Implementation and Validation of an Automated Flow Cytometry Analysis Pipeline for Human Immune Profiling

del Hierro A, H-Vázquez J, Cuesta-Sancho S, Bernardo D. State-of-the-art cytometry in the search of novel biomarkers in digestive cancers

Despite that colorectal and liver cancer are among the most prevalent tumours in the world, the identification of non-invasive biomarkers to aid on their diagnose and subsequent prognosis is a current unmet need that would diminish both their incidence and mortality rates. In this context, conventional flow cytometry has been widely used in the screening of biomarkers with clinical utility in other malignant processes like leukaemia or lymphoma. Therefore, in this review, we will focus on how advanced cytometry panels covering over 40 parameters can be applied on the study of the immune system from patients with colorectal and hepatocellular carcinoma and how that can be used on the search of novel biomarkers to aid or diagnose, prognosis, and even predict clinical response to different treatments. In addition, these multiparametric and unbiased approaches can also provide novel insights into the specific immunopathogenic mechanisms governing these malignant diseases, hence potentially unravelling novel targets to perform immunotherapy or identify novel mechanisms, rendering the development of novel treatments. As a consequence, computational cytometry approaches are an emerging methodology for the early detection and predicting therapies for gastrointestinal cancers.

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

BACKGROUND

Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases.

METHODS

Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer.

RESULTS

Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83-0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89-0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71-0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state.

CONCLUSION

CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018.

Implementing flowDensity for Automated Analysis of Bone Marrow Lymphocyte Population

Introduction

Manual gating of flow cytometry (FCM) data for marrow cell analysis is a standard approach in current practice, although it is time- and labor-consuming. Recent advances in cytometry technology have led to significant efforts in developing partially or fully automated analysis methods. Although multiple supervised and unsupervised FCM data analysis algorithms have been developed, they have not been widely adopted by the clinical and research laboratories. In this study, we evaluated flowDensity, an open source freely available algorithm, as an automated analysis tool for classification of lymphocyte subsets in the bone marrow biopsy specimens.

Materials and Methods

FlowDensity-based gating was applied to 102 normal bone marrow samples and compared with the manual analysis. Independent expression of each cell marker was assessed for comprehensive expression analysis and visualization.

Results

Our findings showed a correlation between the manual and flowDensity-based gating in the lymphocyte subsets. However, flowDensity-based gating in the populations with a small number of cells in each cluster showed a low degree of correlation. Comprehensive expression analysis successfully identified and visualized the lymphocyte subsets.

Discussion

Our study found that although flowDensity might be a promising method for FCM data analysis, more optimization is required before implementing this algorithm into day-to-day workflow.

Peripheral immunophenotyping of AITD subjects reveals alterations in immune cells in pediatric vs adult-onset AITD

Autoimmune thyroid disease (AITD) is caused by aberrant activation of the immune system allowing autoreactive B and T cells to target the thyroid gland leading to disease. Although AITD is more frequently diagnosed in adults, children are also affected but rarely studied. Here, we performed phenotypic and functional characterization of peripheral blood immune cells from pediatric and adult-onset AITD patients and age-matched controls using mass cytometry. Major findings indicate that unlike adult-onset AITD patients, pediatric AITD patients exhibit a decrease in anergic B cells (B_ND) and DN2 B cells and an increase in immature B cells compared to age-matched controls. These results indicate alterations in peripheral blood immune cells seen in pediatric-onset AITD could lead to rapid progression of disease. Hence, this study demonstrates diversity of AITD by showing differences in immune cell phenotypes and function based on age of onset, and may inform future therapies.

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Penetrance of high-risk HLA-DR3 haplotype is higher in pediatric AITD patients

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Pediatric AITD patients display altered frequency of autoreactive B cell subsets

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Immune cell subset frequency and function is similar in adult AITD and controls

An Extensive Quality Control and Quality Assurance (QC/QA) Program Significantly Improves Inter-Laboratory Concordance Rates of Flow-Cytometric Minimal Residual Disease Assessment in Acute Lymphoblastic Leukemia: An I-BFM-FLOW-Network Report

Simple Summary

Standardization of flow-cytometric assessment of minimal residual disease in acute lymphoid leukemia (ALL) is necessary to allow concordant multicentric application of the methodology. This is a prerequisite for internationally collaborative trials, such as the AIEOP-BFM-ALL and the ALL IC-BFM trial. We developed and applied a comprehensive training and quality control program involving a large number of international laboratories within the I-BFM consortium to complement standardization of the methodology with an educational component as well as with persistent quality control measures to allow large ALL treatment trials which use multi-laboratory FCM-MRD assessments for risk stratification of pediatric patients with ALL.

Abstract

Monitoring of minimal residual disease (MRD) by flow cytometry (FCM) is a powerful prognostic tool for predicting outcomes in acute lymphoblastic leukemia (ALL). To apply FCM-MRD in large, collaborative trials, dedicated laboratory staff must be educated to concordantly high levels of expertise and their performance quality should be continuously monitored. We sought to install a unique and comprehensive training and quality control (QC) program involving a large number of reference laboratories within the international Berlin-Frankfurt-Münster (I-BFM) consortium, in order to complement the standardization of the methodology with an educational component and persistent quality control measures. Our QC and quality assurance (QA) program is based on four major cornerstones: (i) a twinning maturation program, (ii) obligatory participation in external QA programs (spiked sample send around, United Kingdom National External Quality Assessment Service (UK NEQAS)), (iii) regular participation in list-mode-data (LMD) file ring trials (FCM data file send arounds), and (iv) surveys of independent data derived from trial results. We demonstrate that the training of laboratories using experienced twinning partners, along with continuous educational feedback significantly improves the performance of laboratories in detecting and quantifying MRD in pediatric ALL patients. Overall, our extensive education and quality control program improved inter-laboratory concordance rates of FCM-MRD assessments and ultimately led to a very high conformity of risk estimates in independent patient cohorts.

New cytometry tools for immune monitoring during cancer immunotherapy

The success of cancer immunotherapy (CIT) in the past decade has brought renewed excitement and the need to better understand how the human immune system functions during health and disease. Advances in single cell technologies have also inspired the creation of a Human Cell Atlas to identify and describe every cell in the human body with the intention of elucidating how to "fix" the ones that fail normal function. For example, treatment of cancer patients with immune checkpoint blockade (ICB) antibodies can reinvigorate their T cells and produce durable clinical benefit in a subset of patients, but a number of resistance mechanisms exist that prohibit full benefit to all treated patients. Early detection of biomarkers of response and mechanisms of resistance are needed to identify the patients who can benefit most from ICB. A noninvasive approach to predict treatment outcomes early after immunotherapies is a longitudinal analysis of peripheral blood immune cells using flow cytometry. Here we review some of the advances in our understanding of how ICB antibodies can re-invigorate tumor-specific T cells and also highlight the recent advances in high complexity flow cytometry, including spectral cytometers, that allow longitudinal sampling and deep immune phenotyping in clinical settings. We encourage the scientific community to utilize advanced cytometry platforms and analyses for immune monitoring in order to optimize CIT treatments for maximum clinical benefit.

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Since the publication of the Society for Immunotherapy of Cancer's (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients.

Standardization procedure for flow cytometry data harmonization in prospective multicenter studies

One of the most challenging objective for clinical cytometry in prospective multicenter immunomonitoring trials is to compare frequencies, absolute numbers of leukocyte populations and further the mean fluorescence intensities of cell markers, especially when the data are generated from different instruments. Here, we describe an innovative standardization workflow to compare all data to carry out any large-scale, prospective multicentric flow cytometry analysis whatever the duration, the number or type of instruments required for the realization of such projects.

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.

Flow cytometry data analysis: Recent tools and algorithms

Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R-based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.

T cell Co-Stimulatory molecules ICOS and CD28 stratify idiopathic pulmonary fibrosis survival

Idiopathic pulmonary fibrosis (IPF) is a devastating disease that kills as many Americans as breast cancer each year. This study investigated whether lung function decline and survival associates with adaptive immunity in patients with IPF, specifically the expression of checkpoint molecules ICOS, CD28 and PD-1 on circulating CD4 T cells. Clinical data, blood samples and pulmonary function tests were collected prospectively and longitudinally from 59 patients with IPF over a study period of 5 years. Patients were followed until death, lung transplantation, or study end, and cell surface expression of CD45RO, CD28, ICOS, and PD-1 was measured on CD4 T cells via flow cytometry. Repeated measures of ICOS and CD28 on CD4 T cells revealed significant associations between declining ICOS and CD28 expression, and declining lung function parameters FVC and DLCO, independent of age, sex, race, smoking history, or immunosuppressant use. Strikingly, patients in the highest quintile of ICOS at study entry had markedly improved survival, while those with low CD28 fared poorly. No change in PD-1 expression was found. Analysis of ICOS and CD28 from the first blood draw identified three populations of IPF patients; those at high risk for early death, those with intermediate risk, and those at low risk. These results highlight the role of T cell mediated immunity in IPF survival, finding the assessment of two T cell stimulatory checkpoint molecules, CD28 and ICOS, was sufficient to discriminate three distinct survival trajectories over 5 years of patient follow up.

Flow-Cytometric Monitoring of Minimal Residual Disease in Pediatric Patients With Acute Myeloid Leukemia: Recent Advances and Future Strategies

Minimal residual disease (MRD) by multiparametric flow cytometry (MFC) has been recently shown as a strong and independent prognostic marker of relapse in pediatric AML (pedAML) when measured at specific time points during Induction and/or Consolidation therapy. Hence, MFC-MRD has the potential to refine the current strategies of pedAML risk stratification, traditionally based on the cytogenetic and molecular genetic aberrations at diagnosis. Consequently, it may guide the modulation of therapy intensity and clinical decision making. However, the use of non-standardized protocols, including different staining panels, analysis, and gating strategies, may hamper a broad implementation of MFC-MRD monitoring in clinical routine. Besides, the thresholds of MRD positivity still need to be validated in large, prospective and multi-center clinical studies, as well as optimal time points of MRD assessment during therapy, to better discriminate patients with different prognosis. In the present review, we summarize the most relevant findings on MFC-MRD testing in pedAML. We examine the clinical significance of MFC-MRD and the recent advances in its standardization, including innovative approaches with an automated analysis of MFC-MRD data. We also touch upon other technologies for MRD assessment in AML, such as quantitative genomic breakpoint PCR, current challenges and future strategies to enable full incorporation of MFC-MRD into clinical practice.