Cytometry Part B: Clinical Cytometry最新文献

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.

Thrombocythemia (ET), polycythemia vera (PV), primary myelofibrosis (PMF), prefibrotic/early (pre-PMF), and overt fibrotic PMF (overt PMF) are classical Philadelphia-Negative (Ph-negative) myeloproliferative neoplasms (MPNs). Differentiating between these types based on morphology and molecular markers is challenging. This study aims to clarify the application of flow cytometry in the diagnosis and differential diagnosis of classical MPNs. This study retrospectively analyzed the immunophenotypes, clinical characteristics, and laboratory findings of 211 Ph-negative MPN patients, including ET, PV, pre-PMF, overt PMF, and 47 controls. Compared to ET and PV, PMF differed in white blood cells, hemoglobin, blast cells in the peripheral blood, abnormal karyotype, and WT1 gene expression. PMF also differed from controls in CD34⁺ cells, granulocyte phenotype, monocyte phenotype, percentage of plasma cells, and dendritic cells. Notably, the PMF group had a significantly lower plasma cell percentage compared with other groups. A lasso and random forest model select five variables (CD34⁺CD19⁺cells and CD34⁺CD38⁻ cells on CD34⁺cells, CD13^dim+CD11b⁻ cells in granulocytes, CD38^str+CD19^+/−plasma, and CD123⁺HLA-DR⁻basophils), which identify PMF with a sensitivity and specificity of 90%. Simultaneously, a classification and regression tree model was constructed using the percentage of CD34⁺CD38⁻ on CD34⁺ cells and platelet counts to distinguish between ET and pre-PMF, with accuracies of 94.3% and 83.9%, respectively. Flow immunophenotyping aids in diagnosing PMF and differentiating between ET and PV. It also helps distinguish pre-PMF from ET and guides treatment decisions.

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.

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.