Cytometry Part B: Clinical Cytometry最新文献

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.

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is an uncommon mature T-cell neoplasm occurring in patients with textured breast implants, typically after 7–10 years of exposure. Although cytopathologic or histopathologic assessment is considered the gold standard diagnostic method for BIA-ALCL, flow cytometry (FC)-based immunophenotyping is recommended as an adjunct test. However, the diagnostic efficacy of FC is not well reported. We reviewed 290 FC tests from breast implant pericapsular fluid and capsule tissue from 182 patients, including 16 patients with BIA-ALCL over a 6-year period, calculating diagnostic rates and test efficacy. FC showed an overall sensitivity of 75.9%, specificity of 100%, and negative and positive predictive values of 95.4% and 100%, respectively. Blinded expert review of false-negative cases identified diagnostic pitfalls, improving sensitivity to 96.6%. Fluid samples had better rates of adequate samples for FC testing compared with tissue samples. Paired with FC testing of operating room (OR)-acquired fluid samples, capsulectomy FC specimens added no diagnostic value in patients with concurrent fluid samples; no cases had positive capsule FC with negative fluid FC. Fluid samples are adequate for FC testing more often than tissue. Capsule tissue FC specimens do not improve FC efficacy when paired with OR-acquired fluid FC samples and are often inadequate samples. FC is 100% specific for BIA-ALCL and can serve as a confirmatory test but should not be the sole diagnostic method. Awareness of sample-specific diagnostic pitfalls greatly improves the sensitivity of BIA-ALCL testing by FC.

Immunophenotyping by flow cytometry is an integral part of the diagnosis and classification of leukemias/lymphomas. The expression of ROR1 associated with chronic B lymphocytic leukemia (CLL) is well described in the literature, both in its diagnosis and in the follow-up of minimal residual disease (MRD) research, however, there are few studies regarding the expression pattern of ROR1 in other subtypes of mature B lymphoid neoplasms. With the aim of evaluating the expression of ROR1 and associating it with the expression of other important markers for the differentiation of mature B lymphoid neoplasms (MBLN), 767 samples of cases that entered our laboratory for immunophenotyping with clinical suspicion of MBLN were studied. ROR1 expression is predominant in CD5+/CD10− neoplasms. Overall, positive ROR1 expression was observed in 461 (60.1%) cases. The CD5+/CD10− group had a significantly higher proportion of ROR1 positive samples (89.9%) and more brightly expressed ROR1 than the other groups. Our results highlight the importance of evaluating ROR1 expression in the diagnosis of MBLN to contribute to the differential diagnosis, and possibly therapy of mainly CLL, and indicate that this marker could be considered as a useful addition to immunophenotypic panels, particularly for more challenging cases.

CD19 is frequently targeted for immunotherapy in B cell malignancies, which may result in loss of CD19 expression in leukemic cells as an escape mechanism. Stage 0 hematogones (Hgs) are normal CD19-negative very early B cell precursors that can be potentially mistaken for CD19 negative residual leukemic cells by flow cytometry (FCM) in B cell acute lymphoblastic leukemia (BCP-ALL) cases treated with anti CD19 therapy. Our main objective was to characterize and study the incidence of stage 0 hematogones in follow-up bone marrow samples of pediatric BCP-ALL cases. We analyzed the flow cytometry standard files of 61 pediatric BCP-ALL cases treated with conventional chemotherapy and targeted anti-CD19 therapy, for identifying the residual disease and normal B cell precursors including stage 0 Hgs. A non-CD19 alternate gating strategy was used to isolate the B cells for detecting the residual disease and stage 0 Hgs. The stage 0 Hgs were seen in 95% of marrow samples containing CD19+ Hgs. When compared with controls and posttransplant marrow samples, the fraction of stage 0 Hgs was higher in patients receiving anti CD19 therapy (p = 0.0048), but it was not significant when compared with patients receiving chemotherapy (p = 0.1788). Isolated stage 0 Hgs are found in samples treated with anti-CD19 therapy simulating CD19 negative residual illness. Our findings aid in understanding the stage 0 Hgs and its association with CD19+ Hgs in anti CD19 therapy and conventional chemotherapy. This is crucial as it can be potentially mistaken for residual disease in patients treated with anti CD19 therapy.

Since response to antigen-based immunotherapy relies upon the level of tumor antigen expression we developed an antigen quantification assay using ABC values. Antigen quantification as a clinical assay requires methods for quality control and for interlaboratory and inter-cytometer platform standardization. A single lot of Cytotrol™ Lyophilized Control Cells (Beckman Coulter) used for all studies. The variability in antigen quantification across 4 different instrument platforms in 2 separate laboratories was evaluated. The effect of the antibody clone utilized, importance of custom 1:1 molar ratio (fluorophore to protein, F/P) verses off-the-shelf antibodies, and QuantiBrite PE calibration verses linearity calibration combined with a single point scale transformation with CD4 as reference were determined. Use of single lot control cells allowed validation of reproducibility between flow cytometer platforms and laboratories and allowed assessment of different antibody lots, cocktail preparation, and different antibody clones. Off the shelf antibody preparations provide reproducible estimates of antigen density, however custom 1:1 unimolar antibody preparations should be utilized for definitive measurement of antigen expression.Geometric Mean fluorescent Intensity (GeoMFI) was not comparable across instruments and inter-laboratory. The use of CD4 as the reference marker can minimize variability in ABC values. Comparable antigen quantification is vital in managing patients receiving antigen-based immunotherapy. If this assay is to be utilized in a clinical setting, quality control methods have to be instituted to assure reproducibility and allow validation across laboratories. We have demonstrated that use of a lyophilized cell control is highly valuable in achieveing these goals.