSPC composition we explored GMP ratios to include GMP/HSC ratios and GMP/MEP ratios and observed that both were associated with inferior survival in univariate and multivariate when considering clinical and genetic features such as the CPSS-molecular prognostic score and FAB classification. When considering both GMP/HSC and GMP/MEP ratios in a multivariate model only GMP/MEP ratios and CPSS molecular remained independently prognostic. As an orthogonal approach, pseudotime projections were carried out using a reference map from Setty et al., 2019 to calculate differentiation potential and branch probabilities. This approach enabled both cell identity assignment and placement of cells along the hematopoietic differentiation trajectory. Samples were classified as monocyte-biased, MEP-biased, or normal-like, as previously described (Ferrall-Fairbanks et al. 2022) and demonstrated that increased GMP ratios are associated with a monocytic biased trajectory and that this trajectory is also associated with overall survival. Conclusion This study represents an interim analysis of among the largest scRNA-seq cohorts generated in chronic myeloid malignancies. This enabled us, for the first time, to demonstrate that HSPC composition is independently prognostic even when considering modern CMML prognostic models. As this cohort continues to increase, validation of these findings and examining the impact of therapy on HPSC composition in serial samples will be prioritized.