Cytometry Part B: Clinical Cytometry最新文献

Characterization of the tumor immune microenvironment (TIME) of pituitary neuroendocrine tumors (PitNETs) is crucial for understanding the behavior of different types of PitNETs and identification of possible causes of their aggressiveness, rapid growth, and resistance to therapy. High-dimensional flow cytometry (FC) is a promising technology for studying TIME but poses unique technical challenges, especially when applied to solid tissues and PitNETs, in particular. This paper evaluates the potential of FC for analyzing TIME in PitNETs by addressing methodological difficulties across all stages of the workflow and proposing solutions. We developed a protocol for preparing single-cell suspensions from PitNET tissues for FC. This involved optimization of enzymatic digestion and comparison of it with mechanical tissue dissociation assessing cell yield, viability, and target antigen expression. We designed four multicolor FC panels to analyze major lymphocyte and myeloid cell subsets including determination of subpopulations of T, B, NK cells and their activation and cytotoxic potential, neutrophils, monocytes, CD68 + CD64 + CD11b^low macrophages of M2 and M1 subtypes, and two types of myeloid suppressor cells - PMN-MDSC and M-MDSC. Principles of multicolor panel design, spreading error, and importance of voltage balance for proper flow cytometer setting are discussed. The panels were validated and demonstrated the feasibility of their simultaneous use on pituitary tumor surgical tissue for comprehensive TIME characterization. We compared lymphocyte frequencies in blood, PitNETs, and three sequential PitNET eluates to find out the contamination level of PitNET samples with blood leukocytes. To address technical challenges, we propose a strategy of logical data gating that removes spurious signals from aggregates, dead cells, and subcellular debris that can interfere with analysis. Our results indicate that despite all technical difficulties, multiparametric FC can effectively characterize different types of PitNETs. This enhanced understanding of the immune infiltrate provides valuable insights into PitNET biology and advances clinical diagnostics.

Flow cytometry (FC) is an indispensable tool for myeloid neoplasia (MN) diagnosis, and the cell of origin has clinical diagnostic and prognostic significance. However, the complex maturational pathways within the blast compartment complicate the detection of the abnormal population at the minimal/measurable disease (MRD) level using the difference from normal approach. The analysis could be simplified by separating the blast compartment into maturation-defined stages with relatively uniform phenotypes as reference populations. This requires a relatively extensive panel of antibodies to define maturation stages and simultaneously detect the common deviations from the normal pattern. We validated a single tube 28-color clinical assay for MN assessment and acute myeloid leukemia (AML) MRD detection assisted by the precise maturation stage assignment. The new assay uses a previously described 21-antigen backbone, with the additions of CD10, CD36, CD42b, CD45RA, CD90, CD105, and CD371. Validation was performed using 37 normal samples and 151 MN follow-up samples in a split-sample fashion comparing the new assay to the legacy 3-tube, 21-antigen assay. Dilution studies were performed to establish assay sensitivity. A new analysis framework relying on comparison to well-defined maturational stages was established for MRD analysis. The assays showed 100% qualitative concordance with excellent quantitative concordance. Dilution studies yielded a limit of detection of 0.01%. The addition of new antibodies allowed for consistent comparisons of candidate abnormal populations to well-defined normal maturation stages through traditional FC plots and Uniform Matrix Approximation and Projection assessments. This new single-tube, 28-color clinical assay allows for efficient MN assessment in clinical settings. It reliably detects MRD levels of abnormal myeloid cells because the expanded panel allows for precise comparison to defined lineage commitment maturational stages. Lastly, it provides high information density while reducing equipment use, reagent use, and technical labor.

Acute myeloid leukemia (AML) comprises 32% of adult leukemia cases, with a 5-year survival rate of only 20–30%. Here, the immunophenotypic landscape of this heterogeneous malignancy is explored in a single-center cohort using a novel quantitative computational pipeline. For 122 patients who underwent induction treatment with intensive chemotherapy, leukemic cells were identified at diagnosis, computationally preprocessed, and quantitatively subtyped. Computational analysis provided a broad characterization of inter- and intra-patient heterogeneity, which would have been harder to achieve with manual bivariate gating. Statistical testing discovered associations between CD34, CD117, and HLA-DR expression patterns and genetic abnormalities. We found the presence of CD34⁺ cell populations at diagnosis to be associated with a shorter time to relapse. Moreover, CD34⁻ CD117⁺ cell populations were associated with a longer time to AML-related mortality. Machine learning (ML) models were developed to predict 2-year survival, European LeukemiaNet (ELN) risk category, and inv(16) or NPM1^mut, based on computationally quantified leukemic cell populations and limited clinical data, both readily available at diagnosis. We used explainable artificial intelligence (AI) to identify the key clinical characteristics and leukemic cell populations important for our ML models when making these predictions. Our findings highlight the importance of developing objective computational pipelines integrating immunophenotypic and genetic information in the risk stratification of AML.

Anticipating the evolution of septic patients with community-acquired pneumonia (CAP) is challenging for front-line physicians in the Emergency Department (ED). Prognosis depends mainly on early identification, antibiotics, organ support, but also immune status. The objective of this proof-of-concept study was to perform a cluster analysis to investigate whether specific phenotypes, including cellular immunology parameters, are associated with the prognosis in patients with CAP presenting to the ED. We performed an exploratory study in the ED of Limoges university hospital (France) on patients with a confirmed CAP. Deterioration was defined by a composite criterion monitored during 7 days following admission: (i) acute respiratory failure with a high flow oxygen requirement, (ii) subsequent ICU admission, (iii) shock, (iv) worsening of organ dysfunction, and (v) in-hospital mortality. Multicolor Flow Cytometry (MFC) was performed within 12 h after ED admission. Monocyte HLA-DR (mHLA-DR) panels consisting of 11 colors for neutrophils and eight colors for lymphocytes were utilized. Phenotypes were defined using non-supervised hierarchical clustering, including 65 clinical, biological, and immunological variables. During 5 months, 63 patients were prospectively studied (age = 66 ± 19 years; 38 men [60%]; SOFA score = 2.6 ± 1.5; Sepsis = 71%; in-hospital mortality = 5%) of whom 11 patients (17%) were assigned to the deterioration group. Upon admission, we observed no differences in any markers or in the demographic or clinical characteristics of the patients. In contrast, by performing hierarchical clustering, we identified three groups: Cluster #1 corresponded to a population with a low deterioration (5%) compared with Clusters #2 (23%) and #3 (31%). Markers from the myeloid lineage, including mHLA-DR, immature neutrophils, and CD64+ neutrophils, were among the parameters discriminating for cluster construction. Cluster #3 displayed the most severe profile, characterized by elevated markers such as CRP, PCT, and immature granulocytes, along with reduced mHLA-DR levels. A clustering strategy, based on myeloid markers obtained through flow cytometry, provided prognostic insights by identifying three phenotypes with distinct outcomes, while none of the individual markers studied (n = 65, both clinical and biological) showed similar prognostic value. A panel of myeloid markers, alongside clinical management, could optimize patient triage and resource allocation upon ED admission.

The relationship between apoptosis inhibition and cell proliferation was studied during the maturation stages of erythropoiesis, granulopoiesis, and monopoiesis in patients with myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). The anti-apoptotic and proliferative cell fractions were determined in bone marrow aspirates derived from 25 MDS patients and 25 AML patients and compared to 50 nonmalignant cases, using antibodies to Bcl-2 and Ki-67, respectively. These were applied along with ten-color flow cytometry of maturation markers for the three hematopoietic cell lineages. Next, the Bcl-2:Ki-67 ratio was determined as the ratio between the Bcl-2+ and Ki-67+ cell fractions of the specific hematopoietic cell lineages, both in total and during the different stages of maturation. Bone marrow samples from MDS and AML patients showed a significant increase in the anti-apoptotic cell fraction and a reduced proliferative cell compartment compared to non-malignant cases. Overall, the anti-apoptotic cell fraction was particularly increased in the more mature stages, while the proliferative cell fractions were decreased more frequently in the immature stages. These changes varied among different hematopoietic cell lineages. The erythropoietic maturation process showed the most significant differences in both Ki-67+ and Bcl-2+ cell fractions when comparing MDS and AML to non-malignant cases. This difference was restricted to that of the Bcl-2+ cell fractions in the granulopoiesis and that of the Ki-67+ cell fraction of the monopoiesis. All three hematopoietic cell lineages encompass a small fraction of cells (up to 10%) that concurrently exhibit anti-apoptotic and proliferative marker expression. Although MDS and AML patients displayed considerable variability in their anti-apoptotic and proliferation index, the Bcl-2:Ki-67 ratio resulted in a clear separation between the malignant and non-malignant cases. Incorporating this combination of the Bcl-2 and Ki-67 markers into the study of MDS and AML and in future diagnostic workups may provide important insights into the biological behavior of these blood-borne neoplasms and facilitate personalized therapy decisions for patients.

Machine-learning (ML) models in flow cytometry have the potential to reduce error rates, increase reproducibility, and boost the efficiency of clinical labs. While numerous ML models for flow cytometry data have been proposed, few studies have described the clinical deployment of such models. Realizing the potential gains of ML models in clinical labs requires not only an accurate model but also infrastructure for automated inference, error detection, analytics and monitoring, and structured data extraction. Here, we describe an ML model for the detection of Acute Myeloid Leukemia (AML), along with the infrastructure supporting clinical implementation. Our infrastructure leverages the resilience and scalability of the cloud for model inference, a Kubernetes-based workflow system that provides model reproducibility and resource management, and a system for extracting structured diagnoses from full-text reports. We also describe our model monitoring and visualization platform, an essential element for ensuring continued model accuracy. Finally, we present a post-deployment analysis of impacts on turn-around time and compare production accuracy to the original validation statistics.

T lymphoblastic leukemia/lymphoma (T-ALL) is a malignancy composed of proliferating T lymphoblasts. T lymphoblasts can be identified by flow cytometric analysis through the detection of aberrant antigen expression and/or immaturity marker expression. An ideal marker would be expressed brightly on T lymphoblasts but absent in mature T lymphocytes. One such marker is protein tyrosine kinase-7 (PTK7). However, PTK7 has not been widely adopted in clinical flow cytometry labs or incorporated into any T-ALL flow cytometry best practice recommendations. To this end, we demonstrate the utility of PTK7 in flow cytometry panels for T-ALL diagnosis, minimal/measurable residual disease (MRD) detection, and relapse. We retrospectively evaluated flow cytometry data on 175 patients. PTK7 was classified as positive, showing a near two-fold difference in brightness versus background mature T cells, in 87.76% of T-ALL cases at initial diagnosis, 75% of T-ALL at MRD, and 100% of T-ALL at relapse. PTK7 expression remained intact in cases of CD34 and/or TdT negative T-ALL (p = 0.992) and while expression was dimmer at MRD (72% decrease, p = 0.0313), PTK7 remained intact at relapse (33% increase, p = 0.8125). PTK7 should be included in flow cytometry panels when evaluating for T-ALL, both at initial diagnosis, relapse, and for the presence of MRD.

Implementing a new laboratory information system (LIS) presents an opportunity to improve operational efficiency and streamline reporting by refining workflows by utilizing LIS functionality. Flow cytometry laboratories face unique challenges because the specimen and test results may be categorized under clinical pathology (CP), anatomic pathology (AP), or both. We describe the design and implementation of reporting flow cytometry results within the Epic Beaker CP module, its interface with the Epic Beaker AP module, and integrated reporting for AP/CP cases at an academic institution. This manuscript emphasizes the challenges and steps needed to integrate anatomic and clinical pathology workflows by leveraging LIS functionality to implement electronic and predominantly paperless workflows within a flow cytometry laboratory.