Cytometry Part B: Clinical Cytometry最新文献

This issue of Cytometry Part B, Clinical Cytometry consists of four original articles and four letters to the Editor, bridged by a discussion forum. Although finding common themes between these works is not necessary, some naturally emerged for me under a simple but helpful way of thinking about clinical flow cytometry that I learned from Professor Alberto Orfao's education sessions. To paraphrase, in clinical flow cytometry, we are doing one of three things at any time: identifying, characterizing (as either normal or abnormal) or enumerating cell populations. Fourth, flow cytometry must be interpreted in a broader clinicopathological context. These principles assist in defining the indication and context of use of an assay, which in turn help determine panel design, and other pre-analytical, analytical and post-analytical components. Lastly, this journal recognizes the value of the single case report. While some journals have abandoned them, if well researched, relevant and succinct, they can serve as a useful educational tool or cautionary tale, illustrate the application, strengths or weakness of a guideline, or document rare, interesting cases and other novel phenomena.

Therefore, rather than in order of appearance, I introduce this issue's contents as follows:

Kumar et al. (2024) provide a nice example of improving the identification of plasma cells for later characterization and enumeration, demonstrating substantial improvement in CD138 expression and, ultimately, plasma cell recovery using a gentler “stain-lyse-no-wash” sample preparation technique compared to their standard “(bulk) lyse-stain-wash” method in 36 paired bone marrow samples, with no adverse effect on the intensity of other antigens in the panel. They changed their practice, using this simpler technique for the surface marker tube on 244 additional samples over 6 years, reserving “lyse-stain-wash” preparation for the analysis of cytoplasmic light chains. Whether this can be applied to myeloma measurable residual disease (MRD) assessment remains to be tested.

The study by Ramalingam et al. (2024) and letter from Placek et al. (2024) reinforce that a masterful appreciation of normal B-cell maturation under various clinical conditions is critical for monitoring residual B-acute lymphoblastic leukemia (B-ALL). Ramalingam et al. provide a concise assessment of the immunophenotype of type 0 hematogones (by CD34, CD10, CD45, CD19, CD20, CD22 and CD24 expression) in 61 pediatric patients under various conditions and time points following CD19-targeting, conventional chemotherapy, and hematopoietic stem cell transplantation. While the existence of CD19-negative B-cell precursors (BCP) has been known for some time (Dworzak et al., 1998; Uckun & Ledbetter, 1988), they have, until recently, remained under recognized within the confines of standard B-ALL MRD panels until Cherian et al. devel

Multiparameter flow cytometry is widely used for acute myeloid leukemia minimal residual disease testing (AML MRD) but is time consuming and demands substantial expertise. Machine learning offers potential advancements in accuracy and efficiency, but has yet to be widely adopted for this application. To explore this, we trained single cell XGBoost classifiers from 98 diagnostic AML cell populations and 30 MRD negative samples. Performance was assessed by cross-validation. Predictions were integrated with UMAP as a heatmap parameter for an augmented/interactive AML MRD analysis framework, which was benchmarked against traditional MRD analysis for 25 test cases. The results showed that XGBoost achieved a median AUC of 0.97, effectively distinguishing diverse AML cell populations from normal cells. When integrated with UMAP, the classifiers highlighted MRD populations against the background of normal events. Our pipeline, MAGIC-DR, incorporated classifier predictions and UMAP into flow cytometry standard (FCS) files. This enabled a human-in-the-loop machine learning guided MRD workflow. Validation against conventional analysis for 25 MRD samples showed 100% concordance in myeloid blast detection, with MAGIC-DR also identifying several immature monocytic populations not readily found by conventional analysis. In conclusion, Integrating a supervised classifier with unsupervised dimension reduction offers a robust method for AML MRD analysis that can be seamlessly integrated into conventional workflows. Our approach can support and augment human analysis by highlighting abnormal populations that can be gated on for quantification and further assessment. This has the potential to speed up MRD analysis, and potentially improve detection sensitivity for certain AML immunophenotypes.

Chimeric antigen receptor (CAR) modified T cell therapies targeting BCMA have displayed impressive activity in the treatment of multiple myeloma. There are currently two FDA licensed products, ciltacabtagene autoleucel and idecabtagene vicleucel, for treating relapsed and refractory disease. Although correlative analyses performed by product manufacturers have been reported in clinical trials, there are limited options for reliable BCMA CAR T detection assays for physicians and researchers looking to explore it as a biomarker for clinical outcome. Given the known association of CAR T cell expansion kinetics with toxicity and response, being able to quantify BCMA CAR T cells routinely and accurately in the blood of patients can serve as a valuable asset. Here, we optimized an accurate and sensitive flow cytometry test using a PE-conjugated soluble BCMA protein, with a lower limit of quantitation of 0.19% of CD3+ T cells, suitable for use as a routine assay for monitoring the frequency of BCMA CAR T cells in the blood of patients receiving either ciltacabtagene autoleucel or idecabtagene vicleucel.

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.

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.

It is a pleasure to usher in the first issue of Cytometry Part B: Clinical Cytometry for the New Year. I would like to take this opportunity to wish the International Society for Clinical Cytometry, the European Society for Clinical Cell Analyses, and Cytometry Part B, continued success in 2024. Also, I would like to thank all the people who make each issue of our journal possible, the submitting authors, the reviewers, the Editorial Board, the Associate Editors, Deputy Editor, Janos Kappelmayer, and our Editor-in-Chief, Fred Preffer. And last, but certainly not least, special thanks to our Managing Editor, Doris Regal who somehow makes it all come together, each and every issue.

In this issue, the importance of multiparametric flow cytometry in clinical diagnosis and drug development is highlighted with many of the papers echoing my passion for standardization, validation, and quality control.

The paper from the laboratories of Wang et al. (2024), “Standardization of Flow Cytometric Detection of Antigen Expression,” is the result of a collaboration between the National Institute of Standards and Technology (NIST) and the National Cancer Institute (NCI) and promises to be one of the most important papers of the year (Tian et al., 2024). This point is highlighted by the Commentary on the paper by Bruce Davis, “Editorial on IVD cellular assay validation” (Davis, 2024). Both documents are ones that everyone conducting cytometry, in any setting, needs to read and re-read. They bring us one step closer to understanding what is required in order to achieve reproducible and quantitative flow cytometry data across platforms and across laboratories.

These manuscripts highlight the increased importance of accurately measuring antigen expression levels when treating patients with novel immunotherapies. Antigen density measurements not only impact patient selection, but are also instrumental in determining treatment efficacy and patient outcomes. The Tian et al. paper ultimately concludes that assay standardization is a critical requirement to enable broad clinical utility and impact of this novel class of therapies. A good part of the paper focuses on the inherent variability and subjectivity in qualitative estimates of antigen density (e.g., dim, moderate, bright) and the resulting need for quantitative measurements of cell surface antigen expression. Common methods for determining antigen density such as geometric mean fluorescence intensity (GeoMFI) and antibodies bound per cell (ABC) appear to be straightforward; however, result comparability across different instrument platforms, reagent lots, operators, and laboratories has not yet been demonstrated. Using a systematic, well-thought-out approach, this team evaluated assay variability of flow cytometric quantitation and then describe procedures and quality control practices whereby highly reproduceable antigen expression measurements ca

The 5-azacytidine (AZA) and decitabine (DEC) are noncytotoxic, differentiation-inducing therapies approved for treatment of myelodysplastic syndrome, acute myeloid leukemias (AML), and under evaluation as maintenance therapy for AML postallogeneic hematopoietic stem cell transplant and to treat hemoglobinapathies. Malignant cell cytoreduction is thought to occur by S-phase specific depletion of the key epigenetic regulator, DNA methyltransferase 1 (DNMT1) that, in the case of cancers, thereby releases terminal-differentiation programs. DNMT1-targeting can also elevate expression of immune function genes (HLA-DR, MICA, MICB) to stimulate graft versus leukemia effects. In vivo, there is a large inter-individual variability in DEC and 5-AZA activity because of pharmacogenetic factors, and an assay to quantify the molecular pharmacodynamic effect of DNMT1-depletion is a logical step toward individualized or personalized therapy. We developed and analytically validated a flow cytometric assay for DNMT1 epitope levels in blood and bone marrow cell subpopulations defined by immunophenotype and cell cycle state. Wild type (WT) and DNMT1 knock out (DKO) HC116 cells were used to select and optimize a highly specific DNMT1 monoclonal antibody. Methodologic validation of the assay consisted of cytometry and matching immunoblots of HC116-WT and -DKO cells and peripheral blood mononuclear cells; flow cytometry of H116-WT treated with DEC, and patient samples before and after treatment with 5-AZA. Analysis of patient samples demonstrated assay reproducibility, variation in patient DNMT1 levels prior to treatment, and DNMT1 depletion posttherapy. A flow-cytometry assay has been developed that in the research setting of clinical trials can inform studies of DEC or 5-AZA treatment to achieve targeted molecular pharmacodynamic effects and better understand treatment-resistance/failure.

As the association of human leukocyte antigen B27 (HLA-B27) with spondylarthropathies is widely known, HLA-B27 antigen expression is frequently identified using flow cytometric or other techniques. Because of the possibility of cross-reaction with off target antigens, such as HLA-B7, each flow cytometric technique applies a "gray zone" reserved for equivocal findings. Our aim was to use machine learning (ML) methods to classify such equivocal data as positive or negative. Equivocal samples (n = 99) were selected from samples submitted to our institution for clinical evaluation by HLA-B27 antigen testing. Samples were analyzed by flow cytometry and polymerase chain reaction. Features of histograms generated by flow cytometry were used to train and validate ML methods for classification as logistic regression (LR), decision tree (DT), random forest (RF) and light gradient boost method (GBM). All evaluated ML algorithms performed well, with high accuracy, sensitivity, specificity, as well as negative and positive predictive values. Although, gradient boost approaches are proposed as high performance methods; nevertheless, their effectiveness may be lower for smaller sample sizes. On our relatively smaller sample set, the random forest algorithm performed best (AUC: 0.92), but there was no statistically significant difference between the ML algorithms used. AUC values for light GBM, DT, and LR were 0.88, 0.89, 0.89, respectively. Implementing these algorithms into the process of HLA-B27 testing can reduce the number of uncertain, false negative or false positive cases, especially in laboratories where no genetic testing is available.