Olteanu, Horatiu

Background/Objectives: Acute myeloid leukemia (AML) is a biologically heterogeneous malignancy with a variable prognosis. Despite many patients achieving complete remission, relapse remains common, underscoring the need for effective prognostic markers. Measurable residual disease (MRD) has emerged as a critical prognostic indicator, associated with higher relapse risk and shorter survival. This study reports on our initial experience of MRD detection by flow cytometry in 385 bone marrow samples from 126 AML patients. Methods: The flow cytometry MRD assay, validated according to stringent consensus recommendations, consists of a 3-tube, 10-color panel incorporating a broad spectrum of lineage differentiation markers. Analytical specificity, sensitivity, precision, and reproducibility were evaluated, demonstrating the assay’s robustness. Results: The results reveal distinct immunophenotypic aberrancies in all AML cases, with consistent identification of aberrant immunophenotypes in follow-up specimens. AML MRD was detected in 32 out of 126 patients (25%) and in 77 out of 385 analyses (20%), with a median aberrant blast percentage of 1.87% (range, 0.01–12). A change in immunophenotype was documented in 21% of the MRD-positive cases. MRD positivity detected in the first sample studied was associated with reduced overall survival (HR: 5.153; p < 0.0001). Conclusions: Our findings support the integration of flow cytometric MRD analysis into routine clinical practice to enhance risk stratification and treatment planning for AML patients, as currently recommended by professional guidelines.

Introduction: Myeloma response to treatment can be biochemical, pathological, or radiological, with pathological assessments being invasive. Next-generation blood flow (PBF) for assessment of circulating tumor plasma cells (CTCs) has shown promising results as a prognostic in Multiple Myeloma (MM) patients. CTC detection via PBF has a potential to serve as non-invasive method for assessing pathological burden of disease and response to treatment. Comparing CTC responses to the International Myeloma Working Group (IMWG) criteria will further help to understand PBF's utility in predicting response to therapy. Patients and Methods: The study conducted is a retrospective observational study. We included patients with MM treated at Mayo Clinic from 01/01/2011 to 12/31/2019 with CTC records available at the time of best response (BR) to frontline therapy. The CTC values were expressed as the number of plasma cell events per 150,000 Peripheral Blood Mononuclear Cells (CD38, CD138, CD19, CD45, Kappa, Lambda). The CTC levels were compared across all response(IMWG Criteria) groups at BR, and at baseline. The CTC response was expressed as % CTC decrease at BR from baseline. Time-to-event analyses utilized the Kaplan Meier method and Cox-proportional hazards models, with comparisons made using the log-rank test. Results: We identified 621 patients with CTC data at the time of BR to frontline therapy. Of these, 148 (24%) had baseline CTC data from diagnosis. The median age at diagnosis was 62.3 (27.2-83.7). The median follow-up was 66.15 months; the distribution of patients by treatment class is as follows- Alkylator: 0.8%, PI(Proteasome Inhibitor)+Cyclophosphamide: 27.4%, IMid: 12.7%, PI+IMid(Immunomodulator): 55.9%, PI+IMid+antiCD38: 3.2%; 378 patients (78.8%) underwent an autologous transplant. BR to initial therapy was sCR/stringent Complete Response (10, 1.6%), CR (230, 36.9%), VGPR/Very Good Partial Response (224, 36.1%), PR (123, 19.8%), MR/Minimal Response (12, 1.9%), SD/Stable Disease (14, 2.3%), PD/Progressive Disease (8, 1.3%). In the study, 64 patients (10.31%) had detectable CTCs at their BR. The distribution of CTCs across the different response categories was as follows: none (0%) in sCR, 3 patients (1.3%) in CR, 18 patients (8%) in VGPR, 24 patients (19.5%) in PR, 8 patients (66.7%) in MR, 6 patients (42.9%) in SD, and 5 patients (62.5%) in PD. The median CTC counts and their interquartile ranges for these categories showed significant variability: CR had a median of 46 (IQR: 28.5, 923), VGPR had 39.5 (IQR: 15.5, 111.25), PR had 47 (IQR: 22.75, 82.5), MR had 55 (IQR: 28.5, 85), SD had 96.5 (IQR: 22.25, 218.75), and PD had 317 (IQR: 57, 415). This Analysis of Variance (ANOVA) for response-wise distribution of CTC was &lt;0.01. Response was dichotomized as PR and above for sCR, CR, VGPR, PR and less than PR for MR, SD and PD. Median Overall Survival(OS) for PR and above was 127.8 months (vs less than PR 56.7 months, p=0.01). Median OS for CTC+ at BR was 56.7 months (vs 134.8 for CTC negative, p=0.01). On multivariate analysis, presence of CTC at best response (Hazards Ratio = 2.9, n=1.1-2.9), RISS staging at diagnosis (Hazards Ratio = 2.35, n=1.5-3.05), less than PR (Hazards Ratio =1.8, 1.1-2.9) were independent predictors of inferior OS. In patients with data for CTC available at both baseline and at BR to frontline therapy (n=148), 110 had CTC detectable at diagnosis. Among patients in various response categories, a complete disappearance of CTCs was observed in sCR (n=3, 75.0%), CR (n=36, 69.2%), VGPR (n=38, 64.4%), and PR (n=14, 60.9%). CTC responses greater than 90% were noted in VGPR (n=3, 5.1%), PR (n=2, 8.7%), and SD (n=2, 40.0%). No CTC response was reported in sCR (1, 25.0%), CR (16, 30.8%), VGPR (2, 3.4% and 13, 22.0%), PR (3, 13.0% and both 2, 8.7%), SD (1, 20.0% and both 2, 40.0%), MR (1, 50.0%), and PD (1, 33.3% and 1, 50.0%, respectively). The median OS for those with a CTC response was 112.4 months vs 28.2 months for no response. (p=0.01). Conclusion: The presence of CTC at best response is an independent prognostic marker for inferior OS. Depth of response to frontline therapy correspond to a high rate of clearance of CTCs. Presence of CTC at best response provides independent prognostic value beyond the depth of response. A complete clearance of CTC at best response is associated with improved OS. CTC levels have not been corrected for WBC counts and may not be applicable in all settings.

Background: Flow cytometry (FC) is widely utilized for the identification of minimal residual disease (MRD) in B acute lymphoblastic leukemia (BALL), 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 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 BALL-MRD on a single-tube 10-color panel (CD10, CD19, CD20, CD22, CD24, CD34, CD38, CD45, CD58, CD66c). Raw FC files corresponding to peripheral blood or bone marrow specimens from 171 BALL MRD positive cases (MRD &lt; 5%, median = 0.11%, 107 patients), and 89 MRD-negative cases (63 patients) were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline on a cloud environment (Google Vertex AI). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), dimensionality reduction (UMAP), cluster-based anomaly detection compared to negative controls (24 bone marrow aspirates and 7 peripheral bloods), and cluster-informed downsampling with preservation of low-event subsets. 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 3.5, Beckman Coulter), and %MRD estimates compared to reported results based on expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells per case to be manually analyzed from 1.2 million to 155,884 cells on average (87% cellularity reduction); resulting in a smaller FC data file (from 64.5 MB to 13.7 MB; 79% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 100% retention for both MRD&lt;0.01% and MRD 0.01-1%). 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. Identification of MRD was aided by an AI-generated “aberrancy scale” parameter that discriminated BALL (median: 991) from both benign mononuclear cells (median: 486, p &lt; 0.0001) and background B-lymphoid cells (median: 379, p &lt; 0.0001), with a performance (AUC: 0.98 and 0.94 for mononuclear and B-lymphoid cells, respectively) superior to CD20 (0.54 and 0.69), CD38 (0.60 and 0.81), CD45 (0.75 and 0.74), CD58 (0.52 and 0.89), CD66c (0.56 and 0.72) and all other antigens studied. The use of AI-enhanced files reduced manual analysis time from 14.16 minutes (SD = 3.7) to 1.05 minutes (SD = 0.6) per case, on average (p &lt; 0.0001) (93% reduction). BALL MRD was detected in all positive cases above clinically relevant threshold (≥0.01%, as recommended by NCCN guidelines), and in 28 of 32 (88%) cases below this threshold, with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 1.18, intercept = 0.04%, R squared = 0.92, P &lt; 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw BALL-MRD FC data into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of BALL-MRD in clinical diagnostics, reducing number of cells analyzed by 87% and manual analysis time by 93%, without impacting test performance. Moreover, CCADDAS can be utilized for any labroatory-developed BALL-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 BALL-MRD FC analysis by more clinical laboratories.

Introduction: The current definition of plasma cell leukemia (PCL) is based on peripheral blood smear (PBS) demonstrating ≥5% plasma cells. However, PCL cases are enriched in translocation t(11;14), which often has a lympho-plasmacytoid appearance. Detection and quantification of circulating tumor plasma cells (CTC) through peripheral blood flow cytometry (PBF) has emerged as a promising approach for assessing disease burden and prognosis in multiple myeloma (MM) patients. While PBF is more sensitive, the potential threshold values on PBF that correspond with the PBS detection of circulating plasma cells (PBS-PC) remain undetermined. It is important to understand this comparison better to categorize PCL based on PBF. Patients and Methods: This is a retrospective cross-sectional diagnostic accuracy study. We reviewed results of patients diagnosed with plasma cell dyscrasia at the Mayo Clinic from January 2011 to December 2020. The presence of circulating tumor plasma cells (CTC) in encounters was evaluated using peripheral blood flow cytometry (PBF) and peripheral blood smear (PBS) for plasma cells (PBS-PC). PBS-PC was measured as a percentage of plasma cells (PC) in a 100-cell manual differential smear count, and PBF was expressed as the number of plasma cell events per 150,000 peripheral blood mononuclear cells (PBMCs) (Ficoll-isolated peripheral blood mononuclear cells using antibodies to CD38, CD138, CD19, CD45, kappa, lambda, sensitivity (Sn)=6.7x10-5). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of PBS with respect to PBF were calculated. For threshold analysis, four groups were defined: A = absent PBS-PC, C = ≥1% and &lt;5%, D = ≥5%. Sensitivity and specificity of detecting PBS-PC (y-axis) for groups A vs. B, B vs. C, and C vs. D were plotted against CTC levels on PBF (x-axis) to determine the best cut-off. Area Under the Curve (AUC) was calculated to determine accuracy. Agreement analysis was done for internal validation and reliability assessment. Results: 6066 test records of CTC were reviewed. Circulating tumor plasma cells (CTC) by peripheral blood flow cytometry (PBF) were reported in 1610 (26.5%) instances, and peripheral blood smear for plasma cells (PBS-PC) in 226 (3.7%) tests. Peripheral blood smear (PBS) demonstrated a sensitivity of 9.3%, specificity of 99.5%, positive predictive value (PPV) of 86.7%, and negative predictive value (NPV) of 75.3% when compared to peripheral blood flow cytometry (PBF). The correlation between PBS-PC% and CTC was weakly positive (Spearman's rho=0.27, p&lt;0.01). The median (Q1, Q3) of circulating tumor plasma cells (CTC) for group A (n=5840) was 0 (0, 0), group B (n=82) was 780 (97.75, 4012.75), group C (n=67) was 1801 (61.50, 8723.50), and group D (n=77) was 20518 (2296, 41349) (p-value &lt;0.001). On post-hoc analysis, all pairs had significant differences in CTCs. To distinguish between A and B, a threshold of 32.5 for CTC demonstrated a sensitivity of 81.8% (AUC=87.8%), and to distinguish B and C, 1125 demonstrated a sensitivity of 59.4% (AUC=63.2%). To distinguish between C and D, 5875 for CTC demonstrated a sensitivity of 68.8% (AUC=74.6%). Agreement Analysis: The Bland-Altman plot demonstrates a slight positive bias between the Clonal Event % and %PBS-PC values (mean of difference = 0.28). The variability of differences increases with higher mean values, indicating a potential proportional bias. All predicted thresholds lie within the limits of agreement (LoA) (upper: 6.13, lower: -5.58) and are therefore reliable. The precision range (upper: 0.35, lower: 0.2) is narrower for the predicted thresholds. The methods should not be compared beyond a circulating tumor plasma cell (CTC) count of 9,195. Conclusion: Detection of circulating tumor plasma cells (CTC) by peripheral blood smear (PBS) is very specific but not sensitive and can be improved by peripheral blood flow cytometry (PBF) detection of CTC. Important clinical cutoffs by morphology (presence and ≥5%) have their counterparts by PBF (32 and 5875 out of 150,000 peripheral blood mononuclear cells (PBMC), respectively). The predicted thresholds are reliable. The study is retrospective and may not be generalizable across all demographics and disease severity. Circulating tumor plasma cell (CTC) values aren't absolute counts and may not be generalizable for low white blood cell (WBC) counts.

Introduction: VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) is an adult-onset autoinflammatory disorder defined by somatic UBA1 mutations in hematopoietic cells. VEXAS is associated with a high frequency of clonal hematopoiesis (CH- 60%); with the clonal landscape being dominated by DNMT3A mutations. In our prior study (Gutierrez-Rodrigues Blood 2023), we showed that DNMT3A mutations in VEXAS associated with increased mortality, but without a higher risk of MDS/AML. As DNMT3A is a critical epigenetic regulator, we conducted this study to define the epigenetic deregulation and impact on mortality of DNMT3Amt (mutant) CH in VEXAS. Methods: After IRB approval, 88 VEXAS patients from the Mayo Clinic and the NIH were retrospectively assessed, including 81 screened for CH by error-corrected DNA sequencing (ECS). DNA methylation changes were identified using the Illumina Infinium MethylationEPIC BeadChip on peripheral blood samples (PB). Cellular indexing of transcriptomes and epitopes (CITE)-seq was performed using the 10x Chromium 3' GEX platform; 16,600 pooled cells were subjected to UMAP dimensionality reduction, followed by Azimuth based cell identification. Genotyping of targeted loci with chromatin accessibility (GoTChA) was conducted on two PB DNMT3Amt samples from CCUS individuals (Izzo et al Nature 2024). Results: Patients' median age was 70 years (range: 56 - 89); 95% were white. Twelve out of 81 patients screened by ECS were found with a DNMT3Amt (15%), with a median variant allele fraction (VAF) of 42% (range: 6-46%). In this cohort, while the presence of any CH mutation did not impact survival, DNMT3Amt CH had an age-independent negative impact on mortality (5.5 years vs 8.7 years, p =0.005), without an increase in risk for MDS/AML. Changes in DNA methylation were profiled in 19 and 9 VEXAS patients without CH (Median Age: 68) or with DNMT3Amt CH (Median Age: 74), respectively. VEXAS samples with DNMT3Amt CH had &gt;4,000 differentially methylated CpGs with 95% of the CpGs being hypomethylated compared to those without CH. Using the Roadmap Epigenomics project, we found that this hypomethylation was enriched at enhancer (Enh) sites and actively transcribed states (TxWk). Pathway analysis revealed that the hypomethylated sites were in or near genes involved in the development and differentiation of leukocytes, with IL-13 and IL-15 being identified as upstream regulators of the methylation changes. Bulk RNA-seq performed on PB of 2 and 3 VEXAS patients with and without DNMT3Amt, respectively, identified &gt;1,500 differentially expressed genes in DNMT3Amt samples, 78% of these being overexpressed. Pathway analysis was enriched in gene pathways involved in the development of leukocytes, with IL-13 and IL-15 once again being identified as upstream regulators. Integration of methylation and transcriptomic data identified upregulation of several genes within the IL-13 and IL-15 pathways, with these regions correlating with a decrease in methylation. To assess if the epigenetic dysregulation is linked to DNMT3A mutations, we performed GoTChA-seq on two DNMT3Amt clonal cytopenia samples (CCUS). The comparison of the number of cutsites in DNMT3A wild type vs mt cells from the same individual demonstrated increased open chromatin within the IL-13 and IL-15 pathways in mt cells, both globally and in a genomic locus specific manner. CITE-seq performed on 3 VEXAS samples (2 DNMT3Amt CH vs 1 no CH) further showed that expression of genes involved in IL-13 and IL-15 pathway is significantly increased in CD4+ and CD8+ T cells from DNMT3Amt samples, suggesting a paracrine regulatory effect of T lymphocytes (mostly UBA1 and DNMT3A wild-type) in VEXAS. Pathway analysis of DEGs in these cells also revealed an enrichment of genes involved in inflammation and lymphocyte proliferation. Conclusions: Our study validates the age-independent, negative prognostic impact of DNMT3Amt CH on survival in VEXAS syndrome. DNMT3Amt mediates epigenetic and transcriptional priming associated with upregulation of IL-13/15 pathway in CD4/CD8 T cells, which are mostly DNMT3A and UBA1 wild-type, suggesting that the contribution of T lymphocytes to inflammation may be secondary to CH in the myeloid compartment.

Background: A deeper understanding of the cytogenetic features has enabled robust risk stratification for patients with newly diagnosed multiple myeloma (NDMM). However, heterogeneity persists in the currently available prognostic tools and there is further room for personalization. We assess the utility of PC-PRO, a test to measure the proportion of monotypic plasma cells in the synthetic phase (S-phase) of cell cycle, as a prognostic marker for NDMM. Methods: Patients with active MM diagnosed between January 2013 and January 2023 with a flow cytometry-based S-phase assessment at diagnosis were included. Flow cytometric immunophenotyping was performed using the following antibodies: CD19, CD38, CD45, CD138, cytoplasmic kappa and lambda immunoglobulin, and DAPI (4',6-diamidino-2-phenylindole). The percentage of plasma cells in S-phase was determined by measuring the proportion of cells with DNA content of the monotypic plasma cells between the G0/G1 and G2/M peaks. Plasma cell clonality was detected through demonstrating CD38 and CD138 positivity along with immunoglobulin light chain restriction, abnormality of CD19/ CD45 expression and/or ploidy difference by DAPI staining. High-risk cytogenetic abnormalities (HRCA) by FISH were defined as follows: presence of deletion 17p (del17p), t(4;14), t(14;16), t(14;20), 1q gain/amplification, 1p deletion (when available). Additionally, the second iteration of the revised ISS (R2-ISS), incorporating both laboratory and cytogenetic features as well an adaptation of the recently proposed International Myeloma Working Group (IMWG) Classification for high-risk disease, was utilized for risk stratification. All time-to-event analyses were performed using the Kaplan Meier method and compared using the log-rank test. Univariable and multivariable Cox proportional hazards analyses were performed to assess the independent prognostic impact of S-phase. Results: A total of 823 patients were included in the study. The median follow-up was 6 years [95% Confidence interval (CI): 5.5-6.3 years] and estimated median overall survival (OS) was 8.3 years. The median S-phase value for the cohort was 0.8 % (interquartile range: 0.5-1.4%), with 135 (16%) patients harboring an S-phase of ≥2% at diagnosis. Applying the R2-ISS prognostic model to our cohort, 11% (79/706) patients were stratified to the R2-ISS high-risk cohort; 93 patients (out of 788, 12%) had 2 or more HRCA at diagnosis. Patients with a S-phase ≥2% at diagnosis had a median OS of 3.9 years (95% CI: 2.9-5.7) vs. 9.2 years [95% CI: 7.9-not reached (NR)] for patients with a S-phase &lt;2% [Hazard ratio (HR): 2.2 (95% CI: 1.7-2.8), p&lt;0.0001]. The median OS was inferior for cohorts with an increasing S-phase value: S-phase of 0.1-1%: HR: 1 (standard), S-phase 1.1-3%: HR 1.7 (95% CI: 1.3-2.1), S-phase &gt;3%: HR 3.0 (95% CI: 2.2-4.1)]. On a multivariable analysis including S-phase ≥2%, age at diagnosis of MM (&gt;65 years) and R2-ISS high-risk status, S-phase ≥2% was an independent predictor of inferior OS [hazard ratio (HR) 1.9 (95% CI: 1.4-2.6), p&lt;0.0001]. Age &gt;65 years [HR 1.9 (95% CI: 1.5-2.5), p&lt;0.0001] and R2-ISS high-risk status [HR 1.8 (1.3-2.6), p=0.001] were also independent predictors of OS. Within the sub cohort of patients with 2 or more of the listed high-risk cytogenetic features (n=93, 12%), a S-phase ≥2% (n=24) was associated with a worse median OS of 1.6 years (95% CI: 0.7-NR) vs. 6.2 years (95 % CI:3.3-NR) for S-phase &lt;2% (p=0.002). Conclusion: Flow cytometry-based determination of the proportion of monotypic plasma cells in S-phase is an easy-to-study and powerful prognostic tool for NDMM. A S-phase of ≥2% at diagnosis is a predictor of OS independent of age and R2-ISS high-risk stage. An S-phase of ≥2% was able to identify a distinct group with markedly inferior outcomes even within the sub cohort of patients with 2 or more high-risk cytogenetic features.

Background: Flow cytometry (FC) is widely utilized for the identification of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL), 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 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 CLL MRD on a single-tube 10-color panel (CD5, CD19, CD20, CD22, CD38, CD43, CD45, CD200, kappa and lambda). Raw FC files corresponding to peripheral blood or bone marrow specimens from 166 CLL MRD positive cases (MRD &lt; 5%, median = 0.17%, 122 patients), and 61 MRD-negative cases (45 patients) were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline on a cloud environment (Google Vertex AI). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), dimensionality reduction (UMAP), cluster-based anomaly detection compared to negative controls (15 bone marrow aspirates and 14 peripheral bloods), and cluster-informed downsampling with preservation of low-event subsets. 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 3.5, Beckman Coulter), and %MRD estimates compared to reported results based on expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells per case to be manually analyzed from 1.1 million to 165,010 cells on average (85% cellularity reduction); resulting in a smaller FC data file (from 62.1 MB to 15.2 MB; 75% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 100% retention for both MRD&lt;0.01% and MRD 0.01-1%). 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. Rapid identification of suspected MRD was aided by an AI-generated “aberrancy scale” parameter that discriminated CLL MRD (mean ± 2SD: 969 ± 144) from background B-lymphoid cells (mean ± 2SD: 395 ± 401) (p&lt;0.0001) (AUC = 0.98), with a performance superior to CD5 (AUC = 0.93), CD20 (AUC = 0.83), CD43 (AUC = 0.8) and CD200 (AUC = 0.7). Immunoglobulin light chain-positive CLL MRD was visually identified on a pre-calculated UMAP plot as a single-colored cluster, after dual coloring for kappa and lambda. The use of AI-enhanced files reduced manual analysis time from 11.6 minutes (SD = 4.5) to 0.8 minutes (SD = 0.4) per case, on average (p &lt; 0.01) (93% reduction). CLL MRD was detected in all positive cases above clinically relevant threshold (≥0.01%, as recommended by NCCN guidelines), and in 17 of 21 (81%) cases below this threshold, with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 1.02, intercept = 0.001%, R squared = 0.98, P &lt; 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw CLL-MRD FC data into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of CLL-MRD in clinical diagnostics, reducing number of cells analyzed by 85% and manual analysis time by 93%, without impacting test performance. Moreover, CCADDAS can be utilized for any laboratory-developed CLL-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 CLL MRD FC analysis by more clinical laboratories.

Abstract Objectives To define the prevalence, distribution and characteristics of patients with VEXAS (vacuoles, E1-enzyme, X-linked, autoinflammation, somatic) syndrome who have confirmed vasculitis. Methods Patients with VEXAS syndrome, verified by positive UBA1 mutation, were included. Chart review was performed to identify patient characteristics and outcomes. Vasculitis diagnosis was based on either histopathology showing vascular inflammation or non-invasive angiography findings. Summary statistics were calculated. Results Eighty-nine patients met inclusion criteria. All were male with a median age of onset of 66.9 years (interquartile range 60.1, 72.7). Median (interquartile range) follow-up was 3.8 (2.2–5.5) years, during which 21 patients (23.6%) had evidence of vasculitis. Vasculitis subtypes included small vessel vasculitis (19.1%), cutaneous medium vessel vasculitis (2.2%) and large vessel vasculitis (2.2%). No patient had more than one vessel size involved. Histopathology in small vessel vasculitis patients was consistent with cutaneous leukocytoclastic vasculitis in the majority, though one patient had leukocytoclastic peritubular capillaritis on renal biopsy. Cranial symptoms (headache, vision changes or jaw pain) were noted in 18.0%. Two additional patients not experiencing cranial symptoms exhibited large vessel involvement with confirmed carotid thickening on non-invasive angiography; one of these had a positive temporal artery biopsy. Conclusion VEXAS syndrome manifests as a variable vessel vasculitis in a quarter of patients, with cutaneous small and medium vessel involvement being particularly common. Some patients may have positive ANCA serologies or even renal vasculitis leading to misdiagnosis. Cranial symptoms are common and may mimic GCA, though documented large vessel inflammation is rare.