Multiparameter Flow Cytometry Data Research Articles

Immunophenotyping myelodysplastic neoplasms: the role of flow cytometry in the molecular classification era.

The unique heterogenous landscape of myelodysplastic syndromes/neoplasms (MDS) has resulted in continuous redefinition of disease sub-entities, in view of the novel translational research data that have clarified several areas of the pathogenesis and the progression of the disease. The new international classifications (WHO 2022, ICC 2022) have incorporated genomic data defining phenotypical alterations, that guide clinical management of specific patient subgroups. On the other hand, for over a decade, multiparameter flow cytometry (MFC) has proven its value as a complementary diagnostic tool for these diseases and although it has never been established as a mandatory test for the baseline evaluation of MDS patients in international guidelines, it is almost universally adopted in everyday clinical practice for the assessment of suspected cytopenias through simplified scoring systems or elaborate analytical strategies for the detection of immunophenotypical dysplastic features in every hematopoietic cell lineage in the bone marrow (BM). In this review, we explore the clinically meaningful interplay of MFC data and genetic profiles of MDS patients, to reveal the currently existing and the potential future role of each methodology for routine clinical practice, and the benefit of the patients. We reviewed the existing knowledge and recent advances in the field and discuss how an integrated approach could lead to patient re-stratification and guide personalized management.

Blood immune cell profiling in adults with longstanding type 1 diabetes is associated with macrovascular complications.

There is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity. Adult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects. Hierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications. Machine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications.

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.

Persistence of KIRneg NK cells after haploidentical hematopoietic stem cell transplantation protects from human cytomegalovirus infection/reactivation.

Haploidentical hematopoietic stem cell transplantation (h-HSCT) is a therapeutic option to cure patients affected by hematologic malignancies. The kinetics and the quality of immune-reconstitution (IR) impact the clinical outcome of h-HSCT and limit the onset of life-threatening Human Cytomegalovirus (HCMV) infection/reactivation. Natural Killer (NK) cells are the first lymphocytes that recover after h-HSCT and they can provide rapid innate immune responses against opportunistic pathogens. By performing a longitudinal single-cell analysis of multiparametric flow-cytometry data, we show here that the persistence at high frequencies of CD158b1b2jneg/NKG2Apos/NKG2Cneg/NKp30pos/NKp46pos (KIRneg) NK cells is associated with HCMV infection/reactivation control. These KIRneg NK cells are "unlicensed", and are not terminal-differentiated lymphocytes appearing early during IR and mainly belonging to CD56bright/CD16neg and CD56bright/CD16pos subsets. KIRneg NK cells are enriched in oxidative and glucose metabolism pathways, produce interferon-γ, and are endowed with potent antiviral activity against HCMV ex vivo. Decreased frequencies of KIRneg NK cells early during IR are associated with clinically relevant HCMV replication. Taken together, our findings indicate that the prolonged persistence of KIRneg NK cells after h-HSCT could serve as a biomarker to better predict HCMV infection/reactivation. This phenomenon also paves the way to optimize anti-viral immune responses by enriching post-transplant donor lymphocyte infusions with KIRneg NK cells.

Side-by-Side Comparison of Compensation Beads Used in Polychromatic Flow Cytometry.

Compensation or unmixing is essential in analyzing multiparameter flow cytometry data. Errors in data correction, either by compensation or unmixing, can completely change the outcome or mislead the researchers. Owing to limited cell numbers, researchers often use synthetic beads to generate the required single stains for the necessary calculation. In this study, the capacity of synthetic beads to influence data correction is evaluated. Corrected data for human peripheral blood cells were generated using cell-based compensation from the same cells or bead-based compensation to identify differences between the methods. These data suggest that correction with beads on full-spectrum and conventional cytometers does not always follow the basic flow compensation/unmixing expectations and alters the data. Overall, the best approach for bead-based correction for an experiment is to evaluate which beads and fluorochromes are most accurately compensated/unmixed.

Immunophenotypic portrait of leukemia-associated-phenotype markers in B acute lymphoblastic leukemia.

Multiparametric flow cytometry (MFC) is an essential diagnostic tool in B acute lymphoblastic leukemia (B ALL) to determine the B-lineage affiliation of the blast population and to define their complete immunophenotypic profile. Most MFC strategies used in routine laboratories include leukemia-associated phenotype (LAP) markers, whose expression profiles can be difficult to interpret. The aim of our study was to reach a better understanding of 7 LAP markers' landscape in B ALL: CD9, CD21, CD66c, CD58, CD81, CD123, and NG2. Using a 10-color MFC approach, we evaluated the level of expression of 7 LAP markers including CD9, CD21, CD66c, CD58, CD81, CD123, and NG2, at the surface of normal peripheral blood leukocytes (n = 10 healthy donors), of normal precursor B regenerative cells (n = 40 uninvolved bone marrow samples) and of lymphoblasts (n = 100 peripheral blood samples or bone marrow samples from B ALL patients at diagnosis). The expression profile of B lymphoblasts was analyzed according the presence or absence of recurrent cytogenetic aberrations. The prognostic value of the 7 LAP markers was examined using Maxstat R algorithm. In order to help the interpretation of the MFC data in routine laboratories, we first determined internal positive and negative populations among normal leukocytes for each of the seven evaluated LAP markers. Second, their profile of expression was evaluated in normal B cell differentiation in comparison with B lymphoblasts to establish a synopsis of their expression in normal hematogones. We then evaluated the frequency of expression of these LAP markers at the surface of B lymphoblasts at diagnosis of B ALL. CD9 was expressed in 60% of the cases, CD21 in only 3% of the cases, CD58 in 96% of the cases, CD66c in 45% of the cases, CD81 in 97% of the cases, CD123 in 72% of the cases, and NG2 in only 2% of the cases. We confirmed the interest of the CD81/CD58 MFI expression ratio as a way to discriminate hematogones from lymphoblasts. We observed a significant lower expression of CD9 and of CD81 at the surface of B lymphoblasts with a t(9;22)(BCR-ABL) in comparison with B lymphoblasts without any recurrent cytogenetic alteration (p = 0.0317 and p = 0.0011, respectively) and with B lymphoblasts harboring other cytogenetic recurrent abnormalities (p = 0.0032 and p < 0.0001, respectively). B lymphoblasts with t(1;19) at diagnosis significantly overexpressed CD81 when compared with B lymphoblasts with other recurrent cytogenetic abnormalities or without any recurrent alteration (p = 0.0001). An overexpression of CD58 was also observed in the cases harboring this abnormal cytogenetic event, when compared with B lymphoblasts with other recurrent cytogenetic abnormalities (p = 0.030), or without any recurrent alteration (p = 0.0002). In addition, a high expression of CD123, of CD58 and of CD81 was associated with a favorable prognosis in our cohort of pediatric and young adult B ALL patients. We finally built a risk score based on the expression of these 3 LAP markers, this scoring approach being able to split these patients into a high-risk group (17%) and a better outcome group (83%, p < 0.0001). The complexity of the phenotypic signature of lymphoblasts at diagnosis of B ALL is illustrated by the variability in the expression of LAP antigens. Knowledge of the expression levels of these markers in normal leukocytes and during normal B differentiation is crucial for an optimal interpretation of diagnostic cytometry results and serves as a basis for the biological follow-up of B ALL.

Merging and imputation of flow cytometry data: a critical assessment.

Although most modern techniques and analysis methods in multiparameter flow cytometry (MFC) allow for increased dimensionality for the characterization and quantification of cell populations, most MFC applications depend on flow cytometers measuring relatively small (<16) numbers of parameters. When more markers than the available parameters need to be acquired, these are commonly distributed over multiple independent measurements that include a backbone of common markers. Several methods have been proposed to impute values for combinations of markers that were not measured simultaneously. These imputation methods are frequently used without proper validation and knowledge of their effects on data analysis. We evaluated the performance of existing imputation software (Infinicyt, CyTOFmerge, CytoBackBone, and cyCombine) in approximating known measured expression data in terms of similarity in visual appearance, cell expression, and gating in different datasets by splitting MFC samples into separate measurements with partially overlapping markers and re-calculating missing marker expression. Out of the assessed packages, CyTOFmerge showed the most accurate approximation of the known expression in terms of similar expression values and concordance with manual gating, with a mean F-score between 0.53-0.87 when retrieving cell populations in different datasets. Performance remained inadequate for all methods, with only limited similarity at the cell level. In conclusion, the use of imputed MFC data should take such limitations into account and include independent validation of results to justify conclusions. This article is protected by copyright. All rights reserved.

Five-year follow-up update of defined-composition CD19 CAR T-cell therapy for relapsed/refractory CLL.

7511 Background: We have described high response rates in patients (pts) with relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL) treated with CD19-targeted CAR T cells. We now report comprehensive analyses of factors associated with duration of response (DOR) with a 68-month median follow-up. Methods: We included 49 pts with R/R CLL and/or Richter’s transformation (n = 10) treated on a phase I/II clinical trial of defined-composition CD19 CAR T-cell therapy (1:1 ratio of CD8+:CD4+). Response rates were investigator-assessed by 2018 iwCLL criteria. Measurable residual disease (MRD) in bone marrow (BM) was evaluated by multiparameter flow cytometry (MFC) and IGH next-generation sequencing (NGS; clonoSEQ assay). CAR T-cell enumeration was assessed by MFC. Serum cytokine concentrations (20 analytes) were measured by Luminex assay. Univariate associations between duration of response (DOR) and patient, disease, CAR T-cell manufacturing, and treatment-related variables were estimated using Cox regression (50+ variables). Association between CAR T-cell in vivo persistence and DOR was modeled with a time-dependent Cox model including blood CAR transgene levels (copies/µg genomic DNA). Results: Median age was 61 years (IQR, 55-67). Forty-six pts (94%) had complex karyotype and/or 17p deletion; 46 (94%) received cyclophosphamide/fludarabine lymphodepletion. In the response-evaluable cohort (n = 47), median follow-up was 68.3 months (IQR, 54.3-81.6). Overall and complete response rates at day+28 were 70% and 17%, respectively, and the median DOR was 22.2 months (95% CI, 10.7-not reached [NR]). MRD-negativity at day+28 by MFC occurred in 33/47 (70%). In pts with MRD-negativity by MFC and available data, MRD negativity by NGS was seen in 18/29 (62%). In NGS MRD-negative responders, the median DOR was 56.8 months (95% CI, 39.5-NR). In all evaluable patients, the 5-year OS and PFS were 35.5% (95% CI, 24.0%-52.5%) and 21.5% (95% CI, 12.0%-38.5%), respectively. In univariate Cox regression, day+28 MRD-negativity by MFC (HR = 0.03, 95% CI, 0.01-0.18, p &lt; 0.001), day+28 MRD-negativity by NGS (HR = 0.21, 95% CI, 0.07-0.61, p = 0.004), peak CD8+ CAR T-cell expansion (HR = 0.49, 95% CI, 0.24-1.00, p = 0.05), CAR T-cell persistence (HR = 0.71, 95% CI, 0.54-0.91, p = 0.01), and lower peak MIP-1β (HR = 3.12, 95% CI, 1.13-8.65, p = 0.03) were associated with longer DOR. Peak MIP-1β remained associated with DOR (HR = 3.83, 95% CI, 1.35-10.9, p = 0.012) in a multivariable model including pre-LD CLL cells in BM, tumor cross-sectional area, peak CD8+ CAR T-cell expansion, and day+28 MRD by MFC. Conclusions: CD19 CAR T-cell therapy achieved durable remissions in R/R CLL pts. Day+28 MRD, CAR T-cell peak expansion, and CAR T-cell persistence were strongly associated with DOR. Lower peak serum levels of MIP-1β, a cytokine known to be produced by CLL cells upon BCR activation, was strongly and independently associated with DOR. Clinical trial information: NCT01865617 .

Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group.

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.

Single Centre, Retrospective Analysis of Treatment Outcomes and Prognostic Factors in Blastic Phase Chronic Myeloid Leukemia (CML-BP) Following Systemic Therapy

Introduction: Despite the introduction of BCR-ABL1 tyrosine kinase inhibitors (TKI), the prognosis of blastic phase chronic myeloid leukemia (CML-BP) remains dismal. Due to its low incidence, prospective evaluation of outcomes of CML-BP following systemic treatment is challenging. Thus, we aimed to evaluate the outcomes of CML-BP following systemic therapy, and to analyze prognostic factors affecting treatment outcomes. Methods: We retrospectively identified 55 patients with CML-BP per the European LeukaemiaNet 2013 criteria who treated at the Princess Margaret Cancer Centre in Toronto, Canada between 2000 and 2022. Patient and disease characteristics at the time of CML-BP diagnosis are presented in Table 1. Multiparametric flow cytometry data are described below, with percentages reflecting the proportion of positive antigen expression in evaluable patients. Treatment outcomes measured included: complete response (CR), leukaemia-free survival (LFS) and overall survival (OS). Prognostic factors evaluated include: age, transformation from chronic/accelerated phase (CP/AP), clonal evolution (CE), additional cytogenetic abnormalities (ACA), ABL1 KD mutation, extramedullary disease, blast immunophenotype, systemic treatment modality (intensive chemotherapy (IC) +/- TKI vs TKI monotherapy) and allogeneic haematopoietic stem cell transplantation (HCT). Results: Among the 55 patients, the median age at time of CML-BP diagnosis was 45 years (17 - 68) and included 35 (64%) cases of CML in myeloid BP (MBP) and 20 (36%) cases of lymphoid BP (LBP). 40 (72%) patients had transformed from a previous CP/AP with a median time of 2.4 years (0.1 - 19.3) from initial diagnosis to transformation. Extramedullary disease was present in 18/53 (34%) patients. The blast immunophenotype of MBP patients (n=31) was: CD34+ (84%), CD117+ (52%), HLA-DR+ (68%), CD13+ (87%), CD33+ (97%), MPO+ (40%), CD38+ (100%) and CD123+ (50%). Nine MBP patients (36%) had aberrant CD7 expression. Among 17 evaluable LBP patients, the immunophenotypic profile was: CD34+ (94%), HLA-DR+ (76%), CD19+ (94%), CD20+ (41%), CD10+ (94%), TdT (88%), CD38+ (100%) and CD7+ (18%). Nine patients (53%) had aberrant CD13 expression and five (29%) displayed aberrant CD33 expression. At CML-BP diagnosis, 21/42 (50%) had an ACA, of which 11 (26%) patients had high risk ACA, defined as −7/7q, 11q23 or 3q26.2 aberrations, or complex karyotype. Among transformed patients, CE was present in 16/43 (37%) patients. In 18 patients evaluated with Sanger sequencing at time of BP transformation, 9 patients (50%) had an ABL1-KD mutation. 54/55 received systemic therapy: 31 (57%) intensive chemotherapy (IC) + TKI, 7 (13%) IC alone, and 16 (30%) TKI monotherapy. Two patients died before response assessment, one after IC and one after TKI monotherapy. Among 46 patients with available post-treatment bone marrow assessments, CR/CRi rates were similar in those treated with IC+ TKI (29/31; 94%), IC only (5/6; 83%) or TKI only (7/9; 78%). Overall, 40/54 (74%) patients proceeded to allogeneic HCT after achieving CR/CRi or second CP. After a median follow-up of 39 months, the 3-year LFS and OS rates of all patients were 34.9% (20.7-49.6%) and 44.9% (95% CI, 30.4-58.4%), respectively. Patients with CML-LBP had a significantly higher OS compared to those with CML-MBP (p=0.0463), however, no significant difference in LFS was seen (p= 0.0913). Median duration of LFS and OS was 1.56 and 2.51 years, respectively. The following variables were found to be independently prognostic for OS: Transformation from CP/AP (p=0.0489, HR 4.328 [1.007, 18.60]), CD117 expression at time of CML-BP (p=0.0291, HR 2.809 [1.111, 7.102]), and IC + TKI (vs TKI monotherapy) (p=0.0068, HR 0.268 [0.104, 0.689]). CE at BP, transformation from CP/AP and IC +TKI (vs TKI monotherapy) were prognostic for LFS by univariate analysis, however, none was identified as independent risk factors. Conclusion The prognosis of CML-BP remains poor despite systemic therapy. Transformation from CP/AP, CD117 positivity at diagnosis of CML-BP and TKI monotherapy were identified as independent, adverse prognostic risk factors for OS. However, treatment with IC + TKI appeared to improve survival outcomes compared with TKI therapy alone. Therefore, combination therapy with IC + TKI should be considered the optimal strategy in treatment patients with CML-BP. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

High-potency synthetic STING agonists rewire myeloid stroma in the tumour microenvironment to amplify immune checkpoint blockade efficacy in refractory pancreatic cancer.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is clinically unresponsive to immune checkpoint blockade (ICB) immunotherapy. Dense immunosuppressive myeloid stroma (MS) and consequent T cell exclusion from the tumour microenvironment renders PDAC resistant to immune-based therapies. Innate immune activation of the MS via cyclic dinucleotide (CDN) agonists of the STING (Stimulator of Interferon Genes) pathway can trigger T cell infiltration into cold tumours leading to robust anti-tumour immunity. Despite proven therapeutic efficacy in preclinical models, the cellular mechanisms of how CDNs reprogram the suppressive MS to sensitise tumours to ICB is poorly understood. Using multi-omic profiling of MDSCs and M2 Macrophages of human and murine origin, we show that the synthetic STING agonist, IACS-8803, rewires these populations from immunosuppressive to immune-permissive phenotypes in part through inhibition of c-Myc signaling, energy metabolism modulation, and antagonism of cell cycle. Furthermore, dimensionality reduction analyses of multiparameter flow cytometry data and survival studies in a KPC-derived model of PDAC show a proinflammatory remodeling of the MS and an increased T/NK cell infiltration and function by synthetic CDN STING agonists into the tumour that underlies an amplified therapeutic response to ICB. We are the first to report that synthetic CDN STING agonists affect MDSC and M2 macrophage repolarization through altering energy metabolism, cell cycle dynamics and c-Myc signalling. This study uncovers molecular and cellular mechanisms by which high-potency STING agonists drive a proinflammatory conversion of the tumour MS to overcome resistance to ICB in an aggressive orthotopic tumour model of PDAC. Supported by grants from PanCan (206100-90-100300-54-00006617-11111)

Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic Flow-Self Organizing Maps algorithm.

BackgroundThe Flow‐Self Organizing Maps (FlowSOM) artificial intelligence (AI) program, available within the Bioconductor open‐source R‐project, allows for an unsupervised visualization and interpretation of multiparameter flow cytometry (MFC) data.MethodsApplied to a reference merged file from 11 normal bone marrows (BM) analyzed with an MFC panel targeting erythropoiesis, FlowSOM allowed to identify six subpopulations of erythropoietic precursors (EPs). In order to find out how this program would help in the characterization of abnormalities in erythropoiesis, MFC data from list‐mode files of 16 patients (5 with non‐clonal anemia and 11 with myelodysplastic syndrome [MDS] at diagnosis) were analyzed.ResultsUnsupervised FlowSOM analysis identified 18 additional subsets of EPs not present in the merged normal BM samples. Most of them involved subtle unexpected and previously unreported modifications in CD36 and/or CD71 antigen expression and in side scatter characteristics. Three patterns were observed in MDS patient samples: i) EPs with decreased proliferation and abnormal proliferating precursors, ii) EPs with a normal proliferating fraction and maturation defects in late precursors, and iii) EPs with a reduced erythropoietic fraction but mostly normal patterns suggesting that erythropoiesis was less affected. Additionally, analysis of sequential samples from an MDS patient under treatment showed a decrease of abnormal subsets after azacytidine treatment and near normalization after allogeneic hematopoietic stem‐cell transplantation.ConclusionUnsupervised clustering analysis of MFC data discloses subtle alterations in erythropoiesis not detectable by cytology nor FCM supervised analysis. This novel AI analytical approach sheds some new light on the pathophysiology of these conditions.

Single-Cell Transcriptomics for Residual Disease Detection in Acute Myelogenous Leukemia Post Allogeneic Hematopoietic Cell Transplantation

Detection of residual disease is a critical component of modern, risk-adapted therapy for Acute Myeloid Leukemia (AML). However, the genetic and phenotypic diversity of AML has made the development of a universal assay for disease assessment particularly challenging. While purely mutation-based tests promise high sensitivity, they are not broadly applicable given molecular heterogeneity and complex clonal evolution. Single-cell approaches, such as multiparameter flow cytometry (MFC), are more broadly applicable and increasingly accepted as the standard in clinical care. However, the limited number of leukemia-specific cell-surface markers and high numbers of shared markers between malignant myeloid blasts and healthy progenitors make MFC data extremely challenging to interpret. Motivated to develop a broadly applicable assay that can provide a more confident assessment of residual disease, we developed a platform using droplet-partitioned single-cell RNA sequencing accompanied by a computational pipeline specifically tailored to quantify residual disease after allogeneic HCT (alloHCT).With bone marrow samples from an 11-year-old patient with suspected post-alloHCT relapse of AML, we interrogated three methods of sample processing, 1) RBC lysis, 2) Ficoll-centrifugation, and 3) Ficoll-centrifugation combined with CD34+ immunomagnetic selection. The samples were further split to separately capture the 3' or 5' end of polyadenylated transcripts. The six resulting libraries were sequenced using standard short-read sequencing, and reads were demultiplexed and counted using common workflows. Data from the samples were combined, and sub-populations were visualized using UMAP (see Figure).This study demonstrated the feasibility of real-time single-cell sequencing for clinical utility. It is possible to process, capture, and sequence a patient's sample in approximately three working days (A). By integrating our data with single-cell expression profiles from an atlas of healthy human bone marrow, we were able to identify cells with gene-expression programs distinct from those of normal hematopoietic cells (B). With these integrated data, we could clearly identify populations of cells that embed away from healthy atlas cells (yellow circle, B), defining a different than normal single-cell profile. This “malignant” profile also included several genes whose expression is usually restricted to healthy hematopoietic progenitors (Panel C), suggesting these cells had a severely dysregulated transcriptome. As this patient was post-alloHCT, we interrogated the abundance of single-nucleotide-polymorphisms (SNPs) in the sequence data. We quantified these SNPs in single cells to distinguish each cell as either of donor or recipient origin using a method we have previously validated for genotyping RNA sequence in single cells. We clearly demonstrate that those cells identified as “different than normal” have a distinct SNP profile suggesting they are of recipient origin. Further analysis revealed that this malignant population was highly enriched for a population of cells expressing a previously described set of “AML-restricted genes” (Huang, B. et al., ASH 2021). (Panel E). Finally, from the Ficoll-processed sample, we quantified a level of 9.8% residual disease (243 malignant cells from a total of 2487). Notably, the number of abnormal myeloid progenitors determined by MFC was 2.0% which increased to 13% on a subsequent marrow sample drawn one week later. Incidentally, we observed only minimal differences across the two single-cell sequencing chemistries (3' vs. 5').Taken together, our data strongly argue that droplet-based, single-cell RNA sequencing is a feasible and powerful tool for the ascertainment of residual disease in AML. Given the robust nature of the platform and the ability to incorporate SNP integration into the analytic pipeline, it allows confident detection of residual disease in the post-alloHCT setting. By combining genomic quantification of transcripts with the power of SNP-based genotyping all at the level of the single cells, we believe this technology can substantially improve our diagnosis of post-alloHCT AML relapse. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Assessing the added value of bone marrow morphologic evaluation beyond flow cytometry for disease detection in the setting of acute myeloid leukemia after chemotherapy

Abstract Complete response after acute myeloid leukemia (AML) therapy historically requires a decrease in bone marrow (BM) blasts to &amp;lt;5% by morphologic assessment. However, accumulating data indicate that measurable residual disease by more sensitive multiparameter flow cytometry (MFC) is a better predictor of outcome than traditional morphologic evaluation. In most cases, morphology alone cannot differentiate between regenerative and neoplastic blasts, raising the question of its added value to more specific MFC. The objective of this study was to assess the value morphologic evaluation adds to MFC for the detection of residual AML in the post-chemotherapy (induction or consolidation) setting. A search of our pathology database identified 47 consecutive nontransplant BM evaluations (41 total patients) in the post-chemotherapy setting between January and March 2020. The morphology portion for each case was reviewed by three independent hematopathologists for evaluation of adequacy (&amp;gt;200 cells), blast count, trilineage hematopoiesis, dysplastic features, focal/patchy disease, and any non-hematopoietic or alternative diagnoses that would not have been discovered by the associated MFC study. The MFC data for each case was separately reviewed by two hematopathologists. Six of the 47 cases showed &amp;gt;5% blasts by morphology (range 8%-70%); in all six cases, &amp;gt;5% abnormal blasts were also identified by MFC (range 23%-78%). Of cases with &amp;lt;5% blasts by morphology (defined as negative by morphology), MFC detected &amp;gt;5% abnormal blasts in four cases and &amp;lt;5% abnormal blasts in five cases (range 0.007-14%). One of these additional nine cases by MFC was identified to have focally clustered blasts by morphology (7% blasts by MFC, &amp;lt;5% overall by morphology). Two of the nine additional cases by MFC were deemed to have inadequate aspirates by morphology. In total, four of the 47 morphology cases had inadequate aspirate smears (9%). Of these four cases, three had adequate particle preparations. Eleven cases overall had corresponding core biopsies submitted, of which five were inadequate (45%). Morphologic examination identified seven cases with dysplastic features; abnormal blasts were identified in four of these cases by MFC (all &amp;gt;5% blasts). In the remaining three cases with dysplasia, ancillary molecular studies supported molecular evidence of residual disease in one case (NPM1 mutation and FLT3-ITD positivity by PCR). No unexpected carcinomas or other findings that would not be detected by the associated MFC study were identified. In conclusion, in our study, morphologic evaluation did not definitively identify any additional cases of residual/recurrent AML that were not identified by MFC. Morphologic evaluation detected dysplasia without abnormal blasts in three cases; however, the significance was unclear without supportive molecular studies. Reducing the number of cases for morphologic evaluation could save resources and improve turnaround time if MFC can provide the relevant details to assess response.

The Clinical Significance of Mesenteric Lymphocytes in Human Colorectal Cancer.

ObjectiveThe mesentery is a potential site of residual tumor in patients with colorectal cancer (CRC). However, the mesenteric immune microenvironment remains unclear. In this study, we investigated the immune landscape of the mesentery, particularly the role of lymphocytes and its association with the clinicopathological characteristics of CRC.MethodsFlow cytometry was used to detect lymphocytes in the paired mesenteric tissue specimens adjacent to the colorectal tumors and normal mesenteric tissue specimens 10 cm away from the colorectal tumor edge and preoperative peripheral blood samples obtained from patients with CRC who underwent surgery. T-distributed stochastic neighbor embedding was utilized to analyze multiparameter flow cytometry data. Multiplex immunohistochemistry was performed to evaluate T cells subsets in the paired mesentery adjacent to the colorectal tumors and normal mesentery. The Fisher’s exact test and non-parametric Wilcoxon’s matched-pairs tests were used for statistical analysis. The non-parametric Mann-Whitney U test was used to determine associations between percentage data and clinical parameters of patients with CRC.ResultsWe found that immune cells in the normal mesentery were mainly of lymphoid lineage. Compared with peripheral blood, the normal mesentery showed decreased NK cells and the CD4/CD8 ratio and increased CD3+ CD56+, memory CD4+ T, memory CD8+ T, CD4+ tissue-resident memory T (TRM), and CD8+ TRM cells. Compared with the normal mesentery, the mesentery adjacent to the colorectal tumor showed increased B and regulatory T cells and decreased NK, CD3+ CD56+, CD4+ TRM, and CD8+ TRM cells. Moreover, memory CD8+ T cells and plasmablasts are negatively correlated with the depth of invasion of CRC. Increased memory CD4+ T cells are associated with distant metastasis of CRC and high preoperative serum carcinoembryonic antigen levels.ConclusionThe mesentery shows a specific immune microenvironment, which differs from that observed in peripheral blood. CRC can alter the mesenteric immune response to promote tumor progression.

Unsupervised Flow Cytometry Analysis: Application to Minimal Residual Disease Detection in a Cohort of 40 Acute Myeloblastic Leukemia Patients with Molecular Markers

Minimal/measurable residual disease (MRD) assessment in the course of acute myeloblastic leukemia (AML) monitoring is a topic that currently reaches undisputable interest. Multiparameter flow cytometry (MFC) appears as a valuable, rapid tool to assess response to therapy. Within the European LeukemiaNet (ELN) a task force has promoted the use of a backbone panel of monoclonal antibodies likely to cover both the most common leukemia associated immunophenotyping patterns (LAIP) and different from normal (DfN) immunophenotypes (Schuurhuis et al., Blood 2018). Classical gating strategies of MRD assessment however rely on supervised/operator-dependent approaches. The development of mass-cytometry has led to the emergence of new unsupervised analysis software. Among them, the FlowSOM (Flow Self Organizing Maps) R-based solution has been recognized as the most efficient, applicable to both mass and classical flow cytometry. Combination of the R-based FlowSOM definition of self-organized minimal spanning trees (MST) and the Kaluza® classical flow cytometry analysis software allows for a combined exploration of a normal bone marrow (NBM) matrix (derived from 19 normal BM samples)together with individual diagnosis and follow-up (FU) samples. This strategy was applied to 40 AML patients treated with a conventional 3+7 regimen for whom at least post induction FU was available, together with targets allowing for molecular MRD assessment (NPM1, RUNX1, CBFB-MYHorBCR-ABL). According to previously published panels (GEIL, Cytometry part B, 2018), two tubes were tested in MFC and results were compared to molecular MRD results for 96 FU time points. All patients could be evaluated in MFC at all time points. Comparison of molecular and MFC data showed an overall good concordance (80.2%), especially for FU1 time points (87.5%) with a sensitivity of 0.89 and a specificity of 0.75. Yet, outcome after induction appeared to be more correlated with MFC-negative MRD. This work also allowed to demonstrate the particular ability of the FlowSOM algorithm to disclose subtle chemoresistant subclones during FU. Moreover, by systematic comparison of normal bone marrow and FU points, MRD could be asserted without ambiguity and regenerating BM identified as such. Of note, this work could be performed on retrospective data, based on the fact that instruments were harmonized as reported (Lacombe et al. Leukemia, 2016) and identical panels were used all along. This makes this strategy easily transferrable to a network of laboratories using consensus settings and panels, as proposed by the European LeukemiaNet (Schuurhuis et al., Blood 2018). All in all, this study shows that unsupervised MFC MRD assessment appears as an accurate tool to appreciate response to chemotherapy in AML, and the FlowSOM + Kaluza method, combined with a systematic diagnostic FU / NBM multicomparison approach, increases the resolving power in the monitoring of MRD compared to the classic LAIP/different from normal approaches as implemented to date. Disclosures Dumas: Abbvie:Honoraria;Jazz-pharmaceutical:Consultancy;Daiichi Sankyo:Consultancy, Research Funding;Astellas:Research Funding, Speakers Bureau.

The Impact of COVID-19 on the State of Clinical and Laboratory Research Globally in Transplantation in May 2020.

The Impact of COVID-19 on the State of Clinical and Laboratory Research Globally in Transplantation in May 2020.

Multiparameter flow cytometry (MFC) and next generation sequencing (NGS) for minimal residual disease (MRD) evaluation: Results of the FORTE trial in newly diagnosed multiple myeloma (MM).

8533 Background: The role of MRD by MFC and NGS is well known in MM, with few data on the concordance of the two techniques. We analyzed and compared MRD data from the FORTE trial both by MFC and NGS. Methods: Newly diagnosed MM patients (pts) ≤65 years were randomized to: carfilzomib, lenalidomide, dexamethasone (KRd) induction-autologous stem cell transplant (ASCT) - KRd consolidation (KRd_ASCT); 12 KRd cycles (KRd12); carfilzomib, cyclophosphamide, dexamethasone (KCd) induction-ASCT-KCd consolidation (KCd_ASCT). Pts were then randomized to maintenance: lenalidomide alone or plus carfilzomib. MRD was assessed by 8-color second generation flow cytometry (sensitivity 10−5) in pts with ≥very good partial response (VGPR) before maintenance. In a subgroup of these pts, next generation flow (NGF; sensitivity 10−5-10−6) was performed. In ≥CR pts, MRD pre-maintenance was also assessed by NGS (Adaptive Biotechnologies; sensitivity 10−5-10−6). Thus, both MFC and NGS evaluations were available only in ≥CR pts. 1-year sustained MRD negativity by MFC and NGS was also analyzed in pts with at least one sample available at least 1 year apart. Results: MFC and NGS data were available in 184/233 (79%) CR pts (66 KRd_ASCT, 67 KRd12 and 51 KCd_ASCT). Median age of this ≥CR population was 57 years (IQR 52-62), 13% had International Staging System (ISS) III and 27% high risk cytogenetics by FISH [either del(17p) or t(4;14) or t(14;16)]. Table reports MRD negativity rate by MFC and NGS at a cut-off of 10−5 and 10−6 among ≥CR negative pts in the 3 arms. NGS negativity at a cut-off 10−6 was found in 36/133 (27%) ≥CR pts (for 51/184 CR pts 10−6 sensitivity was not reached). In evaluable pts, 1-year sustained 10−5 MRD negativity by MFC and NGS was superimposable (83%). We evaluated concordance of MRD results by the two techniques and observed agreement was 86% for MFC and NGS at 10−5 evaluable samples (n: 335; r: 0.61) and 78% for MFC and NGS at 10−6 evaluable samples (n: 56; r: 0.77). Conclusions: In pts who achieved ≥CR, similar rate of pre-maintenance 10−5 negativity by MFC and NGS has been reached in each arm, with 83% pts maintaining 1-year MFC or NGS 10−5 sustained MRD negativity. Concordance between MFC and NGS was good, particularly when the same sensitivity was reached. Longer follow up is needed to draw definitive conclusions. Clinical trial information: NCT02203643 . [Table: see text]

Phenotypical T Cell Differentiation Analysis: A Diagnostic and Predictive Tool in the Study of Primary Immunodeficiencies.

Multiparametric flow cytometry (MFC) represents a rapid, highly reproducible, and sensitive diagnostic technology for primary immunodeficiencies (PIDs), which are characterized by a wide range of T cell perturbations and a broad clinical and genetic heterogeneity. MFC data from CD4+ and CD8+ T cell subsets were examined in 100 patients referred for Primary Immunodeficiencies to our center. Naïve, central memory, effector memory, and terminal effector memory cell differentiation stages were defined by the combined expression CD45RA/CD27 for CD4 and CD45RA/CCR7 for CD8. Principal component analysis (PCA), a non-hypothesis driven statistical analysis, was applied to analyze MFC data in order to distinguish the diverse PIDs. Among severe lymphopenic patients, those affected by severe combined and combined immunodeficiency (SCID and CID) segregated in a specific area, reflecting a homogenous, and a more severe T cell impairment, compared to other lymphopenic PID, such as thymectomized and partial DiGeorge syndrome patients. PID patients with predominantly antibody defects were distributed in a heterogeneous pattern, but unexpectedly PCA was able to cluster some patients' resembling CID, hence warning for additional and more extensive diagnostic tests and a diverse clinical management. In conclusion, PCA applied to T cell MFC data might help the physician to estimate the severity of specific PID and to diversify the clinical and diagnostic approach of the patients.

Acute Leukemia with Lineage Infidelity: Mixed Phenotype AML Exhibits a Distinct Immunophenotype with Clinical Features Overlapping Mixed Phenotype Acute Leukemia

Acute Leukemia with Lineage Infidelity: Mixed Phenotype AML Exhibits a Distinct Immunophenotype with Clinical Features Overlapping Mixed Phenotype Acute Leukemia