Synthetic abnormal mast cell particles successfully mimic neoplastic mast cells by flow cytometry.

Clinical flow cytometry laboratories require quality control materials for assay development, validation, and performance monitoring, including new reagent lot qualification. However, finding suitable controls for populations with uncommonly expressed antigens or for rare populations, such as mast cells, can be difficult. To that end, we evaluated synthetic abnormal mast cell particles (SAMCP), developed together with, and manufactured by, Slingshot Biosciences. The SAMCP's were designed to phenotypically mimic abnormal neoplastic mast cells: they were customized to have the same light scatter and autofluorescence properties of mast cells, along with surface antigen levels of CD45, CD33, CD117, CD2, CD25, and CD30 consistent with that seen in mast cell disease. We evaluated several performance characteristics of these particles using ARUP's high sensitivity clinical mast cell assay, including limit of detection, off-target activity and FMO controls, precision, scatter properties of the particles utilizing several different cytometer platforms, and particle antigen stability. The phenotype of the SAMCP mimicked abnormal mast cells, and they could be distinguished from normal native mast cells. FMO controls demonstrated specificity of each of the markers, and no off-target binding was detected. The limit of detection of the particles spiked into normal bone marrow was found to be ≤0.003% in a limiting dilution assay. The mast cell particles were found to perform similarly on Becton Dickinson Lyric, Cytek Aurora, and Beckman Coulter Navios and CytoFLEX platforms. Within run and between run precision were less than 10% CV. SAMCP were stable up to 13 days with minimal loss of antigen fluorescence intensity. The SAMCP's were able to successfully mimic neoplastic mast cells based on the results of our high sensitivity mast cell flow cytometry panel. These synthetic cell particles represent an exciting and innovative technology, which can fulfill vital needs in clinical flow cytometry such as serving as standardized control materials for assay development and performance monitoring.

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.

Recommendations for using artificial intelligence in clinical flow cytometry.

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.

Systematic single amino acid affinity tuning of CD229 CAR T cells retains efficacy against multiple myeloma and eliminates on-target off-tumor toxicity.

T cells expressing chimeric antigen receptors (CARs) have shown remarkable therapeutic activity against different types of cancer. However, the wider use of CAR T cells has been hindered by the potential for life-threatening toxicities due to on-target off-tumor killing of cells expressing low amounts of the target antigen. CD229, a signaling lymphocyte-activation molecule (SLAM) family member, has previously been identified as a target for CAR T cell-mediated treatment of multiple myeloma (MM) due to its high expression on the surfaces of MM cells. CD229 CAR T cells have shown effective clearance of MM cells in vitro and in vivo. However, healthy lymphocytes also express CD229, albeit at lower amounts than MM cells, causing their unintended targeting by CD229 CAR T cells. To increase the selectivity of CD229 CAR T cells for MM cells, we used a single amino acid substitution approach of the CAR binding domain to reduce CAR affinity. To identify CARs with increased selectivity, we screened variant binding domains using solid-phase binding assays and biolayer interferometry and determined the cytotoxic activity of variant CAR T cells against MM cells and healthy lymphocytes. We identified a CD229 CAR binding domain with micromolar affinity that, when combined with overexpression of c-Jun, confers antitumor activity comparable to parental CD229 CAR T cells but lacks the parental cells' cytotoxic activity toward healthy lymphocytes in vitro and in vivo. The results represent a promising strategy to improve the efficacy and safety of CAR T cell therapy that requires clinical validation.

BCR-ABL1 (p210) Transcript Kinetics.

CONTEXT.­: Delta checks are a powerful technique for monitoring clinical assays in many disciplines but have not been routinely used in molecular testing. OBJECTIVE.­: To determine if the biologically determined kinetics of BCR-ABL1's rise and fall could allow the development of a delta check in BCR-ABL1 testing. DESIGN.­: Nine years of BCR-ABL1 p210 results were evaluated, and patients with 3 or more results were selected for inclusion. The kinetics of these percentages of international standard values were plotted against time along with the median and the 90th and 95th percentile lines. A Monte Carlo simulation of a batch mix-up was performed for 6 months of data to determine the efficacy of the proposed cutoff. RESULTS.­: The median kinetics showed a 1-log drop of the percentage of international standard in 90 days, with less than 5% of cases showing faster than a 2-log drop in 90 days, and less than 2.5% showing a faster than 3-log drop in 90 days (extrapolated to 1 log in 30 days). The Monte Carlo simulation of a batch mix-up showed that an average batch mix-up of 23 samples could routinely be flagged by this cutoff, albeit with wide variance. CONCLUSIONS.­: These results suggest that using a drop in the percentage of international standard of greater than 1 log in 30 days can be a useful trigger in implementing a delta-check system for this molecular test.

Flow cytometric myeloma measurable residual disease testing in the era of targeted therapies.

Therapies in myeloma are rapidly advancing with a host of new targeted therapies coming to market. While these drugs offer significant survival benefits and better side-effect profiles compared with conventional chemotherapeutics, they raise significant difficulties in monitoring post-therapy disease status by flow cytometry due to assay interference and/or selection of phenotypically different sub-clones. The principal culprit, anti-CD38 monoclonal antibodies, limits the ability to detect plasma cells based on classical CD38/CD45 gating. Other markers, such as CD138, are known to be suboptimal by flow cytometry. Various techniques have been proposed to overcome this problem. The most promising of these techniques has been the marker VS38c, a monoclonal antibody targeting an endoplasmic reticulum protein which has shown high sensitivity for plasma cells. Alternative techniques for gating plasma cells, while variably effective in the near term are already the subject of several targeted therapies rendering their usefulness limited in the longer term. Likewise, future targets of these therapies may render present aberrancy markers ineffective in MRD testing. These therapies pose challenges that must be overcome with new markers and novel panels in order for flow cytometric MRD testing to remain relevant.

Augmented Human Intelligence and Automated Diagnosis in Flow Cytometry for Hematologic Malignancies.

OBJECTIVES: Clinical flow cytometry is laborious, time-consuming, and expensive given the need for data review by highly trained personnel such as technologists and pathologists as well as the significant number of normal cases. Given these issues, automation in analysis and diagnosis holds the key to major efficiency gains. The objective was to design an automated pipeline for the diagnosis of B-cell malignancies in flow cytometry and evaluate its performance against our standard clinical diagnostic flow cytometry process. METHODS: Using 3,417 cases of peripheral blood data over 6 months from our 10-color B-cell screening tube, we used a newly described method for feature extraction and dimensionality reduction called UMAP on the raw flow cytometry data followed by random forest classification to classify cases without gating on specific population. RESULTS: Our automated classifier was able to achieve greater than 95% accuracy in diagnosing all B-cell malignancies, and even better performance for specific malignancies for which the panel was designed, such as chronic lymphocytic leukemia. By adjusting classifier cutoffs, 100% sensitivity could be achieved with an albeit low 14% specificity. Hypothetically, this would allow 11% of the cases to be autoverified without human intervention. CONCLUSIONS: These results suggest that a clinical implementation of this pipeline can greatly assist in quality control, improve turnaround time, and decrease staff workloads.

Computer-aided detection of rare tumor populations in flow cytometry: an example with classic Hodgkin lymphoma.

OBJECTIVES: Diagnosing classical Hodgkin lymphoma (cHL) by flow cytometry (FC) relies on an observer gating rare populations of Hodgkin/Reed Sternberg (HRS) cells. Here, we apply machine-learning methods to aid in the detection of rare tumor cell populations using data derived from clinical FC analysis of cHL as a model disease. METHODS: FC data from 144 clinical cases using a nine-color FC reagent panel were analyzed using Python 2.7 and the "scikit-learn" module. RESULTS: Seventy-eight 50 × 50 two-dimensional histograms were generated from routine FC data and a reciprocal power function applied to favor rare events. Data were classified by support vector machine (SVM), gradient boosting, and random forest classifiers. All three classifiers showed no statistical difference in performance, with 89%-92% accuracy on cross-validation. Nearly all classifiers misclassified the same set of cases, with more false-positive than false-negative cases. Dimensionality reduction by ensemble methods selected for data points in a CD5+/ CD40+/CD64- region. CONCLUSIONS: All classifiers provide probabilistic confidences for each result, and diagnostic cutoffs can be chosen to minimize false negatives and serve as a screening tool. Computational exclusion of manually gated HRS cells had little impact on the overall performance of selected support vectors in SVM or dimensionality reduction, suggesting that features of the immune response in cHL may dictate the method accuracy. We hypothesize there are distinct inflammatory cells that suggest cHL.

Short-term preoperative diet modification reduces steatosis and blood loss in patients undergoing liver resection.

BACKGROUND: Steatosis and steatohepatitis have been associated with increased morbidity and mortality after liver resection. Our objective was to determine the effect of a preoperative calorie-restricted diet on steatosis and steatohepatitis in patients undergoing liver resection. METHODS: We studied 111 consecutive patients who had major elective hepatic resections. More than 90% of the patients had cancer resections. The mean body mass index was 27.2; 32% had a body mass index ≥30. A week-long calorie- and fat-restricted diet was instituted in the most recent patient group (n = 51). We retrospectively evaluated steatosis and steatohepatitis in the diet and no-diet control groups. Clinical outcomes of the 2 groups, including complications and blood loss, were compared. RESULTS: The preoperative diet patients had less steatosis (15.7% vs 25.5% of hepatocytes containing fat, P = .05) than the nondiet controls. A lower percentage of patients in the diet group had steatohepatitis than in the nondiet group (15% vs 27%, P =.02). Preoperative diet patients had less mean intraoperative blood loss than nondiet patients (600 mL vs 906 mL, P = .002). There was no difference in overall or infectious complications between the groups. CONCLUSION: We have shown for the first time that short-term calorie restriction before liver resection significantly reduces both hepatic steatosis and steatohepatitis. Dietary modification also was associated with decreased intraoperative blood loss. This intervention is easily instituted; therefore, it is clinically feasible.