Characterization of myeloproliferative neoplasms based on genetics only and prognostication of transformation to blast phase.

Myeloproliferative neoplasms (MPN) are a heterogeneous group of clonal disorders characterized by aberrant hematopoietic proliferation and an intrinsic risk of progression to blast phase. The WHO classification 2022 identifies chronic myeloid leukemia and the BCR::ABL1 negative MPNs polycythemia vera, primary myelofibrosis and essential thrombocythemia as individual entities. However, overlaps, borderline findings or transitions between MPN subtypes occur and incomplete clinical data often complicates diagnosis. By conducting a thorough genetic analysis, we've developed a model that relies on 12 genetic markers to accurately stratify MPN patients. The model can be simplified into a decision tree for routine use. Comparing samples at chronic and blast phase revealed, that one third of patients lost their MPN driver-gene mutation, while mutations in splicing and chromatin modifying genes were stable, indicating a shared founder clone of chronic and blast phase with different driver mutations and therefore different progressing capacities. This was further supported by gain of typical de novo AML gene mutations, accompanied by gain of complex karyotypes and RAS pathway gene mutations. Our data suggest to perform a broader genetic screening at diagnosis and also at clinical progression, as driver mutations may change and the MPN-driver mutations present at diagnosis may disappear.

Flow cytometry assay modifications: Recommendations for method validation based on CLSI H62 guidelines.

The Clinical and Laboratory Standards Institute (CLSI) H62-Validation of Assays Performed by Flow Cytometry guideline, released in 2021, provides recommendations for platform workflow and quality system essentials, instrument setup and standardization, assay development and optimization and fit-for-purpose analytical method validation. In addition, CLSI H62 includes some recommendations for the validation strategies after a validated flow cytometric method has been modified. This manuscript builds on those recommendations and discusses the impact of different types of assay modifications on assay performance. Recommendations regarding which validation parameters to evaluate depending on the type of modification are provided. The impact of assay modification on the assay's intended use is discussed. When recommending minor deviations from the CLSI H62 process for a laboratory-initiated assay revision (e.g., specimen numbers for sensitivity, specificity, or precision studies), a rationale based on expert opinion is provided with the understanding that not every laboratory, assay type, and circumstance can be comprehensively addressed in this paper. These recommendations are meant as a practical recommendation and are not intended to be restrictive, prescriptive, or understood as necessarily sufficient to meet every specific requirement from regulatory bodies (e.g., FDA or New York State Department of Health).

Implementation of flow cytometry testing on rare matrix samples: Special considerations and best practices when the sample is unique or difficult to obtain.

The publication of Clinical and Laboratory Standards Institute's guideline H62 has provided the flow cytometry community with much-needed guidance on development and validation of flow cytometric assays (CLSI, 2021). It has also paved the way for additional exploration of certain topics requiring additional guidance. Flow cytometric analysis of rare matrices, or unique and/or less frequently encountered specimen types, is one such topic and is the focus of this manuscript. This document is the result of a collaboration subject matter experts from a diverse range of backgrounds and seeks to provide best practice consensus guidance regarding these types of specimens. Herein, we define rare matrix samples in the setting of flow cytometric analysis, address validation implications and challenges with these samples, and describe important considerations of using these samples in both clinical and research settings.

Clinical and Genomic-Based Decision Support System to Define the Optimal Timing of Allogeneic Hematopoietic Stem-Cell Transplantation in Patients With Myelodysplastic Syndromes.

PURPOSE: Allogeneic hematopoietic stem-cell transplantation (HSCT) is the only potentially curative treatment for patients with myelodysplastic syndromes (MDS). Several issues must be considered when evaluating the benefits and risks of HSCT for patients with MDS, with the timing of transplantation being a crucial question. Here, we aimed to develop and validate a decision support system to define the optimal timing of HSCT for patients with MDS on the basis of clinical and genomic information as provided by the Molecular International Prognostic Scoring System (IPSS-M). PATIENTS AND METHODS: We studied a retrospective population of 7,118 patients, stratified into training and validation cohorts. A decision strategy was built to estimate the average survival over an 8-year time horizon (restricted mean survival time [RMST]) for each combination of clinical and genomic covariates and to determine the optimal transplantation policy by comparing different strategies. RESULTS: Under an IPSS-M based policy, patients with either low and moderate-low risk benefited from a delayed transplantation policy, whereas in those belonging to moderately high-, high- and very high-risk categories, immediate transplantation was associated with a prolonged life expectancy (RMST). Modeling decision analysis on IPSS-M versus conventional Revised IPSS (IPSS-R) changed the transplantation policy in a significant proportion of patients (15% of patient candidate to be immediately transplanted under an IPSS-R-based policy would benefit from a delayed strategy by IPSS-M, whereas 19% of candidates to delayed transplantation by IPSS-R would benefit from immediate HSCT by IPSS-M), resulting in a significant gain-in-life expectancy under an IPSS-M-based policy (P = .001). CONCLUSION: These results provide evidence for the clinical relevance of including genomic features into the transplantation decision making process, allowing personalizing the hazards and effectiveness of HSCT in patients with MDS.

Immune-monitoring of myelodysplastic neoplasms: Recommendations from the i4MDS consortium.

Advancements in comprehending myelodysplastic neoplasms (MDS) have unfolded significantly in recent years, elucidating a myriad of cellular and molecular underpinnings integral to disease progression. While molecular inclusions into prognostic models have substantively advanced risk stratification, recent revelations have emphasized the pivotal role of immune dysregulation within the bone marrow milieu during MDS evolution. Nonetheless, immunotherapy for MDS has not experienced breakthroughs seen in other malignancies, partly attributable to the absence of an immune classification that could stratify patients toward optimally targeted immunotherapeutic approaches. A pivotal obstacle to establishing "immune classes" among MDS patients is the absence of validated accepted immune panels suitable for routine application in clinical laboratories. In response, we formed International Integrative Innovative Immunology for MDS (i4MDS), a consortium of multidisciplinary experts, and created the following recommendations for standardized methodologies to monitor immune responses in MDS. A central goal of i4MDS is the development of an immune score that could be incorporated into current clinical risk stratification models. This position paper first consolidates current knowledge on MDS immunology. Subsequently, in collaboration with clinical and laboratory specialists, we introduce flow cytometry panels and cytokine assays, meticulously devised for clinical laboratories, aiming to monitor the immune status of MDS patients, evaluating both immune fitness and identifying potential immune "risk factors." By amalgamating this immunological characterization data and molecular data, we aim to enhance patient stratification, identify predictive markers for treatment responsiveness, and accelerate the development of systems immunology tools and innovative immunotherapies.

TRBC1 in flow cytometry: Assay development, validation, and reporting considerations.

The assessment of T-cell clonality by flow cytometry has long been suboptimal, relying on aberrant marker expression and/or intensity. The introduction of TRBC1 shows much promise for improving the diagnosis of T-cell neoplasms in the clinical flow laboratory. Most laboratories considering this marker already have existing panels designed for T-cell workups and will be determining how best to incorporate TRBC1. We present this comprehensive summary of TRBC1 and supplemental case examples to familiarize the flow cytometry community with its potential for routine application, provide examples of how to incorporate it into T-cell panels, and signal caution in interpreting the results in certain diagnostic scenarios where appropriate.

Technical, gating and interpretation recommendations for the partitioning of circulating monocyte subsets assessed by flow cytometry.

The monocyte subset partitioning by flow cytometry, known as "monocyte assay," is now integrated into the new classifications as a supporting criterion for CMML diagnosis, if a relative accumulation of classical monocytes above 94% of total circulating monocytes is observed. Here we provide clinical flow cytometry laboratories with technical support adapted for the most commonly used cytometers. Step-by-step explanations of the gating strategy developed on whole peripheral blood are presented while underlining the most common difficulties. In a second part, interpretation recommendations of circulating monocyte partitioning from the dedicated French working group "CytHem-LMMC" are shared as well as the main pitfalls, including false positive and false negative cases. The particular flow-defined inflammatory profile is described and the usefulness of the nonclassical monocyte specific marker, namely slan, highlighted. Examples of reporting to the physician with frequent situations encountered when using the monocyte assay are also presented.

Translating the regulatory landscape of medical devices to create fit-for-purpose artificial intelligence (AI) cytometry solutions.

The implementation of medical software and artificial intelligence (AI) algorithms into routine clinical cytometry diagnostic practice requires a thorough understanding of regulatory requirements and challenges throughout the cytometry software product lifecycle. To provide cytometry software developers, computational scientists, researchers, industry professionals, and diagnostic physicians/pathologists with an introduction to European Union (EU) and United States (US) regulatory frameworks. Informed by community feedback and needs assessment established during two international cytometry workshops, this article provides an overview of regulatory landscapes as they pertain to the application of AI, AI-enabled medical devices, and Software as a Medical Device in diagnostic flow cytometry. Evolving regulatory frameworks are discussed, and specific examples regarding cytometry instruments, analysis software and clinical flow cytometry in-vitro diagnostic assays are provided. An important consideration for cytometry software development is the modular approach. As such, modules can be segregated and treated as independent components based on the medical purpose and risk and become subjected to a range of context-dependent compliance and regulatory requirements throughout their life cycle. Knowledge of regulatory and compliance requirements enhances the communication and collaboration between developers, researchers, end-users and regulators. This connection is essential to translate scientific innovation into diagnostic practice and to continue to shape the development and revision of new policies, standards, and approaches.

Recommendations for using artificial intelligence in clinical flow cytometry.

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI-based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.

Comparing malignant monocytosis across the updated WHO and ICC classifications of 2022.

ABSTRACT: The World Health Organization (WHO) classification of hematolymphoid tumors and the International Consensus Classification (ICC) of 2022 introduced major changes to the definition of chronic myelomonocytic leukemia (CMML). To assess its qualitative and quantitative implications for patient care, we started with 3311 established CMML cases (according to WHO 2017 criteria) and included 2130 oligomonocytosis cases fulfilling the new CMML diagnostic criteria. Applying both 2022 classification systems, 356 and 241 of oligomonocytosis cases were newly classified as myelodysplastic (MD)-CMML (WHO and ICC 2022, respectively), most of which were diagnosed as myelodysplastic syndrome (MDS) according to the WHO 2017 classification. Importantly, 1.5 times more oligomonocytosis cases were classified as CMML according to WHO 2022 than based on ICC, because of different diagnostic criteria. Genetic analyses of the newly classified CMML cases showed a distinct mutational profile with strong enrichment of MDS-typical alterations, resulting in a transcriptional subgroup separated from established MD and myeloproliferative CMML. Despite a different cytogenetic, molecular, immunophenotypic, and transcriptional landscape, no differences in overall survival were found between newly classified and established MD-CMML cases. To the best of our knowledge, this study represents the most comprehensive analysis of routine CMML cases to date, both in terms of clinical characterization and transcriptomic analysis, placing newly classified CMML cases on a disease continuum between MDS and previously established CMML.

The clinical and genomic landscape of patients with DDX41 variants identified during diagnostic sequencing.

Deleterious germ line variants in DDX41 are a common cause of genetic predisposition to hematologic malignancies, particularly myelodysplastic neoplasms (MDS) and acute myeloid leukemia (AML). Targeted next-generation sequencing was performed in a large cohort of sequentially recruited patients with myeloid malignancy, covering DDX41 as well as 30 other genes frequently mutated in myeloid malignancy. Whole genome transcriptome sequencing data was analyzed on a separate cohort of patients with a range of hematologic malignancies to investigate the spectrum of cancer predisposition. Altogether, 5737 patients with myeloid malignancies were studied, with 152 different DDX41 variants detected. Multiple novel variants were detected, including synonymous variants affecting splicing as demonstrated by RNA-sequencing. The presence of a somatic DDX41 variant was highly associated with DDX41 germ line variants in patients with MDS and AML, and we developed a statistical approach to incorporate the co-occurrence of a somatic DDX41 variant into germ line variant classification at a very strong level (as per the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines). Using this approach, the MDS cohort contained 108 of 2865 (3.8%) patients with germ line likely pathogenic/pathogenic (LP/P) variants, and the AML cohort 106 of 2157 (4.9%). DDX41 LP/P variants were markedly enriched in patients with AML and MDS compared with those in patients with myeloproliferative neoplasms, B-cell neoplasm, and T- or B-cell acute lymphoblastic leukemia. In summary, we have developed a framework to enhance DDX41 variant curation as well as highlighted the importance of assessment of all types of genomic variants (including synonymous and multiexon deletions) to fully detect the landscape of possible clinically relevant DDX41 variants.

Interplay of TP53 allelic state, blast count, and complex karyotype on survival of patients with AML and MDS.

Several clinical and genetic factors impact overall survival (OS) in myelodysplastic neoplasms (MDS) and acute myeloid leukemia (AML), including complex karyotype (CK), TP53 allelic state, and blast count. We analyzed the interplay of these factors by performing Cox regression analysis and by determining the frequency of TP53 single-hit (sh) and double-hit (dh) events and OS in MDS (n = 747) with <5% blasts, with ≥5% but <10% blasts, and ≥10% but <20% blasts and AML (n = 772). MDS with <5% blasts showed the best outcome, followed by with ≥5% but <10% blasts, and ≥10% but <20% blasts, and AML (median OS: 75, 54, 27, and 18 months, respectively). The same hierarchy was observed when each subgroup was divided into TP53sh, TP53dh, and without TP53 alterations (alt), revealing a dismal outcome of TP53dh in all subgroups (17, 10, 8, and 1 month[s], respectively). MDS with <5% blasts differed from the other subgroups by showing predominantly TP53sh (76% of TP53alt cases), and by an independent adverse impact of CK on OS (hazard ratio, 5.2; P < .001). The remaining subgroups displayed many similarities, with TP53dh found at high frequencies (67%, 91%, and 71%, respectively) and only TP53alt but not CK independently influencing OS, and TP53dh showing the strongest influence. When the total cohort was split based on TP53 state, only the blast count and not CK had an independent adverse impact on OS in all subgroups. Thus, TP53dh is the strongest prognostic factor, further supporting its integration into risk stratification guidelines and classification as a separate entity. However, the blast count also influences OS independent of TP53 state, whereas CK plays a minor prognostic role.

Surface-Initiated Polymerizations Mediated by Novel Germanium-Based Photoinitiators.

Since surface-initiated photopolymerization techniques have gained increasing interest within the last decades, the coupling of photoinitiators to surfaces and particles has become an important research topic in material and surface sciences. In terms of surface modification and functionalization, covalently coupled photoinitiators and subsequent photopolymerizations are employed to provide a huge variety of surface properties, such as wettability, stimulus responsive features, antifouling behavior, protein binding, friction control, drug delivery, and many more. For this purpose, numerous type I and type II photoinitiators or other photosensitive moieties have been attached to different substrates so far. In our studies, a convenient and straightforward synthetic protocol to prepare a novel germanium-based photoinitiator (bromo-tris(2,4,6-trimethylbenzoyl)germane) in good yields was developed. The immobilization of this photoinitiator at the surface of silicon wafers and quartz plates was evidenced by X-ray photoelectron spectroscopy (XPS). Employing visible-light-triggered surface-initiated polymerization of different functional monomers, including acrylamide, perfluorodecyl acrylate, and fluorescein-o-acrylate, surfaces with various features such as hydrophilic/hydrophobic and fluorescent properties were prepared. This was also achieved in a spatially resolved manner. The polymer layers were characterized by contact angle measurements, UV-vis/fluorescence spectroscopy, spectroscopic ellipsometry, and XPS. The thicknesses of the surface grafted polymer layers ranged between 10 and 126 nm.

Accelerometry-Based Classification of Circulatory States During Out-of-Hospital Cardiac Arrest.

OBJECTIVE: Exploit accelerometry data for an automatic, reliable, and prompt detection of spontaneous circulation during cardiac arrest, as this is both vital for patient survival and practically challenging. METHODS: We developed a machine learning algorithm to automatically predict the circulatory state during cardiopulmonary resuscitation from 4-second-long snippets of accelerometry and electrocardiogram (ECG) data from pauses of chest compressions of real-world defibrillator records. The algorithm was trained based on 422 cases from the German Resuscitation Registry, for which ground truth labels were created by a manual annotation of physicians. It uses a kernelized Support Vector Machine classifier based on 49 features, which partially reflect the correlation between accelerometry and electrocardiogram data. RESULTS: Evaluating 50 different test-training data splits, the proposed algorithm exhibits a balanced accuracy of 81.2%, a sensitivity of 80.6%, and a specificity of 81.8%, whereas using only ECG leads to a balanced accuracy of 76.5%, a sensitivity of 80.2%, and a specificity of 72.8%. CONCLUSION: The first method employing accelerometry for pulse/no-pulse decision yields a significant increase in performance compared to single ECG-signal usage. SIGNIFICANCE: This shows that accelerometry provides relevant information for pulse/no-pulse decisions. In application, such an algorithm may be used to simplify retrospective annotation for quality management and, moreover, to support clinicians to assess circulatory state during cardiac arrest treatment.