Robust prediction of clinical outcomes using cytometry data.

MotivationFlow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction.ResultsWe propose a novel strategy (CytoDx) to predict clinical outcomes using cytometry data without cell gating. Applying CytoDx on real-world datasets allow us to predict multiple types of clinical features. In particular, CytoDx is able to predict the response to influenza vaccine using highly heterogeneous datasets, demonstrating that it is not only accurate but also robust to batch effects and cytometry platforms.Availability and implementationCytoDx is available as an R package on Bioconductor (bioconductor.org/packages/CytoDx). Data and scripts for reproducing the results are available on bitbucket.org/zichenghu_ucsf/cytodx_study_code/downloads.Supplementary informationSupplementary data are available at Bioinformatics online

From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of ‘‘supercell statistics’’, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behc¸et’s disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behc¸et’s disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.Fil: Candia, Julian Marcelo. University of Maryland; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; ArgentinaFil: Maunu, Ryan. University of Maryland; Estados UnidosFil: Driscoll, Meghan. University of Maryland; Estados UnidosFil: Biancotto, Angélique. National Institutes of Health; Estados UnidosFil: Dagur, Pradeep. National Institutes of Health; Estados UnidosFil: McCoy Jr., J Philip. National Institutes of Health; Estados UnidosFil: Nida Sen, H.. National Institutes of Health; Estados UnidosFil: Wei, Lai. National Institutes of Health; Estados UnidosFil: Maritan, Amos. Università di Padova; ItaliaFil: Cao, Kan. University of Maryland; Estados UnidosFil: Nussenblatt, Robert B. National Institutes of Health; Estados UnidosFil: Banavar, Jayanth R.. University of Maryland; Estados UnidosFil: Losert, Wolfgang. University of Maryland; Estados Unido

Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma.

Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. The Ivy Foundation Early Phase Clinical Trials Consortium conducted a randomized, multi-institution clinical trial to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone. Neoadjuvant PD-1 blockade was associated with upregulation of T cell- and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhances both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Beneficial effects of reconstituted high-density lipoprotein (rHDL) on circulating CD34+ cells in patients after an acute coronary syndrome

Background: High-density lipoproteins (HDL) favorably affect endothelial progenitor cells (EPC). Circulating progenitor cell level and function are impaired in patients with acute coronary syndrome (ACS). This study investigates the short-term effects of reconstituted HDL (rHDL) on circulating progenitor cells in patients with ACS. Methods and Findings: The study population consisted of 33 patients with recent ACS: 20 patients from the ERASE trial (randomized to receive 4 weekly intravenous infusions of CSL-111 40 mg/kg or placebo) and 13 additional patients recruited as controls using the same enrolment criteria. Blood was collected from 16 rHDL (CSL-111)-treated patients and 17 controls at baseline and at 6–7 weeks (i.e. 2–3 weeks after the fourth infusion of CSL-111 in ERASE). CD34+ and CD34+/kinase insert domain receptor (KDR+) progenitor cell counts were analyzed by flow cytometry. We found preserved CD34+ cell counts in CSL-111-treated subjects at follow-up (change of 1.6%), while the number of CD34+ cells was reduced (-32.9%) in controls (p = 0.017 between groups). The level of circulating SDF-1 (stromal cell-derived factor-1), a chemokine involved in progenitor cell recruitment, increased significantly (change of 21.5%) in controls, while it remained unchanged in CSL-111-treated patients (p = 0.031 between groups). In vitro exposure to CSL-111 of early EPC isolated from healthy volunteers significantly increased CD34+ cells, reduced early EPC apoptosis and enhanced their migration capacity towards SDF-1. Conclusions: The relative increase in circulating CD34+ cells and the low SDF-1 levels observed following rHDL infusions in ACS patients point towards a role of rHDL in cardiovascular repair mechanisms

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology

Automated haematology analysis to diagnose malaria

© 2010 licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.For more than a decade, flow cytometry-based automated haematology analysers have been studied for malaria diagnosis. Although current haematology analysers are not specifically designed to detect malaria-related abnormalities, most studies have found sensitivities that comply with WHO malaria-diagnostic guidelines, i.e. ≥ 95% in samples with > 100 parasites/μl. Establishing a correct and early malaria diagnosis is a prerequisite for an adequate treatment and to minimizing adverse outcomes. Expert light microscopy remains the 'gold standard' for malaria diagnosis in most clinical settings. However, it requires an explicit request from clinicians and has variable accuracy. Malaria diagnosis with flow cytometry-based haematology analysers could become an important adjuvant diagnostic tool in the routine laboratory work-up of febrile patients in or returning from malaria-endemic regions. Haematology analysers so far studied for malaria diagnosis are the Cell-Dyn®, Coulter® GEN•S and LH 750, and the Sysmex XE-2100® analysers. For Cell-Dyn analysers, abnormal depolarization events mainly in the lobularity/granularity and other scatter-plots, and various reticulocyte abnormalities have shown overall sensitivities and specificities of 49% to 97% and 61% to 100%, respectively. For the Coulter analysers, a 'malaria factor' using the monocyte and lymphocyte size standard deviations obtained by impedance detection has shown overall sensitivities and specificities of 82% to 98% and 72% to 94%, respectively. For the XE-2100, abnormal patterns in the DIFF, WBC/BASO, and RET-EXT scatter-plots, and pseudoeosinophilia and other abnormal haematological variables have been described, and multivariate diagnostic models have been designed with overall sensitivities and specificities of 86% to 97% and 81% to 98%, respectively. The accuracy for malaria diagnosis may vary according to species, parasite load, immunity and clinical context where the method is applied. Future developments in new haematology analysers such as considerably simplified, robust and inexpensive devices for malaria detection fitted with an automatically generated alert could improve the detection capacity of these instruments and potentially expand their clinical utility in malaria diagnosis

An Anti-C1s Monoclonal, TNT003, Inhibits Complement Activation Induced by Antibodies Against HLA.

Antibody-mediated rejection (AMR) of solid organ transplants (SOT) is characterized by damage triggered by donor-specific antibodies (DSA) binding donor Class I and II HLA (HLA-I and HLA-II) expressed on endothelial cells. While F(ab')2 portions of DSA cause cellular activation and proliferation, Fc regions activate the classical complement cascade, resulting in complement deposition and leukocyte recruitment, both hallmark features of AMR. We characterized the ability of an anti-C1s monoclonal antibody, TNT003, to inhibit HLA antibody (HLA-Ab)-induced complement activation. Complement deposition induced by HLA-Ab was evaluated using novel cell- and bead-based assays. Human aortic endothelial cells (HAEC) were cultured with HLA-Ab and human complement; production of activated complement proteins was measured by flow cytometry. Additionally, C3d deposition was measured on single antigen beads (SAB) mixed with HLA-Ab and human complement. TNT003 inhibited HLA-Ab mediated complement deposition on HAEC in a concentration-dependent manner; C3a, C4a and C5a anaphylatoxin production was also diminished by TNT003. Finally, TNT003 blocked C3d deposition induced by Class I (HLAI-Ab)- and Class II (HLAII-Ab)-specific antibodies on SAB. These data suggest TNT003 may be useful for modulating the effects of DSA, as TNT003 inhibits complement deposition and split product formation generated by HLA-I/II-Ab in vitro

Early PREdiction of Severe Sepsis (ExPRES-Sepsis) study: protocol for an observational derivation study to discover potential leucocyte cell surface biomarkers.

INTRODUCTION: Sepsis is an acute illness resulting from infection and the host immune response. Early identification of individuals at risk of developing life-threatening severe sepsis could enable early triage and treatment, and improve outcomes. Currently available biomarkers have poor predictive value for predicting subsequent clinical course in patients with suspected infection. Circulating leucocytes provide readily accessible tissues that reflect many aspects of the complex immune responses described in sepsis. We hypothesise that measuring cellular markers of immune responses by flow cytometry will enable early identification of infected patients at risk of adverse outcomes. We aim to characterise leucocyte surface markers (biomarkers) and their abnormalities in a population of patients presenting to the hospital emergency department with suspected sepsis, and explore their ability to predict subsequent clinical course. METHODS AND ANALYSIS: We will conduct a prospective, multicentre, clinical, exploratory, cohort observational study. To answer our study question, 3 patient populations will be studied. First, patients with suspected sepsis from the emergency department (n=300). To assess performance characteristics of potential tests, critically ill patients with established sepsis, and age and gender matched patients without suspicion of infection requiring hospital admission (both n=100) will be recruited as comparator populations. In all 3 groups, we plan to assess circulating biomarker profiles using flow cytometry. We will select candidate biomarkers by cross-cohort comparison, and then explore their predictive value for clinical outcomes within the cohort with suspected sepsis. ETHICS AND DISSEMINATION: The study will be carried out based on the principles in the Declaration of Helsinki and the International Conference on Harmonisation Good Clinical Practice. Ethics approval has been granted from the Scotland A Research Ethics Committee (REC) and Oxford C REC. On conclusion of this study, the results will be disseminated via peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT02188992; Pre-results