Uta Oelschlaegel

The authors would like to make the following correction to their published paper

Background: Myelodysplastic neoplasms (MDSs) are clonal hematopoietic stem cell disorders associated with ineffective hematopoiesis, chronic inflammation, and increased cardiovascular morbidity. Although metabolic dysregulation has been implicated in MDS pathogenesis, systemic metabolic alterations remain incompletely characterized. Methods: Plasma samples from treatment-naïve patients with MDS or chronic myelomonocytic leukemia (CMML) and age-matched healthy controls were analyzed using quantitative nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Metabolomic profiles were compared using unsupervised and supervised multivariate analyses, validated in an independent external MDS cohort, and integrated with re-analysis of publicly available RNA-sequencing datasets from purified CD14⁺ CMML monocytes. Results: Patients with MDS and CMML exhibited broad reductions in circulating lipoprotein-associated metabolites, including HDL-, LDL-, IDL-, and apolipoprotein-associated fractions, indicating disturbed systemic lipoprotein homeostasis. Within the discovery cohort, CMML samples showed higher concentrations of the ketone bodies 3-hydroxybutyrate and acetoacetate, as well as succinate. LC-MS analysis demonstrated selective increases in C18:1 acylcarnitine, oleic and isopalmitic acids, whereas free carnitine abundance remained unchanged. Elevated 3-hydroxybutyrate levels were not associated with mutational burden, hematologic parameters, disease risk, or immunophenotypic features. Re-analysis of public CMML monocyte transcriptomes demonstrated increased expression of genes involved in lipid uptake and intracellular lipid trafficking, including FABP5, APOE, LPL, and SLC27A2, without coordinated activation of fatty acid oxidation pathways. External cohort analysis confirmed the overall MDS-associated plasma metabolomic profile. Conclusions: MDSs and CMML are associated with reproducible alterations in systemic lipid metabolism characterized by reduced circulating lipoprotein-associated metabolites, while CMML showed more pronounced ketone body- and acylcarnitine-associated metabolic phenotype accompanied by changes in lipid-handling transcriptional programs. These findings support altered systemic lipid metabolism and carnitine-dependent fatty acid handling as characteristic features of myeloid neoplasms and provide a rationale for future functional studies investigating lipid metabolism in disease pathogenesis.

Measurable residual disease (MRD) monitoring by multiparameter flow cytometry (MFC) is well established in bone marrow (BM) samples for acute myeloid leukemia (AML), but its use in peripheral blood (PB) remains less developed. We adapted a semi‐automated and well validated MFC‐MRD assay, originally developed for BM, to PB aiming to evaluate its applicability and concordance with established methods. To transfer the MFC‐MRD assay from BM to PB, we incorporated population‐specific reference values derived from healthy and chemotherapy‐exposed non‐AML controls. MRD detection was based on a combined aberrant leukemia associated immunophenotype (aLAIP) and different from normal (DfN) approach. We compared MFC‐MRD detection in paired PB and BM samples and evaluated concordance with molecular MRD (Mol‐MRD) diagnostics. Additional analyses included assessment of mast cells as markers for BM hemodilution as well as time requirements for analyses. Reference values for aberrant populations in PB were generally lower than in BM, particularly considering immature markers. At follow‐up, MFC‐MRD positivity was less common in PB than in BM, leading to a concordance rate of 73.7%. Both quantitative and qualitative differences in aberrant antigen expression between PB and BM were observed. Most cases of MRD positivity were characterized by newly emerging aberrant features rather than persistent ones. Concordance between MFC‐MRD from PB and Mol‐MRD from BM was 64.6%, similar to that of MFC‐MRD from BM versus Mol‐MRD from BM (58.5%). Most discordant cases (MFC‐MRD ^pos /Mol‐MRD ^neg ) may reflect clonal hematopoiesis or limited spectrum of molecular panels. Mast cell quantification proved useful in identifying hemodiluted BM samples, which comprised ~15% of cases. Inter‐rater agreement for MRD detection was strong ( Kα > 0.8). The MFC‐MRD gating and evaluation was rapid (~2 min/sample in both PB and BM). The adapted MFC‐MRD assay is feasible and robust in PB, offering a fast and reproducible alternative to BM‐based MRD monitoring. However, the prognostic relevance of the proposed method needs to be validated in a prospective cohort of patients.

Flow cytometry (FCM) is a co‐criterion in myelodysplastic neoplasms (MDS) diagnostics, currently not used for prognostication. This study aimed to develop an FCM‐score predicting overall survival (OS) in MDS to improve early clinical patient prognostication. FCM of bone marrow samples was performed for diagnostic purposes in 509 therapy‐naïve MDS patients and 77 healthy donors. The following methodology was used: (1) uni‐ and multivariate Cox proportional hazards regression and Kaplan–Meier curves for OS to assess FCM‐parameters' prognostic value; (2) receiver operating characteristic (ROC) curves to test the prognostic superiority of FCM‐parameters versus established FCM‐scores and clinical risk‐scores; and (3) development of a FCM‐prognostic score (FCM‐PS) based on six FCM‐parameters with independent prognostic impact. The final FCM‐PS included aberrancies of progenitor cells (increased CD45 mean fluorescence intensity [MFI]‐ratio of lymphocytes and myeloid progenitor cells, decreased % of lymphatic progenitor cells), granulopoiesis (increased CD33 MFI, decreased sideward scatter [SSC]‐ratio of granulopoiesis and lymphocytes), lymphocytes (increased % of B‐lymphocytes), and plasmacytoid dendritic cells (increased %). FCM‐PS outperformed established scores for OS (hazard ratio [HR] 4.08 [95% CI 2.54−6.55] vs. Ogata‐score: 2.44 [1.61−3.70], International Prognostic Scoring System‐Revised [IPSS‐R]: 2.37 [1.61–3.49], International Prognostic Scoring System‐Molecular [IPSS‐M]: 0.816 [0.303–2.196]). Patients in the FCM‐PS low score category showed significantly better OS (P < 0.0001). Further, FCM‐PS allowed discrimination within IPSS‐R area under the curve [AUC]: 0.70 vs. 0.62) and IPSS‐M (AUC: 0.75 vs. 0.48) subgroups. Validation of the prognostic FCM‐PS in an independent patient cohort confirmed good discrimination performance (AUC: 0.70). We introduce a unique, easy‐to‐use prognostic FCM‐PS score (panel: CD45/CD34/CD117/CD33/CD19/CD123/HLA‐DR) for OS in MDS, allowing refined risk stratification for IPSS‐R subgroups.

Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data.

Background: Flow cytometry (FCM) is a co‐criterion in myelodysplastic syndromes (MDS) diagnostics according to the WHO classification. The presented study compared diagnostic power and prognostic impact of different FCM‐based scores.Methods: A total of 807 bone marrow (BM) samples of patients with cytopenia (543 MDS, 153 non‐clonal cytopenias, 111 non‐MDS myeloid malignancies) and 78 healthy controls have been investigated using a standardized 8‐color‐FCM procedure. FCSS, Ogata‐score, iFS, RED‐score, and ELN‐NEC were analyzed for sensitivity and specificity in comparison to standard diagnostic tools. Median follow up for patients was 26 month (range: 0.2–89).Results: The iFS showed the highest accuracy (80%) with the best balance between sensitivity (79%) and specificity (86%). This was also valid in MDS with very low IPSS‐R and even in MDS without ring sideroblasts, with normal blast count and karyotype, where iFS could confirm diagnosis in 62% and 65% of patients. Besides the high diagnostic power, the established iFS category “consistent with MDS” was associated with inferior overall survival (OS) independent from WHO classification (median: 51 month vs. not reached,p < 0.0001). Remarkably, this iFS category redefined a subgroup of patients with worse OS within IPSS‐R low‐risk category (73 month vs. not reached,p = 0.0433). Finally, multivariable analysis showed that iFS added independent prognostic information regarding OS besides IPSS‐R.Conclusions: The iFS separates non‐clonal cytopenias and MDS with the highest accuracy, provided information in addition to standard diagnostic procedures, and refined established prognostic tools for outcome prediction.