Cytometry Part B: Clinical Cytometry最新文献

Detection of measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) is an important prognostic marker. The most common CLL MRD method in current use is multiparameter flow cytometry, but availability is limited by the need for expert manual analysis. Automated analysis has the potential to expand access to CLL MRD testing. We evaluated the performance of an artificial intelligence (AI)-assisted multiparameter flow cytometry (MFC) workflow for CLL MRD. We randomly selected 113 CLL MRD FCS files and divided them into training and validation sets. The training set (n = 41) was gated by expert manual analysis and used to train the AI model. We then compared the validation set (n = 72) MRD results obtained by the AI-assisted analysis versus those by expert manual analysis using the Pearson correlation coefficient and Bland–Altman plot method. In the validation set, the AI-assisted analysis correctly categorized cases as MRD-negative versus MRD-positive in 96% of cases. When comparing the AI-assisted analysis versus the expert manual analysis, the Pearson r was 0.8650, mean bias was 0.2237 log₁₀ units, and the 95% limit of agreement (LOA) was ±1.0282 log₁₀ units. The AI-assisted analysis performed sub-optimally in atypical immunophenotype CLL and in cases lacking residual normal B cells. When excluding these outlier cases, the mean bias improved to 0.0680 log₁₀ units and the 95% LOA to ±0.2926 log₁₀ units. An automated AI-assisted workflow allows for the quantification of MRD in CLL with typical immunophenotype. Further work is required to improve performance in atypical immunophenotype CLL.

Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. Additionally, cycIHC benefits from the speed and simplicity of brightfield microscopy, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the log odds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact—or do not interact—at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT is a fully open source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

Minimal/measurable residual disease (MRD) is the most important independent prognostic factor for patients with B-lymphoblastic leukemia (B-LL). MRD post therapy has been incorporated into risk stratification and clinical management, resulting in substantially improved outcomes in pediatric and adult patients. Currently, MRD in B-ALL is most commonly assessed by multiparametric flow cytometry and molecular (polymerase chain reaction or high-throughput sequencing based) methods. The detection of MRD by flow cytometry in B-ALL often begins with B cell antigen-based gating strategies. Over the past several years, targeted immunotherapy directed against B cell markers has been introduced in patients with relapsed or refractory B-ALL and has demonstrated encouraging results. However, targeted therapies have significant impact on the immunophenotype of leukemic blasts, in particular, downregulation or loss of targeted antigens on blasts and normal B cell precursors, posing challenges for MRD detection using standard gating strategies. Novel flow cytometric approaches, using alternative strategies for population identification, sometimes including alternative gating reagents, have been developed and implemented to monitor MRD in the setting of post targeted therapy.

Multiparameter flow cytometry (MFC) is one of the essential ancillary methods in bone marrow (BM) investigation of patients with cytopenia and suspected myelodysplastic syndrome (MDS). MFC can also be applied in the follow-up of MDS patients undergoing treatment. This document summarizes recommendations from the International/European Leukemia Net Working Group for Flow Cytometry in Myelodysplastic Syndromes (ELN iMDS Flow) on the analytical issues in MFC for the diagnostic work-up of MDS. Recommendations for the analysis of several BM cell subsets such as myeloid precursors, maturing granulocytic and monocytic components and erythropoiesis are given. A core set of 17 markers identified as independently related to a cytomorphologic diagnosis of myelodysplasia is suggested as mandatory for MFC evaluation of BM in a patient with cytopenia. A myeloid precursor cell (CD34⁺CD19⁻) count >3% should be considered immunophenotypically indicative of myelodysplasia. However, MFC results should always be evaluated as part of an integrated hematopathology work-up. Looking forward, several machine-learning-based analytical tools of interest should be applied in parallel to conventional analytical methods to investigate their usefulness in integrated diagnostics, risk stratification, and potentially even in the evaluation of response to therapy, based on MFC data. In addition, compiling large uniform datasets is desirable, as most of the machine-learning-based methods tend to perform better with larger numbers of investigated samples, especially in such a heterogeneous disease as MDS.