Olteanu, Horatiu

Objective To evaluate the efficacy of cytokine‐directed therapies in VEXAS (vacuoles, E1 enzyme, X‐linked, autoinflammatory somatic) syndrome using previously proposed treatment response criteria. Methods All patients with genetically confirmed VEXAS syndrome who received targeted therapies with JAK inhibitors (JAKi), tumor necrosis factor α inhibitors (TNFαi), or interleukin‐1 (IL‐1) or IL‐6 inhibitors were identified. Clinical and biochemical data were recorded for patients at time of targeted therapy initiation, and at 3‐, 6‐, and 12‐month follow‐up time points. Treatment response was labeled as complete response (CR; normalization of C‐reactive protein [CRP], prednisone <10 mg/day, and resolution of disease activity), partial response (PR; >50% improvement in CRP, >50% prednisone dose reduction, improvement or resolution of disease features), treatment failure (TF; worsening CRP, increasing prednisone, worsening of disease features), or no response (not meeting criteria for CR, PR, or TF). Results Overall, 52 patients received an IL‐1i, IL‐6i, or JAKi therapy. At six‐month follow‐up, 3 of 19 IL‐6i patients experienced CR, 4 of 19 experienced PR, and 7 of 19 experienced TF. 2 of 17 patients receiving JAKi experienced CR, 3 of 17 experienced PR, and 6 of 17 experienced TF. No patients receiving IL‐1i or TNFαi experienced CR, and only 1 of 5 IL‐1i patients experienced PR. Among those still receiving biologic therapy at 12 months, only 1 of 11 receiving IL‐6i and 0 of 7 receiving JAKi experienced CR. Conclusion IL‐6i and JAKi therapies demonstrated limited, unsustained efficacy for management of VEXAS syndrome. IL‐1i and TNFαi therapies were not efficacious. Useful application of previously reported treatment response criteria was demonstrated, but future incorporation of hematologic parameters may be beneficial.

ABSTRACT Hypomethylating agents (HMA) and allogeneic hematopoietic stem cell transplantation (alloHSCT) have both demonstrated remissions in VEXAS; however, comparative data is lacking. We conducted a multicenter, retrospective analysis of 66 patients diagnosed with VEXAS syndrome treated with HMA ( n = 35) or alloHSCT ( n = 31). Baseline characteristics such as genetics, co‐morbidities, and performance status were balanced between the groups, except older age in the HMA group. Median follow‐up from therapy initiation was 18 months (95% CI: 11–26), and 14 (21%) deaths were reported (alloHSCT n = 3; HMA n = 11). Among all evaluable patients within the alloHSCT cohort, all patients achieved molecular remission, and a substantial proportion of patients discontinued glucocorticoids (58%). In contrast, HMA therapy was associated with lower but meaningful rates of molecular remission (22%) and glucocorticoid discontinuation (6%). In a real‐world setting, HMA therapy was associated with a high discontinuation rate related to toxicity or lack of response. On multivariable analysis adjusted for age and Charlson Comorbidity Index, alloHSCT was associated with improved overall survival (HR = 0.20, 95% CI: 0.05–0.81; p = 0.024). This association remained consistent across multiple ancillary sensitivity analyses, including restriction to transplant‐eligible patients, patients aged ≤ 75 years, 1:1 matching, and propensity score‐based weighted analyses. Although limited by retrospective design, these findings suggest that alloHSCT remains an attractive and potentially curative strategy in selected patients with VEXAS. Prospective validation of these findings is warranted.

Abstract Background: VEXAS (Vacuoles, E1 enzyme, X-linked, Autoinflammatory, Somatic) syndrome is a hemato-inflammatory syndrome due to somatic mutations in UBA1 gene. Recently, DNA methyltransferase inhibitors (specifically, 5-azacitidine) and allogeneic hematopoietic stem cell transplantation (alloHSCT) have shown success in eradication of UBA1 clones and clinical remission in some patients. This study aims to provide a comparative efficacy analysis between DNA methyltransferase inhibitors (hypomethylating agents/HMA) versus alloHSCT in VEXAS syndrome patients. Methods: This is a multi-center, retrospective cohort study. UBA1 mutations were detected either through next generation sequencing [NGS, limit of detection (LOD) 2% variant allele fraction (VAF)] or quantitative droplet digital polymerase chain reaction testing (ddPCR, LOD 0.5% VAF). Outcomes of interest included overall survival (OS), glucocorticoid (GC) discontinuation/reduction in dose (prednisone &lt; 10 mg/day), and complete molecular remission (CMR, defined as an undetectable UBA1 mutation). OS was calculated from the time of diagnosis to death or last follow up. Cumulative incidence of reduction in GC dose was calculated using competing risk analysis after accounting for death. Results: Between years 2020-2024, a total of thirty-nine male patients (median age 67, range 45-82 years) with VEXAS syndrome, necessity for therapy (GC-refractory inflammation and/or progressive marrow failure/myeloid neoplasm) and confirmed UBA1 pathogenic mutation were included. Of these, 18 (46%) received HMA therapy (5-azacitidine 72% and Decitabine 28%) with a median of 4 (range 1-36) cycles. and 21 (54%) underwent alloHSCT (MSD=4, MUD=12, MMUD=2, and haploidentical=3), all received reduced intensity conditioning (Flu/Mel =11, Flu/Bu=5, Flu/Cy/TBI=2, and TBF=3) and 91% received post-transplant cyclophosphamide-based graft-versus-host disease (GVHD) prophylaxis (only 2 patients in alloHSCT group received prior HMA without benefit). Baseline characteristics were well-balanced between HMA and alloHSCT groups; age at treatment (median 70 vs 65 years, p=0.2), and UBA1 M41V subtype (25% vs 40%, p=0.3), cytogenetic and non-UBA1 mutation abnormalities (p=0.4), prior lines of therapy (median 4 in both, p=0.5), time from diagnosis to therapy (median 7 vs 13 months, p=0.15), indication for therapy (p=0.12), Charlson CI scores (median 4 in both, p=0.4) and HCI-CI scores (median 4 in both, p=0.7). At a median follow-up of 45 (range 7-57) months, 11 (28%) deaths (alloHSCT=3 and HMA=8) were documented. Causes of death included infections (n=6), cardiac disease (n=2), poor graft function (n=2) and pulmonary GVHD (n=1). Compared to HMA therapy, alloHSCT group demonstrated higher rates of GC discontinuation (52% vs 6%, p&lt;0.01) and CMR (evaluable= 22, 100% vs 50%, p&lt;0.01). In the HMA group, 50% (n=9) discontinued therapy after a median of 4 months due to infectious complications or lack of benefit. In the alloHSCT group, 3 (14%) patients developed grade 2 acute GVHD (1-skin, 2-gut, no grade 3-4 acute GVHD) and 4 (19%) experienced moderate to severe chronic GvHD (2-lung, 2-mucocutaneous). In multivariable analysis (MVA) adjusted for age and HCT-CI, alloHSCT was identified as the strongest favorable predictor for OS (HR 0.18 95% CI 0.04-0.8) with similar results when analysis was restricted to transplant-eligible patients &lt; 75 years of age (HR 0.1, 95% CI 0.03-0.6). In a 6-month post-treatment landmark analysis (n=31 evaluable), patients who underwent alloHSCT (n=17) exhibited numerically superior GC discontiuation rate (29% vs 14%, p=0.049) and subsequent OS compared to HMA (2-years OS: 84% vs. 61%, median not reached vs 25 months, 95% CI: 0.06–1.7). In a post-treatment competing risk [GC dose reduction (death)] analysis alloHSCT was associated with superior 2-, and 3-year rates of 57% (10%), and 76% (10%), compared to HMA 22% (24%), and 31% (42%) [p&lt;0.01 (0.04)]. AlloHSCT was also associated with higher rate of CMR (evaluable= 21, alloHSCT N=16 and HMA N=5, 100% vs 60%, p=0.01). At the last follow up, none of the patients who achieved CMR had re-emergence of the UBA1 clone (despite early mixed chimerism). Conclusions: This study represents the largest comparative analysis of alloHSCT versus HMA therapy in VEXAS syndrome, and confirms superiority of alloHSCT in achieving molecular remission, GC discontinuation/dose reduction, and overall survival. Prospective clinical trial confirmation is warranted.

Abstract Introduction: VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) is an adult-onset autoinflammatory disorder defined by somatic UBA1 mutations in hematopoietic stem cells (HSCs). Given the higher median age at onset and prevalent inflammation, approximately 60% of patients have concurrent clonal hematopoiesis (CH) involving epigenetic regulators, DNMT3A and TET2. We have previously demonstrated (Gutierrez-Rodrigues, Blood 2023) that DNMT3A and TET2 mutations in VEXAS are independently associated with increased mortality, without an increased risk of myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). To define the role of CH in VEXAS, we investigated the impact of CH on mortality and related epigenetic dysregulation in primary VEXAS patient samples and validated these findings using an adenine base-editing (ABE) strategy to model cooccurring UBA1 and DNMT3A mutations in CD34+ HSCs. Methods: After IRB approval, 114 VEXAS patients from the Mayo Clinic and the NIH were retrospectively assessed, including 104 screened for CH by error-corrected DNA sequencing (ECS). DNA methylation changes were identified using the Illumina Infinium MethylationEPIC BeadChip and genome-wide using Biomodal 6-base sequencing on peripheral blood samples (PB). Cellular indexing of transcriptomes and epitopes (CITE)-seq was performed using the 10x Chromium 3' GEX platform; 21,114 pooled cells were subjected to UMAP dimensionality reduction, followed by Azimuth based cell identification. HSCs from different donors were base edited for UBA1 M41T mutation with and without cooccurring DNMT3A splice-site mutations. Levels of cytokines in cell culture supernatants were measured using Olink technology, transcriptomic changes were assessed using bulk RNA-seq. Statistical analyses were carried out using R. Results: Patients' median age was 68 (62-74) years; 95% were white and 99% were males. 39% had a pathogenic CH variant, with DNMT3A (18%; median VAF: 42%; range 6-46%) and TET2 (11.5%; median VAF: 11%; range: 2-37%) being most common (2% had both). While the presence of any CH mutation did not impact survival (7.7 vs 11.5yrs, p =0.69), DNMT3Amt CH had an age-independent negative impact on mortality (9.2 vs 5.6yrs, p=0.02), without an increased risk of MDS/AML (p=0.31). Changes in DNA methylation were profiled using the methylation array in 19 VEXAS patients without CH (Median Age: 68 years) and in 9 with DNMT3Amt CH (Median Age: 74 years), respectively. VEXAS samples with DNMT3Amt CH displayed a hypomethylation phenotype compared to VEXAS samples with no CH, with 4,096 unique hypomethylated CpGs, occurring mainly at gene enhancers (Enh) and transcriptional states (Tx, TxWk). This loss of DNA methylation was enriched in inflammatory pathways with extensive hypomethylation observed in the promoters and gene bodies of IL-1 family genes. CITE-seq performed on 7 VEXAS samples (4 DNMT3Amt CH vs 3 no CH) demonstrated an upregulation of gene expression predominantly in T-cell subsets. This increased expression was enriched in inflammatory genes, largely centered around IL-1 and IL-18 family signaling. Pathway analysis revealed an enrichment in respiratory electron transport and oxidative phosphorylation (oxphos), with the VEXAS samples with DNMT3Amt CH demonstrating significantly increased oxphos gene signatures. Base editing of CD34+ cells was done using an adenine base-editing (ABE) strategy to model dual UBA1 and DNMT3A splice-site mutations. We observed that the HSCs base edited for the UBA1 M41T recapitulated the cellular hallmarks of VEXAS demonstrating reduced ubiquitination, unfolded protein response (UPR) activation, and increased inflammation. The addition of cooccurring DNMT3A mutations resulted in decreased DNMT3A expression, further increasing inflammatory markers, confirming altered inflammatory and metabolic pathways. Conclusion Our study validates the age-independent, negative prognostic impact of DNMT3Amt CH on survival in VEXAS syndrome. DNMT3A mutations mediate epigenetic and transcriptional priming associated with upregulation of inflammatory and mitochondrial function pathways. These findings have been validated in a base edited cell model with UBA1 and DNMT3A co-mutations.

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 &lt; 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 &lt; 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 &lt; 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 &lt; 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 Clinical flow cytometry laboratories are facing rising test volumes, greater assay complexity, and increasing requirements for quality control and assay validation. In response, the International Clinical Cytometry Society (ICCS) conducted a workload survey in early 2023 to gather updated information on assay volumes, complexity, staffing, and technology. Data analysis focused on identifying correlations between length of time to introduce new assays and other factors as a means to gain insight about laboratories that seem to be either adapting or struggling. Flow cytometry assays were categorized into 3 levels of technical/interpretative complexity: high (e.g., measurable/minimal residual disease (MRD assays)), moderate (e.g., leukemia/lymphoma assays (Assays L&L ), excluding MRD assays), and low (e.g., CD4 count). Annual assays per staff member were calculated according to staff involved in case sign‐out (Staff Signout ) or other laboratory operations (Staff LabOps ). Respondents were from 101 laboratories in the United States (69.3%), Canada (4.0%), and other countries (26.7%). Low, moderate, and high technical/interpretative complexity assays were performed in 85.1%, 97.0%, and 47.5% of all laboratories, respectively. Median annual total assays (Assays Total ) per laboratory were 3515 and, based on complexity, were 1518.5 (low), 1808.8 (moderate), and 350 (high). Among all laboratories, the median time (interquartile range) to introduce new Assays L&L was 6 mos. (4–12 mos.), to introduce MRD assays was 11 mos. (5–12 mos.), and to validate/go‐live with new cytometers was 8 mos. (4–12 mos.); these times positively correlated with each other. This study confirmed significantly increased workload since the prior ICCS 2013 workload survey with a concurrent decrease in Staff LabOps . Faster introduction of new assays correlated with other successes, including quicker validation of and going live with new cytometers. Among all laboratories, those that performed myeloid MRD assays versus those that did not were also found to be faster to introduce new assays. The need for sufficient staffing has been emphasized because laboratories with both higher annual volumes of myeloma MRD assays and higher ratios of Assays Total per Staff LabOps were slower to introduce new assays. “Lack of staff and/or time dedicated or protected for assay development” and, more generally, “staff number” were the most commonly identified major barriers for new assay development, with the former specifically linked to slower introduction of new assays among all laboratories.

Abstract Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155 884 cells per case, on average, (87% cellularity reduction), whereas preserving small MRD populations (median, 100% retention for MRD of &lt;1%) and allowing for true percentage MRD estimates using a correction factor. A deep neural network cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score of 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve [AUC] of 0.98) or B-lymphoid cells (AUC of 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (standard deviation of ±0.57); with 100% positive agreement with conventional analysis (for MRD of ≥0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance and has the potential of facilitating B-ALL MRD analysis by more clinical laboratories.

Background: Flow cytometric detection of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL) is complex, time-consuming, and subject to inter-operator variability. Deep neural networks (DNNs) offer potential for standardization and efficiency improvement, but require rigorous validation and monitoring for safe clinical implementation. Methods: We evaluated a DNN-assisted human-in-the-loop approach for CLL MRD detection. Initial validation included method comparison against manual analysis (n = 240), precision studies, and analytical sensitivity verification. Post-implementation monitoring comprised four components: daily electronic quality control, input data drift detection, error analysis, and attribute acceptance sampling. Laboratory efficiency was assessed through a timing study of 161 cases analyzed by five technologists. Results: Method comparison demonstrated 97.5% concordance with manual analysis for qualitative classification (sensitivity 100%, specificity 95%) and excellent correlation for quantitative assessment (r = 0.99, Deming slope = 0.99). Precision studies confirmed high repeatability and within-laboratory precision across multiple operators. Analytical sensitivity was verified at 0.002% MRD. Post-implementation monitoring identified 2.97% of cases (26/874) with input data drift, primarily high-burden CLL and non-CLL neoplasms. Error analysis showed the DNN alone achieved 97% sensitivity compared to human-in-the-loop-reviewed results, with 13 missed cases (1.5%) showing atypical immunophenotypes. Attribute acceptance sampling confirmed 98.8% of reported negative cases were true negatives. The DNN-assisted workflow reduced average analysis time by 60.3% compared to manual analysis (4.2 ± 2.3 vs. 10.5 ± 5.8 min). Conclusions: The implementation of a DNN-assisted approach for CLL MRD detection in a clinical laboratory provides diagnostic performance equivalent to expert manual analysis while substantially reducing analysis time. Comprehensive performance monitoring ensures ongoing safety and effectiveness in routine clinical practice. This approach provides a model for responsible AI integration in clinical laboratories, balancing automation benefits with expert oversight.