Myelodysplastic neoplasms dissected into indolent, leukaemic and unfavourable subtypes by computational clustering of haematopoietic stem and progenitor cells.

Myelodysplastic neoplasms (MDS) encompass haematological malignancies, which are characterised by dysplasia, ineffective haematopoiesis and the risk of progression towards acute myeloid leukaemia (AML). Myelodysplastic neoplasms are notorious for their heterogeneity: clinical outcomes range from a near-normal life expectancy to leukaemic transformation or premature death due to cytopenia. The Molecular International Prognostic Scoring System made progress in the dissection of MDS by clinical outcomes. To contribute to the risk stratification of MDS by immunophenotypic profiles, this study performed computational clustering of flow cytometry data of CD34+ cells in 67 MDS, 67 AML patients and 49 controls. Our data revealed heterogeneity also within the MDS-derived CD34+ compartment. In MDS, maintenance of lymphoid progenitors and megakaryocytic-erythroid progenitors predicted favourable outcomes, whereas expansion of granulocyte-monocyte progenitors increased the risk of leukaemic transformation. The proliferation of haematopoietic stem cells and common myeloid progenitors with downregulated CD44 expression, suggestive of impaired haematopoietic differentiation, characterised a distinct MDS subtype with a poor overall survival. This exploratory study demonstrates the prognostic value of known and previously unexplored CD34+ populations and suggests the feasibility of dissecting MDS into a more indolent, a leukaemic and another unfavourable subtype.

A robust pipeline for high-content, high-throughput immunophenotyping reveals age- and genetics-dependent changes in blood leukocytes.

High-dimensional flow cytometry is the gold standard to study the human immune system in large cohorts. However, large sample sizes increase inter-experimental variation because of technical and experimental inaccuracies introduced by batch variability. Our high-throughput sample processing pipeline in combination with 28-color flow cytometry focuses on increased throughput (192 samples/experiment) and high reproducibility. We implemented quality control checkpoints to reduce technical and experimental variation. Finally, we integrated FlowSOM clustering to facilitate automated data analysis and demonstrate the reproducibility of our pipeline in a study with 3,357 samples. We reveal age-associated immune dynamics in 2,300 individuals, signified by decreasing T and B cell subsets with age. In addition, by combining genetic analyses, our approach revealed unique immune signatures associated with a single nucleotide polymorphism (SNP) that abrogates CD45 isoform splicing. In summary, we provide a versatile and reliable high-throughput, flow cytometry-based pipeline for immune discovery and exploration in large cohorts.

Adjuvant dendritic cell-based immunotherapy after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy in patients with malignant peritoneal mesothelioma: a phase II clinical trial.

BACKGROUND: Malignant peritoneal mesothelioma (MPM) is an aggressive malignancy with a poor prognosis. Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) improves survival outcomes, but recurrence rates remain high. Dendritic cell-based immunotherapy (DCBI) showed promising results in patients with pleural mesothelioma. The primary aim of this trial was to determine feasibility of adjuvant DCBI after CRS-HIPEC. METHODS: This open-label, single-center, phase II clinical trial, performed in the Erasmus MC Cancer Institute Rotterdam, the Netherlands, included patients with epithelioid MPM. 4-6 weeks before CRS-HIPEC leukapheresis was performed. 8-10 weeks after surgery, DCBI was administered three times biweekly. Feasibility was defined as administration of at least three adjuvant vaccinations in 75% of patients. Comprehensive immune cell profiling was performed on peripheral blood samples prior to and during treatment. RESULTS: All patients who received CRS-HIPEC (n=16) were successfully treated with adjuvant DCBI. No severe toxicity related to DCBI was observed. Median progression-free survival (PFS) was 12 months (IQR 5-23) and median overall survival was not reached. DCBI was associated with increased proliferation of circulating natural killer cells and CD4+ T-helper (Th) cells. Co-stimulatory molecules, including ICOS, HLA-DR, and CD28 were upregulated predominantly on memory or proliferating Th-cells and minimally on CD8+ cytotoxic T-lymphocytes (CTLs) after treatment. However, an increase in CD8+ terminally differentiated effector memory (Temra) cells positively correlated with PFS, whereas co-expression of ICOS and Ki67 on CTLs trended towards a positive correlation. CONCLUSIONS: Adjuvant DCBI after CRS-HIPEC in patients with MPM was feasible and safe, and showed promising survival outcomes. DCBI had an immune modulatory effect on lymphoid cells and induced memory T-cell activation. Moreover, an increase of CD8+ Temra cells was more pronounced in patients with longer PFS. These data provide rationale for future combination treatment strategies. TRIAL REGISTRATION NUMBER: NTR7060; Dutch Trial Register (NTR).

Gut Inflammation in Axial Spondyloarthritis Patients is Characterized by a Marked Type 17 Skewed Mucosal Innate-like T Cell Signature.

OBJECTIVE: Patients with spondyloarthritis (SpA) often present with microscopic signs of gut inflammation, a risk factor for progressive disease. We investigated whether mucosal innate-like T cells are involved in dysregulated interleukin-23 (IL-23)/IL-17 responses in the gut-joint axis in SpA. METHODS: Ileal and colonic intraepithelial lymphocytes (IELs), lamina propria lymphocytes (LPLs), and paired peripheral blood mononuclear cells (PBMCs) were isolated from treatment-naive patients with nonradiographic axial SpA with (n = 11) and without (n = 14) microscopic gut inflammation and healthy controls (n = 15) undergoing ileocolonoscopy. The presence of gut inflammation was assessed histopathologically. Immunophenotyping of innate-like T cells and conventional T cells was performed using intracellular flow cytometry. Unsupervised clustering analysis was done by FlowSOM technology. Serum IL-17A levels were measured via Luminex. RESULTS: Microscopic gut inflammation in nonradiographic axial SpA was characterized by increased ileal intraepithelial γδ-hi T cells, a γδ-T cell subset with elevated γδ-T cell receptor expression. γδ-hi T cells were also increased in PBMCs of patients with nonradiographic axial SpA versus healthy controls and were strongly associated with Ankylosing Spondylitis Disease Activity Score. The abundance of mucosal-associated invariant T cells and invariant natural killer T cells was unaltered. Innate-like T cells in the inflamed gut showed increased RORγt, IL-17A, and IL-22 levels with loss of T-bet, a signature that was less pronounced in conventional T cells. Presence of gut inflammation was associated with higher serum IL-17A levels. In patients treated with tumor necrosis factor blockade, the proportion of γδ-hi cells and RORγt expression in blood was completely restored. CONCLUSION: Intestinal innate-like T cells display marked type 17 skewing in the inflamed gut mucosa of patients with nonradiographic axial SpA. γδ-hi T cells are linked to intestinal inflammation and disease activity in SpA.

Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma.

BACKGROUND: Functional profiling of freshly isolated glioblastoma (GBM) cells is being evaluated as a next-generation method for precision oncology. While promising, its success largely depends on the method to evaluate treatment activity which requires sufficient resolution and specificity. METHODS: Here, we describe the 'precision oncology by single-cell profiling using ex vivo readouts of functionality' (PROSPERO) assay to evaluate the intrinsic susceptibility of high-grade brain tumor cells to respond to therapy. Different from other assays, PROSPERO extends beyond life/death screening by rapidly evaluating acute molecular drug responses at single-cell resolution. RESULTS: The PROSPERO assay was developed by correlating short-term single-cell molecular signatures using mass cytometry by time-of-flight (CyTOF) to long-term cytotoxicity readouts in representative patient-derived glioblastoma cell cultures (n = 14) that were exposed to radiotherapy and the small-molecule p53/MDM2 inhibitor AMG232. The predictive model was subsequently projected to evaluate drug activity in freshly resected GBM samples from patients (n = 34). Here, PROSPERO revealed an overall limited capacity of tumor cells to respond to therapy, as reflected by the inability to induce key molecular markers upon ex vivo treatment exposure, while retaining proliferative capacity, insights that were validated in patient-derived xenograft (PDX) models. This approach also allowed the investigation of cellular plasticity, which in PDCLs highlighted therapy-induced proneural-to-mesenchymal (PMT) transitions, while in patients' samples this was more heterogeneous. CONCLUSION: PROSPERO provides a precise way to evaluate therapy efficacy by measuring molecular drug responses using specific biomarker changes in freshly resected brain tumor samples, in addition to providing key functional insights in cellular behavior, which may ultimately complement standard, clinical biomarker evaluations.

Operational tolerance after hematopoietic stem cell transplantation is characterized by distinct transcriptional, phenotypic, and metabolic signatures.

The mechanisms underlying operational tolerance after hematopoietic stem cell transplantation in humans are poorly understood. We studied two independent cohorts of patients who underwent allogeneic hematopoietic stem cell transplantation from human leukocyte antigen-identical siblings. Primary tolerance was associated with long-lasting reshaping of the recipients' immune system compared to their healthy donors with an increased proportion of regulatory T cell subsets and decreased T cell activation, proliferation, and migration. Transcriptomics profiles also identified a role for nicotinamide adenine dinucleotide biosynthesis in the regulation of immune cell functions. We then compared individuals with operational tolerance and nontolerant recipients at the phenotypic, transcriptomic, and metabolomic level. We observed alterations centered on CD38+-activated T and B cells in nontolerant patients. In tolerant patients, cell subsets with regulatory functions were prominent. RNA sequencing analyses highlighted modifications in the tolerant patients' transcriptomic profiles, particularly with overexpression of the ectoenzyme NT5E (encoding CD73), which could counterbalance CD38 enzymatic functions by producing adenosine. Further, metabolomic analyses suggested a central role of androgens in establishing operational tolerance. These data were confirmed using an integrative approach to evaluating the immune landscape associated with operational tolerance. Thus, balance between a CD38-activated immune state and CD73-related production of adenosine may be a key regulator of operational tolerance.

Presence of innate lymphoid cells in allogeneic hematopoietic grafts correlates with reduced graft-versus-host disease.

BACKGROUND: Allogeneic hematopoietic cell transplantation (HCT) can be devastating when graft-versus-host disease (GvHD) develops. GvHD is characterized by mucosal inflammation due to breaching of epithelial barriers. Innate lymphoid cells (ILCs) are immune modulatory cells that are important in the maintenance of epithelial barriers, via their production of interleukin (IL)-22 and their T cell suppressive properties. After chemo- and radiotherapy, ILCs are depleted, and recovery after remission-induction therapy and after allogeneic HCT is slow and incomplete in a significant number of patients, which is associated with an increased risk to develop acute GvHD. OBJECTIVE: To investigate whether the presence of mature ILCs within G-CSF-mobilized HCT grafts is correlated with the development of acute GvHD after allogeneic HCT. STUDY DESIGN: We analyzed ILCs in a cohort of 36 patients who received allogeneic HCT for a hematologic malignancy, by flow-cytometric immune-phenotyping of prospectively collected, cryopreserved peripheral blood mononuclear cells (PBMCs) and donor-derived HCT grafts collected for the same patients. Biased analysis, with ILCs defined as CD3-lineage-CD45+CD127+CD161+ lymphocytes, was performed using FlowJo version 10 software. Unbiased analysis was done using FlowSOM, which uses a self-organizing map (SOM) with a minimal spanning tree (MST) to define and visualize different clusters present in the samples. RESULTS: Remission-induction therapy significantly depleted ILCs from the blood, and patients who had a relatively low percentage of ILCs before allogeneic HCT were significantly more prone to develop acute GvHD, confirming previous findings in a separate cohort. Allogeneic HCT grafts, which were all obtained from the blood of G-CSF-mobilized healthy donors, contained ILCs at a frequency very similar to the peripheral blood of healthy individuals. The ILC subset composition was also comparable to that of the blood of healthy individuals, with the exception of NKp44+ ILC3s, which were significantly more abundant in HCT grafts. The relative ILC content of the graft tended to correlate with ILC reconstitution after allogeneic HCT, suggesting that peripheral expansion of transplanted mature ILCs may contribute to early ILC reconstitution after allogeneic HCT. Patients who received a relatively ILC-poor HCT graft had a significantly increased risk to develop acute GvHD, compared with patients who received relatively ILC-rich allogeneic HCT grafts. Unbiased phenotypic analysis with the FlowSOM algorithm confirmed that allogeneic HCT grafts of patients who developed acute GvHD contained a lower frequency of ILCs that clustered in NKp44+ ILC3 signature groups. CONCLUSION: The presence of ILCs in allogeneic HCT grafts is associated with a reduced risk to develop acute GvHD. These data suggest that enhancement of ILC reconstitution of ILC3s in particular, for example via adoptive transfer of ILCs, may prevent acute GvHD and has the potential to improve outcome of allogeneic HCT recipients.

Immune Monitoring in Melanoma and Urothelial Cancer Patients Treated with Anti-PD-1 Immunotherapy and SBRT Discloses Tumor Specific Immune Signatures.

(1) Background: Blockade of the PD-1/PD-L1 pathway has revolutionized the oncology field in the last decade. However, the proportion of patients experiencing a durable response is still limited. In the current study, we performed an extensive immune monitoring in patients with stage III/IV melanoma and stage IV UC who received anti-PD-1 immunotherapy with SBRT. (2) Methods: In total 145 blood samples from 38 patients, collected at fixed time points before and during treatment, were phenotyped via high-parameter flow cytometry, luminex assay and UPLC-MS/MS. (3) Results: Baseline systemic immunity in melanoma and UC patients was different with a more prominent myeloid compartment and a higher neutrophil to lymphocyte ratio in UC. Proliferation (Ki67+) of CD8+ T-cells and of the PD-1+/PD-L1+ CD8+ subset at baseline correlated with progression free survival in melanoma. In contrast a higher frequency of PD-1/PD-L1 expressing non-proliferating (Ki67-) CD8+ and CD4+ T-cells before treatment was associated with worse outcome in melanoma. In UC, the expansion of Ki67+ CD8+ T-cells and of the PD-L1+ subset relative to tumor burden correlated with clinical outcome. (4) Conclusion: This study reveals a clearly different immune landscape in melanoma and UC at baseline, which may impact immunotherapy response. Signatures of proliferation in the CD8+ T-cell compartment prior to and early after anti-PD-1 initiation were positively correlated with clinical outcome in both cohorts. PD-1/PD-L1 expression on circulating immune cell subsets seems of clinical relevance in the melanoma cohort.

Analyzing high-dimensional cytometry data using FlowSOM.

The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. An average FlowSOM analysis takes 1-3 h to complete, though quality issues can increase this time considerably.

Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes.

The diagnostic work-up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected-MDS. The computational diagnostic workflow consists of methods for pre-processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient).

A Computational Pipeline for the Diagnosis of CVID Patients.

Common variable immunodeficiency (CVID) is one of the most frequently diagnosed primary antibody deficiencies (PADs), a group of disorders characterized by a decrease in one or more immunoglobulin (sub)classes and/or impaired antibody responses caused by inborn defects in B cells in the absence of other major immune defects. CVID patients suffer from recurrent infections and disease-related, non-infectious, complications such as autoimmune manifestations, lymphoproliferation, and malignancies. A timely diagnosis is essential for optimal follow-up and treatment. However, CVID is by definition a diagnosis of exclusion, thereby covering a heterogeneous patient population and making it difficult to establish a definite diagnosis. To aid the diagnosis of CVID patients, and distinguish them from other PADs, we developed an automated machine learning pipeline which performs automated diagnosis based on flow cytometric immunophenotyping. Using this pipeline, we analyzed the immunophenotypic profile in a pediatric and adult cohort of 28 patients with CVID, 23 patients with idiopathic primary hypogammaglobulinemia, 21 patients with IgG subclass deficiency, six patients with isolated IgA deficiency, one patient with isolated IgM deficiency, and 100 unrelated healthy controls. Flow cytometry analysis is traditionally done by manual identification of the cell populations of interest. Yet, this approach has severe limitations including subjectivity of the manual gating and bias toward known populations. To overcome these limitations, we here propose an automated computational flow cytometry pipeline that successfully distinguishes CVID phenotypes from other PADs and healthy controls. Compared to the traditional, manual analysis, our pipeline is fully automated, performing automated quality control and data pre-processing, automated population identification (gating) and deriving features from these populations to build a machine learning classifier to distinguish CVID from other PADs and healthy controls. This results in a more reproducible flow cytometry analysis, and improves the diagnosis compared to manual analysis: our pipelines achieve on average a balanced accuracy score of 0.93 (±0.07), whereas using the manually extracted populations, an averaged balanced accuracy score of 0.72 (±0.23) is achieved.

Myeloid cell heterogeneity in cancer: not a single cell alike.

Tumors of various histological origins show abundant infiltration of myeloid cells from early stages of disease progression. These cells have a profound impact on antitumor immunity and influence fundamental processes that underlie malignancy, including neoangiogenesis, sustained cancer cell proliferation, metastasis and therapy resistance. For these reasons, development of therapeutic approaches to deplete or reprogram myeloid cells in cancer is an emerging field of interest. However, knowledge about the heterogeneity of myeloid cells in tumors and their variability between patients and disease stages is still limited. In this review, we summarize the most recent advances in our understanding about how the phenotype of tumor-associated macrophages, monocytes, neutrophils, myeloid-derived suppressor cells and dendritic cells is dictated by their ontogeny, activation status and localization. We also outline major open questions that will only be resolved by applying high-dimensional single-cell technologies and systems biology approaches in the analysis of the tumor microenvironment.

FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.

The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor.