Identification Of Subset Research Articles

A simplified method for separating renal MPCs using SLAMF9.

Mononuclear phagocytes comprise an array of tissue-resident and monocyte-derived cells with important roles in tissue homeostasis and resistance to infection. Their diverse phenotypes make functional characterization within tissues challenging, because multiple surface markers are typically required for subset identification and isolation by cell sorting methods. Analysis of SLAMF9 expression within renal mononuclear phagocyte populations by multi-parametric flow cytometry indicates that SLAMF9 is a specific marker for identification of kidney-resident CD45+ CD11c+ MHC-II+ cells corresponding to prominent tissue-resident MPC populations derived from dendritic cell progenitors in adult mice. High SLAMF9 expression was sufficient to identify and sort these cells from disaggregated tissue using a user-operated cell sorter. The population can be further subdivided according to expression of CD11b and CD14 to identify IRF8high cDC1 cells and cleanly separate the CD11bhigh F4/80low and CD11bint F4/80high CD11c+ MPC subsets. Therefore, SLAMF9 expression allows for the identification and sorting of kidney-resident CD11b+ CD11c+ CD64+ F4/80+ CX3 CR1+ MHC-II+ MPCs without the need for complex antibody panels or reporter mice, simplifying isolation of these cells for study ex vivo.

Fibroblast pathology in inflammatory joint disease.

Rheumatoid arthritis is an immune-mediated inflammatory disease in which fibroblasts contribute to both joint damage and inflammation. Fibroblasts are a major cell constituent of the lining of the joint cavity called the synovial membrane. Under resting conditions, fibroblasts have an important role in maintaining joint homeostasis, producing extracellular matrix and joint lubricants. In contrast, during joint inflammation, fibroblasts contribute to disease pathology by producing pathogenic levels of inflammatory mediators that drive the recruitment and retention of inflammatory cells within the joint. Recent advances in single-cell profiling techniques have transformed our ability to examine fibroblast biology, leading to the identification of specific fibroblast subsets, defining a previously underappreciated heterogeneity of disease-associated fibroblast populations. These studies are challenging the previously held dogma that fibroblasts are homogeneous and are providing unique insights into their role in inflammatory joint pathology. In this review, we discuss the recent advances in our understanding of how fibroblast heterogeneity contributes to joint pathology in rheumatoid arthritis. Finally, we address how these insights could lead to the development of novel therapies that directly target selective populations of fibroblasts in the future.

Safety of Patients with HepatitisC Virus Treated with Glecaprevir/Pibrentasvir from Clinical Trials and Real-World Cohorts.

IntroductionMore than 70 million people are estimated to be infected with hepatitis C virus (HCV) globally. If left untreated, HCV infection can lead to complications such as extensive liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Evolution of treatments has resulted in highly effective and well-tolerated all-oral direct-acting antivirals. The pangenotypic regimen of glecaprevir/pibrentasvir is approved for treating HCV for patients without cirrhosis or with compensated cirrhosis (CC). Guidelines have evolved to simplify treatment to enable non-specialists to manage and treat HCV-infected patients. Simultaneously, such treatment algorithms provide guidance on the pretreatment identification of small subsets of patients who may require specialist treatment and long-term follow-up for advanced liver disease, including those at risk of developing HCC. This study describes the safety profile of glecaprevir/pibrentasvir in patients identified using previously described noninvasive laboratory measures who may be eligible for treatment by non-liver specialists.MethodsThis post hoc analysis of glecaprevir/pibrentasvir in patients, identified by noninvasive laboratory measures, intended to exclude patients with advanced liver disease and severe renal impairment, who can be managed within non-liver specialist settings. Patients were included from clinical trials and real-world studies of glecaprevir/pibrentasvir for HCV treatment. Baseline demographics, clinical characteristics, and safety assessments, including adverse events and laboratory abnormalities, were summarized.ResultsData across these large-scale studies confirm that glecaprevir/pibrentasvir is well tolerated across different patient populations, with fewer than 0.1% of patients experiencing a serious adverse event related to treatment drugs, and few patients developing HCC during or after treatment.ConclusionThe safety profile of glecaprevir/pibrentasvir enhances the confidence of non-liver specialists to treat the majority of HCV-infected patients, and provides an opportunity to expand the treater pool, potentially increasing diagnosis and treatment rates for HCV, contributing to elimination of HCV.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12325-021-01753-3.

Targeted gene expression classifier identifies pediatric T-cell acute lymphoblastic leukemia (T-ALL) patients at high risk for end induction minimal residual disease positivity.

10002 Background: The heterogeneity of T-ALL has hindered biomarker identification and limited biology-based risk stratification. Historically, minimal residual disease (MRD) has been the strongest predictor of poor outcomes. However, stratification by MRD does not allow for risk-adapted therapy early in treatment, which may induce deeper remissions and decrease risk of relapse. We hypothesized that gene expression profiling at diagnosis may have prognostic value in identifying high risk patients. Methods: We analyzed RNA-seq data from 189 diagnostic samples from the Children’s Oncology Group (COG) AALL0434 trial. Using leave-one-out cross-validation, we identified a set of genes that optimally differentiated MRD+ and MRD- samples. We then derived a risk score (RS) that indicates a probability of being MRD+ for a given gene expression pattern. Finally, we validated this model in an independent cohort of COG AALL1231 samples. Results: The AALL0434 early T-cell precursor (ETP) samples (n = 19), which have high rates of MRD+, had the highest RS, with an average of 81.3 (SD 18.7), versus 24.9 (SD 22.7) for non-ETPs (n = 146). Intriguingly, non-ETPs with RS > 50 had a gene expression pattern that mirrored ETPs and was distinct from the remaining non-ETPs. In this RS > 50 non-ETP cohort, 80% were MRD+, versus 20% of the < 50 cohort (p < 0.0001). When applied to 31 diagnostic non-ETP samples from COG AALL1231, 57% of the RS > 50 cohort were MRD+, versus 17% of the RS < 50 cohort (p = 0.05). Importantly, AALL0434 used prednisone during induction, while AALL1231 used dexamethasone, indicating that the predictive value is independent of the induction steroid. Finally, we converted our model to the customizable Nanostring nCounter platform by analyzing 96 AALL0434 samples on the Nanostring assay. The Nanostring data closely recapitulated the RNA-seq data, with a tight correlation between the resulting RS (concordance correlation coefficient = 0.91). Conclusions: We have developed a gene expression classifier that differentiates a subset of non-ETP T-ALLs with an ETP-like gene expression pattern and a high risk of MRD+, and have adapted the classifier to a clinically tractable targeted platform. Identification of this high-risk subset at diagnosis has the potential to facilitate risk-adapted trials to evaluate the utility of novel or more intensive therapies aimed at improving clinical outcomes.

Identification of the Most Important Subset of Doppler, Laboratory, and Clinical Parameters for Serial TIPS Evaluation.

OBJECTIVE. The purpose of the present study was to identify the subset of a wide range of serial Doppler, laboratory, and clinical parameters most predictive (both individually and in combination) of TIPS dysfunction in a large patient sample. MATERIALS AND METHODS. The medical records of 189 patients who had undergone TIPS procedures were analyzed. The patients (mean age, 52 years; 62% of whom were men) had undergone 1139 Doppler studies and 323 portovenograms. Laboratory parameters included model for end-stage liver disease (MELD) scores, serum albumin levels, presence of ascites, and time since last intervention. Doppler parameters included intrashunt velocities, temporal change in intrashunt velocities, main portal vein velocity, direction of flow in the left portal hepatic vein, and venous pulsatility index. Statistical analysis used ROC, univariate, and multivariate regression models to assess the parameters both individually and in combination. Shunt dysfunction was defined by a portosystemic gradient of more than 12 mm Hg. RESULTS. The laboratory and clinical parameters of greatest predictive value included the MELD score and the time since the last intervention. The Doppler parameters that were of greatest predictive value included the change in velocity at the hepatic venous end and the left portal vein flow direction. Multivariate models produced an AUC of 0.74. Differences between functional and dysfunctional shunts were also statistically significant for absolute velocity at the hepatic venous end, the change in velocity within the stent, and the temporal change in the mid shunt velocity. CONCLUSION. The subset of serial parameters most predictive of TIPS dysfunction are the temporal change in the velocity at the hepatic venous end, the absolute velocity at the hepatic venous end, the direction of flow in the left portal venous branch, and changes in the MELD score.

Development of a dried pre-formulated antibody panel (7 colors/17antibodies) for the identification of human innate lymphoid cells by flow cytometry

Abstract Innate lymphoid cells (ILC) play an important role in innate immunity, lymphoid organogenesis and tissue remodeling. They form a heterogeneous population that can be categorized into 3 groups (i.e. ILC1, ILC2 and ILC3) based on their phenotype and/or distinct patterns of cytokine production. The identification and enumeration of different blood ILC subsets is important to understand immune regulation in disease models such as allergy, cancer and inflammation. Flow Cytometry is the method of choice for analysis of ILCs in human blood. However, as ILC frequencies may range below 0.1% of white blood cells, considerably elevated sample volumes and/or cell-enrichment is required. Furthermore, due to the lack of specific markers for definition of ILCs, at least 15 antibodies are required for mere identification of ILCs, including a lineage-exclusion channel to exclude most of the leukocytes. Based on current knowledge in human ILC biology, we have composed a respective set of markers to design a dry unitized 17 antibodies/7-color panel (CD294/Lineage/CD117/Nkp46/CD127/CD161/CD45) for flow cytometry. The panel in dry DURAClone format allows for the reliable identification of all ILCs subsets in whole blood or peripheral blood mononuclear cell fractions (PBMCs) in concordance with current scientific opinion. Due to the elimination of the pipetting of 17 antibodies, the method can substantially reduce variability and error related to sample preparation. Furthermore, the streamlined workflow can facilitate inter-laboratory transfer of the assays in multi-site studies.

Immunophenotypic and functional characterization of circulating cytotoxic T lymphocytes and natural killer cells using the BD FACSymphony™ A1 Cell Analyzer

Abstract Cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells have become attractive therapeutic targets for cancer immunotherapy because of their efficient mechanisms for killing cancerous cells. Both effector CTLs and mature NK cells are endowed with secretory granules containing pre-formed cytolytic proteins that, once released at the immunological synapse, initiate a cascade of reactions leading to the apoptosis of the target cell. CTLs and NK cells may also promote cell killing through death-receptor-mediated and cytokine-induced cell death mechanisms. Here we show a 16-color flow cytometry panel allowing clear identification of CTLs, NK cells and related cell subsets in whole blood. We also describe an optimized intracellular staining protocol for analysis of granules content, including granzyme B, granzyme K and perforin. Optimal resolution of cell populations was achieved by employing a selection of BD Horizon Brilliant™ Reagents and BD Horizon™ Red 718 Reagent, a new small molecule fluorochrome option for the red laser. Cell fixation and permeabilization were carried out directly in whole blood using the BD Phosflow™ Reagents, adding convenience to the workflow and practical analysis of cell frequencies. The data also showed the expression of selected activation and inhibitory surface receptors. These results combined with the analysis of intracellular proteins contribute to characterization of the functional status of cytotoxic cells. Class 1 Laser Product. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD 25443 (v1.0) 0121 BD, the BD Logo, FACSymphony, Horizon, Horizon Brilliant and Phosflow are trademarks of Becton, Dickinson and Company or its affiliates. © 2021 BD. All rights reserved.

Reconciling the Biological and Transcriptional Variability of Hepatoblastoma with Its Mutational Uniformity.

Simple SummaryHepatoblastoma (HB), the most common form of childhood liver cancer, is associated with dual mutation and/or dysregulation of the Wnt/β-catenin and Hippo pathways in ~50% of cases. However, this mutational simplicity cannot explain HB’s biological and histologic diversity. This discussion focuses upon recent work showing that specific β-catenin mutants are key determinants of this HB variability as well as their metabolic and transcriptional signatures. Dysregulation of the anti-oxidant NFE2L2 pathway also contributes to tumorigenesis by being directly transforming in association with either of the other two factors. The transcriptional overlap of tumors generated by pairs of factors identifies crucial targets that likely mediate HB tumorigenesis, behavior and appearance.Hepatoblastoma (HB), the most common childhood liver cancer, is associated with seven distinct histologic subtypes and variable degrees of clinical aggressiveness and presentation. Yet it is among the least genomically altered tumors known, with about half of HBs showing mutation and/or dysregulation of the Wnt/β-catenin and Hippo pathways. This raises the question of how this mutational simplicity can generate such biological and histologic complexity. Recent work shows that the identity of the underlying β-catenin mutation is a major contributor. Mutation or over-expression of the NFE2L2/NRF2 transcription factor, previously thought only to promote anti-oxidant responses, has also recently been shown to accelerate the growth of HBs generated by mutations in the Wnt/β-catenin and Hippo pathways while imparting novel features such as the tumor-associated cysts and necrosis. Moreover, patient-associated NFE2L2 mutations are overtly transforming when co-expressed with either mutant β-catenin or a Hippo pathway effector. The finding that tumorigenesis can be driven by any two arms of the β-catenin/Hippo/NFE2L2 axis has permitted the identification of a small subset of coordinately regulated tumor-specific transcripts, some of whose levels correlate with inferior long-term outcomes in HB and other cancers. Collectively, these findings begin to provide for more refined and molecularly based classification, survival algorithms and design of chemotherapeutic regimens.

Identification of a novel subset of alveolar type 2 cells enriched in PD-L1 and expanded following pneumonectomy.

Alveolar type 2 (AT2) cells are heterogeneous cells, with specialised AT2 subpopulations within this lineage exhibiting stem cell properties. However, the existence of quiescent, immature cells within the AT2 lineage that are activated during lung regeneration is unknown.SftpcCreERT2/+;tdTomatoflox/flox mice were used for the labelling of AT2 cells and labelled subpopulations were analysed by flow cytometry, quantitative PCR, assay for transposase-accessible chromatin using sequencing (ATAC-seq), gene arrays, pneumonectomy and culture of precision-cut lung slices. Single-cell RNA-sequencing (scRNA-seq) data from human lungs were analysed.In mice, we detected two distinct AT2 subpopulations, with low tdTomato level (TomLow) and high tdTomato level (TomHigh). TomLow cells express lower levels of the AT2 differentiation markers Fgfr2b and Etv5, while TomHigh, as bona fide mature AT2 cells, show higher levels of Sftpc, Sftpb, Sftpa1, Fgfr2b and Etv5 expression. ATAC-seq analysis indicates that TomLow and TomHigh cells constitute two distinct cell populations, with specific silencing of Sftpc, Rosa26 and cell cycle gene loci in the TomLow population. Upon pneumonectomy, the number of TomLow but not TomHigh cells increases and TomLow cells show upregulated expression of Fgfr2b, Etv5, Sftpc, Ccnd1 and Ccnd2 compared to Sham. TomLow cells overexpress programmed cell death 1 ligand 1 (PD-L1), an immune inhibitory membrane receptor ligand, which is used by flow cytometry to differentially isolate these two subpopulations. In the human lung, data mining of a recent scRNA-seq AT2 data set demonstrates the existence of a PD-L1Pos population. Therefore, we have identified a novel population of AT2 quiescent, immature progenitor cells in mouse that expand upon pneumonectomy and we have provided evidence for the existence of such cells in human.

Understanding the Heterogeneity of Human Pericyte Subsets in Blood–Brain Barrier Homeostasis and Neurological Diseases

Pericytes are increasingly recognized as being important in the control of blood–brain barrier permeability and vascular flow. Research on this important cell type has been hindered by widespread confusion regarding the phenotypic identity and nomenclature of pericytes and other perivascular cell types. In addition, pericyte heterogeneity and mouse–human species differences have contributed to confusion. Herein we summarize our present knowledge on the identification of pericytes and pericyte subsets in humans, primarily focusing on recent findings in humans and nonhuman primates. Precise identification and definition of pericytes and pericyte subsets in humans may help us to better understand pericyte biology and develop new therapeutic approaches specifically targeting disease-associated pericyte subsets.

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems.

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.

Intestinal eosinophils: multifaceted roles in tissue homeostasis and disease.

Intestinal eosinophils are largely considered to be one of the central immune effector cells during helminth infection and disorders such as eosinophilic oesophagitis and food allergies. Given the abundance of these cells present in the gastrointestinal tract at homeostasis, emerging studies now reveal novel roles for eosinophils in the development and regulation of immunity, and during tissue repair. In addition, the identification of distinct eosinophil subsets indicates that we must consider the heterogeneity of these cells and how they differentially participate in mucosal immunity at steady state and during disease. Here, we summarise the literature on intestinal eosinophils, and how they contribute to mucosal homeostasis through immune regulation and interactions with the microbiome. We then explore the divergent roles of eosinophils in the context of eosinophilic gastrointestinal disorders and during helminth infection, whereby we discuss key observations and differences that have emerged from animal models and human studies. Lastly, we consider the possible interactions of eosinophils with the enteric nervous system, and how this represents an exciting area for future research which may inform future therapeutic targets.

Identification of an Immature Subset of PMN-MDSC Correlated to Response to Checkpoint Inhibitor Therapy in Patients with Metastatic Melanoma.

Simple SummaryPolymorphonuclear myeloid-derived suppressive cells (PMN-MDSCs) have been associated to bad prognosis and resistance to immune checkpoint inhibitor (ICI) therapy in metastatic melanoma (MM). In this study, we describe an immature subset of PMN-MDSCs capable of a high cytotoxicity against T cells mediated by a MAC-1 interaction, characterized by the absence of expression of the signal regulatory protein alpha. This subset is increased in patients responding to ICI therapy. Although the processes involving these cells in vivo are unknown, low-density CD15+SIRPα− cells might constitute a useful biomarker to monitor clinical response in MM patients.PMN-MDSCs support tumor progression and resistance to ICI therapy through their suppressive functions but their heterogeneity limits their use as biomarkers in cancer. Our aim was to investigate the phenotypic and functional subsets of PMN-MDSCs to identify biomarkers of response to ICI therapy. We isolated low-density CD15+ PMNs from patients with metastatic melanoma and assessed their immune-suppressive capacities. Expression of CD10 and CD16 was used to identify mature and immature subsets and correlate them to inhibition of T cell proliferation or direct cytotoxicity. Frequencies of the PMN-MDSCs subsets were next correlated to the radiological response of 36 patients receiving ICI therapy. Mature activated cells constituted the major population of PMN-MDSCs. They were found in a higher proportion in the pre-treatment blood of patients non responders to ICI. A subset of immature cells characterized by intermediate levels of CD10 and CD16, the absence of expression of SIRPα and a strong direct cytotoxicity to T cells was increased in patients responding to ICI. The paradoxical expansion of such cells during ICI therapy suggests a role of PMNs in the inflammatory events associated to efficient ICI therapy and the usefulness of their monitoring in patients care.

Procedure for the identification of multiple influential observations in block design for incomplete multi-response experiments in presence of masking

In agricultural experiments, if outliers are present in a data set, inference of experiment may be reversed. The purpose of this article is to develop a method for detection of subset of outlier vectors in block designs for incomplete multiresponse experiments in presence of masking. We defined an influence matrix comprising of Cook-statistics in its diagonal and product of two Cook-statistics in its off-diagonal positions. We then obtained eigenvectors corresponding to large eigenvalues of this matrix which can be used for identification of influential subsets. The proposed procedure has been illustrated with a real life data set.

Deciphering trophic interactions in a mid-Cambrian assemblage

SummaryExceptionally preserved fossil sites have allowed specimen-based identification of trophic interactions to which network analyses have been applied. However, network analyses of the fossil record suffer from incomplete and indirect data, time averaging that obscures species coexistence, and biases in preservation. Here, we present a high-resolution fossil data set from Raymond Quarry member of the mid-Cambrian Burgess Shale (7,549 specimens, 61 taxa, ∼510 Mya) and formulate a measure of “preservation bias” that aids identification of assemblage subsets to which network analyses can be reliably applied. For these sections, abundance correlation network analyses predicted longitudinally consistent trophic and competitive interactions. Our analyses predicted previously postulated trophic interactions with 83.5% accuracy and demonstrated a shift from specialist interaction-dominated assemblages to ones dominated by generalist and competitive interactions. This approach provides a robust, taphonomically corrected framework to explore and predict in detail the existence and ecological character of putative interactions in fossil data sets.

30432 Novel insights from single-cell RNAseq analysis of the stromavascular fraction of human adipose tissue

ABSTRACT IMPACT: Characterizing the cellular composition of human adipose tissue may contribute to the prevention and/or treatment of obesity-associated metabolic diseases. OBJECTIVES/GOALS: Our aims in this study were to use single-cell techniques 1. to characterize cell types within the stromavascular fraction of human adipose tissue, 2. to identify subsets of cells within each type (sub-clustering), 3. to identify gene sets and pathways that may provide information on the function and significance of each cell cluster. METHODS/STUDY POPULATION: Abdominal subcutaneous adipose tissue samples from n=6 healthy volunteers (1M, 5F, age 28-38 y, BMI 24.5-63.0 kg/m2) were collected by aspiration or during surgery. In 3 subjects, all females, paired femoral samples were also collected. After collagenase digestion approximately n=10,000 cells/sample were used for single-cell RNA sequencing using the 10X Genomics platform. After QC and downstream analysis, data were analyzed in Seurat v.3.1.5. We identified first different cell types and then subclusters in an unbiased fashion. Gene Set Enrichment Analysis (GSEA) was used for pathway analysis. RESULTS/ANTICIPATED RESULTS: Progenitor markers are related to extracellular matrix, eg DCN (logFC progenitors vs other cells types=3.17, expressed in 99.7% (pct.1) of progenitors, 26.1% (pct.2) of others). Endothelial and pericytes shared markers like RBP7 (logFC=1.67, pct.1 0.88, pct.2 0.39); pericytes also showed unique markers, eg RGS5 (logFC=2.29, pct.1 0.89, pct.2 0.17). Progenitors are further divided into 11 sub-clusters, one of which showed enrichment of CD36 (high proliferation potential), FABP4 (differentiation), and of the novel marker PALMD (logFC=7.13, pct.1 0.94, pct.2 0.48). All p<10E-5. GSEA analysis suggests that inflammatory pathways are downregulated in both adipose progenitors and endothelial/pericyte cells in the femoral compared to the abdominal depot. DISCUSSION/SIGNIFICANCE OF FINDINGS: Single cell RNA sequencing provides unique insights into the molecular profile of cell types and the identification of novel subsets of cells within the human adipose tissue. Such cellular heterogeneity may explain differences in adipose function between individuals and eventually in the risk of obesity-associated metabolic diseases.

The Level of Agreement Among Medical Oncologists on Adjuvant Chemotherapy Decision for Breast Cancer in Pre and Post-Oncotype DX Settings.

Introduction: The Oncotype DX assay plays an important role in the identification of the specific subset of hormone receptor (HR)-positive and node-negative breast cancer (BC) patients, who would benefit the most from adjuvant chemotherapy. The current study aimed at assessing the level of agreement among medical oncologists on adjuvant chemotherapy decisions before and after Oncotype DX, as well as the intra-observer agreement of each medical oncologist’s decision of prescribing adjuvant chemotherapy based on clinicopathological and immunohistochemical parameters only and followed by Oncotype DX recurrence score (RS) results.Methods: A retrospective analysis of data related to clinicopathological and immunohistochemical parameters, and Oncotype DX RS result for 145 female, estrogen receptor (ER)-positive, HER2 negative, and both node-negative and positive BC patients was performed. Initially, the data without Oncotype DX RS was sent to 16 oncologists in multiple centers in the Middle East. After one week, the same data with the shuffling of cases were sent to the oncologists with the addition of the Oncotype DX RS result for each patient. The inter and intra-observer agreement (kappa and Fleiss multi-rater kappa) among oncologists' decision of prescribing adjuvant chemotherapy pre and post-Oncotype DX RS results were assessed. Oncotype DX risk scores were used as continuous variables as well as based on old RS grouping, categorized into low (0-17), intermediate (18-30), and high risk (≥ 31) groups. A test with a p-value of < 0 .05 will be considered statistically significant.Results: The mean age ± SD of the cohort was 51.9 ± 9.4 years. Sixty-nine patients (47.6%) were premenopausal whereas 76 patients (52.4%) were postmenopausal. The mean Oncotype DX RS was 17.8 ± 8.6 and 54.5% had low recurrence risk (RR), 37.9% had intermediate RR and only 7.6% had high RR. The majority of our cases were grade two (53.1%) and T stage one (49%), whereas 29.7% had positive one to three lymph nodes. The addition of Oncotype DX results improved the agreement among oncologists' decision from fair to moderate (kappa = 0.52; p <0.001). On average, an oncologist’s decision of prescribing adjuvant chemotherapy pre and post-Oncotype DX had an agreement in 70.6% of the cases, with agreement observed mostly for cases where the initial decision of adjuvant chemotherapy was (no) and it was retained with post-Oncotype DX assay (46.1%), compared to 24.5% cases where the initial decision was (yes) and it was retained with post-Oncotype DX assay (kappa = 0.39; p <0.001). The addition of the Oncotype DX RS result avoided chemotherapy in 20.4% of cases and identified 9% of cases as candidates for adjuvant chemotherapy (kappa = 0.38; p <0.001). The disagreement was highest among cases with intermediate RR (33.6%) followed by high and low RR (31.3% and 21.6%) with a statistical significance of <0.001.Conclusion: We conclude that the Oncotype DX RS significantly influenced the decision to prescribe adjuvant chemotherapy among HR-positive, HER2 negative, and both node-negative and positive patients, as it increased the level of agreement among oncologists and led to a decrease in the use of adjuvant chemotherapy compared to the pre-Oncotype recommendations.

In Situ Characterization of Follicular Helper CD4 T Cells Using Multiplexed Imaging.

Follicular helper CD4 T (Tfh) cells play an essential role in the formation of germinal centers (GCs), where mature B cells proliferate, differentiate, and provide long-term protective humoral responses. Despite the extensive phenotypic characterization and identification of human Tfh cell subsets, their spatial positioning at tissue level is not well understood. Here, we describe a quantitative multiplexed immunofluorescence approach allowing for the comprehensive in situ characterization of Tfh cells in human tonsils and lymph nodes (LNs) from individuals with angioimmunoblastic T-cell lymphoma (AITL). We have developed eight multiplexed panels comprising a spectrum of Tfh cell markers, like PD-1, CXCR5, and ICOS, along with transcription factors (Bcl6, Tbet, GATA3), to assess their expression, frequencies, spatial distribution and co-localization in a quantitative manner. Combined analysis of relevant markers revealed the presence of several Tfh cell subsets at tissue level based on the differential expression of surface receptors, nuclear factors as well as their distinct localization within the follicular areas. Interestingly, we found a considerable amount of tonsillar Tfh cells expressing high levels of the Th2 regulator GATA3. The co-expression of GATA3, CXCR5, and BCL6, points to an important role of GATA3 for the generation of effector human Tfh cells. Furthermore, our data revealed significantly different Tfh cell profile signatures between health and disease. Therefore, our imaging platform generates meaningful information for the in situ characterization of human Tfh cells and could provide the base for future studies aiming to a comprehensive understanding of Tfh cell tissue heterogeneity.

WITHDRAWN: MMFA-SVM: New bio-marker gene discovery algorithms for cancer gene expression

Abstract Different Conventional machine learning algorithms like the gradient-based approach fail to achieve high accuracy with microarray datasets which are high dimensional and non-linear and fall with the local minima. To avoid these different metaheuristic approaches and nature-inspired swarm-based intelligent algorithms are adopted by various researchers for achieving global optima in the case of high dimensional datasets like the Microarray dataset. While the deal with microarray datasets as the size of the sample is very less as compare with feature size which is also directed towards the curse of dimensionality problem addition with various problems like redundancy, irrelevance, and noise. The extraction of significant biomarker genes is a vital task. This article proposes an innovative wrapper hybrid swarm intelligence approach called MMFA-SVM (Modified Mutated Firefly Algorithm – Support vector machine) for the identification of feature subsets to improve the accuracy of the proposed model. The proposed model works with two stages, in the first stage the enhancement of global convergence speed can be achieved using MMFA and in the second stage meta-search model is used with a well-known classifier SVM with LOOCV are adopted to calculate the accuracy of the biomarker feature subset. The search space exploration is done using MMFA with an enhancement approach of less glowing fireflies with more one. MMFA was used in this study with SVM to achieve optimal convergence time and a stochastic approach with the improvement of convergence time. The mutation-based firefly algorithm enhances the global search mobility of fireflies. We have compared the performance of the proposed one with FA, MMFA, MMFA-DT (decision tree), and MMFA-NB (Naive Bayes). From the simulation results, it confirms that MMFA-SVM performance is better as compared with normal FA, MMFA, MMFA-DT, and MMFA-NB with the microarray datasets. The efficiency of the proposed one performs better in comparison with its counterparts.

The optimal design of clinical trials with potential biomarker effects: A novel computational approach.

As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (eg, expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over two million hits by keyword searches on Google Scholar. However, designing clinical trials that utilize the discovered uncertain subsets/biomarkers is not trivial and rarely discussed in the literature. This leads to a gap between research results and real-world drug development. To fill in this gap, we formulate the problem of clinical trial design into an optimization problem involving high-dimensional integration, and propose a novel computational solution based on Monte Carlo and smoothing methods. Our method utilizes the modern techniques of general purpose computing on graphics processing units for large-scale parallel computing. Compared to a published method in three-dimensional problems, our approach is more accurate and 133 times faster. This advantage increases when dimensionality increases. Our method is scalable to higher dimensional problems since the precision bound of our estimated study power is a finite number not affected by dimensionality. To design clinical trials incorporating the potential biomarkers, users can use our software "DesignCTPB". This software can be found on Github and will be available as an R package on CRAN. Although our research is motivated by the design of clinical trials, the method can be used widely to solve other optimization problems involving high-dimensional integration.