Engineering an inducible leukemia-associated fusion protein enables large-scale ex vivo production of functional human phagocytes.

Ex vivo expansion of human CD34+ hematopoietic stem and progenitor cells remains a challenge due to rapid differentiation after detachment from the bone marrow niche. In this study, we assessed the capacity of an inducible fusion protein to enable sustained ex vivo proliferation of hematopoietic precursors and their capacity to differentiate into functional phagocytes. We fused the coding sequences of an FK506-Binding Protein 12 (FKBP12)-derived destabilization domain (DD) to the myeloid/lymphoid lineage leukemia/eleven nineteen leukemia (MLL-ENL) fusion gene to generate the fusion protein DD-MLL-ENL and retrovirally expressed the protein switch in human CD34+ progenitors. Using Shield1, a chemical inhibitor of DD fusion protein degradation, we established large-scale and long-term expansion of late monocytic precursors. Upon Shield1 removal, the cells lost self-renewal capacity and spontaneously differentiated, even after 2.5 y of continuous ex vivo expansion. In the absence of Shield1, stimulation with IFN-γ, LPS, and GM-CSF triggered terminal differentiation. Gene expression analysis of the obtained phagocytes revealed marked similarity with naïve monocytes. In functional assays, the novel phagocytes migrated toward CCL2, attached to VCAM-1 under shear stress, produced reactive oxygen species, and engulfed bacterial particles, cellular particles, and apoptotic cells. Finally, we demonstrated Fcγ receptor recognition and phagocytosis of opsonized lymphoma cells in an antibody-dependent manner. Overall, we have established an engineered protein that, as a single factor, is useful for large-scale ex vivo production of human phagocytes. Such adjustable proteins have the potential to be applied as molecular tools to produce functional immune cells for experimental cell-based approaches.

What Affects the Quality of Score Transformations? Potential Issues in True-Score Equating Using the Partial Credit Model.

This simulation study investigated to what extent departures from construct similarity as well as differences in the difficulty and targeting of scales impact the score transformation when scales are equated by means of concurrent calibration using the partial credit model with a common person design. Practical implications of the simulation results are discussed with a focus on scale equating in health-related research settings. The study simulated data for two scales, varying the number of items and the sample sizes. The factor correlation between scales was used to operationalize construct similarity. Targeting of the scales was operationalized through increasing departure from equal difficulty and by varying the dispersion of the item and person parameters in each scale. The results show that low similarity between scales goes along with lower transformation precision. In cases with equal levels of similarity, precision improves in settings where the range of the item parameters is encompassing the person parameters range. With decreasing similarity, score transformation precision benefits more from good targeting. Difficulty shifts up to two logits somewhat increased the estimation bias but without affecting the transformation precision. The observed robustness against difficulty shifts supports the advantage of applying a true-score equating methods over identity equating, which was used as a naive baseline method for comparison. Finally, larger sample size did not improve the transformation precision in this study, longer scales improved only marginally the quality of the equating. The insights from the simulation study are used in a real-data example.

Interpretable machine learning for psychological research: Opportunities and pitfalls.

In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most machine learning methods are not directly interpretable. Interpretation techniques that support researchers in describing how the machine learning technique came to its prediction may be a means to this end. We present a variety of interpretation techniques and illustrate the opportunities they provide for interpreting the results of two widely used black box machine learning methods that serve as our examples: random forests and neural networks. At the same time, we illustrate potential pitfalls and risks of misinterpretation that may occur in certain data settings. We show in which way correlated predictors impact interpretations with regard to the relevance or shape of predictor effects and in which situations interaction effects may or may not be detected. We use simulated didactic examples throughout the article, as well as an empirical data set for illustrating an approach to objectify the interpretation of visualizations. We conclude that, when critically reflected, interpretable machine learning techniques may provide useful tools when describing complex psychological relationships. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A New Stopping Criterion for Rasch Trees Based on the Mantel-Haenszel Effect Size Measure for Differential Item Functioning.

To detect differential item functioning (DIF), Rasch trees search for optimal splitpoints in covariates and identify subgroups of respondents in a data-driven way. To determine whether and in which covariate a split should be performed, Rasch trees use statistical significance tests. Consequently, Rasch trees are more likely to label small DIF effects as significant in larger samples. This leads to larger trees, which split the sample into more subgroups. What would be more desirable is an approach that is driven more by effect size rather than sample size. In order to achieve this, we suggest to implement an additional stopping criterion: the popular Educational Testing Service (ETS) classification scheme based on the Mantel-Haenszel odds ratio. This criterion helps us to evaluate whether a split in a Rasch tree is based on a substantial or an ignorable difference in item parameters, and it allows the Rasch tree to stop growing when DIF between the identified subgroups is small. Furthermore, it supports identifying DIF items and quantifying DIF effect sizes in each split. Based on simulation results, we conclude that the Mantel-Haenszel effect size further reduces unnecessary splits in Rasch trees under the null hypothesis, or when the sample size is large but DIF effects are negligible. To make the stopping criterion easy-to-use for applied researchers, we have implemented the procedure in the statistical software R. Finally, we discuss how DIF effects between different nodes in a Rasch tree can be interpreted and emphasize the importance of purification strategies for the Mantel-Haenszel procedure on tree stopping and DIF item classification.

Against the "one method fits all data sets" philosophy for comparison studies in methodological research.

Many methodological comparison studies aim at identifying a single or a few "best performing" methods over a certain range of data sets. In this paper we take a different viewpoint by asking whether the research question of identifying the best performing method is what we should be striving for in the first place. We will argue that this research question implies assumptions which we do not consider warranted in methodological research, that a different research question would be more informative, and how this research question can be fruitfully investigated.

PARP14 is a novel target in STAT6 mutant follicular lymphoma.

The variable clinical course of follicular lymphoma (FL) is determined by the molecular heterogeneity of tumor cells and complex interactions within the tumor microenvironment (TME). IL-4 producing follicular helper T cells (TFH) are critical components of the FL TME. Binding of IL-4 to IL-4R on FL cells activates JAK/STAT signaling. We identified STAT6 mutations (STAT6MUT) in 13% of FL (N = 33/258), all clustered within the DNA binding domain. Gene expression data and immunohistochemistry showed upregulation of IL-4/STAT6 target genes in STAT6MUT FL, including CCL17, CCL22, and FCER2 (CD23). Functionally, STAT6MUT was gain-of-function by serial replating phenotype in pre-B CFU assays. Expression of STAT6MUT enhanced IL-4 induced FCER2/CD23, CCL17 and CCL22 expression and was associated with nuclear accumulation of pSTAT6. RNA sequencing identified PARP14 -a transcriptional switch and co-activator of STAT6- among the top differentially upregulated genes in IL-4 stimulated STAT6MUT lymphoma cells and in STAT6MUT primary FL cells. Quantitative chromatin immunoprecipitation (qChIP) demonstrated binding of STAT6MUT but not STAT6WT to the PARP14 promotor. Reporter assays showed increased IL-4 induced transactivation activity of STAT6MUT at the PARP14 promotor, suggesting a self-reinforcing regulatory circuit. Knock-down of PARP14 or PARP-inhibition abrogated the STAT6MUT gain-of-function phenotype. Thus, our results identify PARP14 as a novel therapeutic target in STAT6MUT FL.

Score-based measurement invariance checks for Bayesian maximum-a-posteriori estimates in item response theory.

A family of score-based tests has been proposed in recent years for assessing the invariance of model parameters in several models of item response theory (IRT). These tests were originally developed in a maximum likelihood framework. This study discusses analogous tests for Bayesian maximum-a-posteriori estimates and multiple-group IRT models. We propose two families of statistical tests, which are based on an approximation using a pooled variance method, or on a simulation approach based on asymptotic results. The resulting tests were evaluated by a simulation study, which investigated their sensitivity against differential item functioning with respect to a categorical or continuous person covariate in the two- and three-parametric logistic models. Whereas the method based on pooled variance was found to be useful in practice with maximum likelihood as well as maximum-a-posteriori estimates, the simulation-based approach was found to require large sample sizes to lead to satisfactory results.

Predictors of depression among middle-aged and older men and women in Europe: A machine learning approach.

Background: The high prevalence of depression in a growing aging population represents a critical public health issue. It is unclear how social, health, cognitive, and functional variables rank as risk/protective factors for depression among older adults and whether there are conspicuous differences among men and women. Methods: We used random forest analysis (RFA), a machine learning method, to compare 56 risk/protective factors for depression in a large representative sample of European older adults (N = 67,603; ages 45-105y; 56.1% women; 18 countries) from the Survey of Health, Ageing and Retirement in Europe (SHARE Wave 6). Depressive symptoms were assessed using the EURO-D questionnaire: Scores ≥ 4 indicated depression. Predictors included a broad array of sociodemographic, relational, health, lifestyle, and cognitive variables. Findings: Self-rated social isolation and self-rated poor health were the strongest risk factors, accounting for 22.0% (in men) and 22.3% (in women) of variability in depression. Odds ratios (OR) per +1SD in social isolation were 1.99x, 95% CI [1.90,2.08] in men; 1.93x, 95% CI [1.85,2.02] in women. OR for self-rated poor health were 1.93x, 95% CI [1.81,2.05] in men; 1.98x, 95% CI [1.87,2.10] in women. Difficulties in mobility (in both sexes), difficulties in instrumental activities of daily living (in men), and higher self-rated family burden (in women) accounted for an additional but small percentage of variance in depression risk (2.2% in men, 1.5% in women). Interpretation: Among 56 predictors, self-perceived social isolation and self-rated poor health were the most salient risk factors for depression in middle-aged and older men and women. Difficulties in instrumental activities of daily living (in men) and increased family burden (in women) appear to differentially influence depression risk across sexes. Funding: This study was internally funded by Colorado State University through research start-up monies provided to Stephen Aichele, Ph.D.

An R toolbox for score-based measurement invariance tests in IRT models.

The detection of differential item functioning (DIF) is a central topic in psychometrics and educational measurement. In the past few years, a new family of score-based tests of measurement invariance has been proposed, which allows the detection of DIF along arbitrary person covariates in a variety of item response theory (IRT) models. This paper illustrates the application of these tests within the R system for statistical computing, making them accessible to a broad range of users. This presentation also includes IRT models for which these tests have not previously been investigated, such as the generalized partial credit model. The paper has three goals: First, we review the ideas behind score-based tests of measurement invariance. Second, we describe the implementation of these tests within the R system for statistical computing, which is based on the interaction of the R packages mirt, psychotools and strucchange. Third, we illustrate the application of this software and the interpretation of its output in two empirical datasets. The complete R code for reproducing our results is reported in the paper.

COVID-19 in Patients Receiving CD20-depleting Immunochemotherapy for B-cell Lymphoma.

The clinical and immunological impact of B-cell depletion in the context of coronavirus disease 2019 (COVID-19) is unclear. We conducted a prospectively planned analysis of COVID-19 in patients who received B-cell depleting anti-CD20 antibodies and chemotherapy for B-cell lymphomas. The control cohort consisted of age- and sex-matched patients without lymphoma who were hospitalized because of COVID-19. We performed detailed clinical analyses, in-depth cellular and molecular immune profiling, and comprehensive virological studies in 12 patients with available biospecimens. B-cell depleted lymphoma patients had more severe and protracted clinical course (median hospitalization 88 versus 17 d). All patients actively receiving immunochemotherapy (n = 5) required ICU support including long-term mechanical ventilation. Neutrophil recovery following granulocyte colony stimulating factor stimulation coincided with hyperinflammation and clinical deterioration in 4 of the 5 patients. Immune cell profiling and gene expression analysis of peripheral blood mononuclear cells revealed early activation of monocytes/macrophages, neutrophils, and the complement system in B-cell depleted lymphoma patients, with subsequent exacerbation of the inflammatory response and dysfunctional interferon signaling at the time of clinical deterioration of COVID-19. Longitudinal immune cell profiling and functional in vitro assays showed SARS-CoV-2-specific CD8+ and CD4+ T-effector cell responses. Finally, we observed long-term detection of SARS-CoV-2 in respiratory specimens (median 84 versus 12 d) and an inability to mount lasting SARS-CoV-2 antibody responses in B-cell depleted lymphoma patients. In summary, we identified clinically relevant particularities of COVID-19 in lymphoma patients receiving B-cell depleting immunochemotherapies.

Anchor Point Selection: Scale Alignment Based on an Inequality Criterion.

For detecting differential item functioning (DIF) between two or more groups of test takers in the Rasch model, their item parameters need to be placed on the same scale. Typically this is done by means of choosing a set of so-called anchor items based on statistical tests or heuristics. Here the authors suggest an alternative strategy: By means of an inequality criterion from economics, the Gini Index, the item parameters are shifted to an optimal position where the item parameter estimates of the groups best overlap. Several toy examples, extensive simulation studies, and two empirical application examples are presented to illustrate the properties of the Gini Index as an anchor point selection criterion and compare its properties to those of the criterion used in the alignment approach of Asparouhov and Muthén. In particular, the authors show that-in addition to the globally optimal position for the anchor point-the criterion plot contains valuable additional information and may help discover unaccounted DIF-inducing multidimensionality. They further provide mathematical results that enable an efficient sparse grid optimization and make it feasible to extend the approach, for example, to multiple group scenarios.

The Oncometabolite 5'-Deoxy-5'-Methylthioadenosine Blocks Multiple Signaling Pathways of NK Cell Activation.

Tumor cells develop various mechanisms to escape immune surveillance. In this context, oncometabolites secreted by tumor cells due to deregulated metabolic pathways, have been in the spotlight of researchers during the last years. 5'-Deoxy-5'-methylthioadenosine (MTA) phosphorylase (MTAP) deficiency in tumors results in the accumulation of MTA within the tumor microenvironment and thereby negatively influencing immune functions of various immune cells, including T and NK cells. The influence of MTA on T cell activation has been recently described in more detail, while its impact on NK cells is still largely unknown. Therefore, we aimed to illuminate the molecular mechanism of MTA-induced NK cell dysfunction. NK cell cytotoxicity against target cells was reduced in the presence of MTA in a dose-dependent manner, while NK cell viability remained unaffected. Furthermore, we revealed that MTA blocks NK cell degranulation and cytokine production upon target cell engagement as well as upon antibody stimulation. Interestingly, the immune-suppressive effect of MTA was less pronounced in healthy donors harboring an expansion of NKG2C+ NK cells. Finally, we demonstrated that MTA interferes with various signaling pathways downstream of the CD16 receptor upon NK cell activation, including the PI3K/AKT/S6, MAPK/ERK, and NF-κB pathways. In summary, we revealed that MTA blocks NK cell functions like cytotoxicity and cytokine production by interfering with the signaling cascade of activating NK cell receptors. Specific targeting of MTA metabolism in MTAP-deficient tumors therefore could offer a promising new strategy to reverse immune dysfunction of NK cells within the tumor microenvironment.

Conditional permutation importance revisited.

BACKGROUND: Random forest based variable importance measures have become popular tools for assessing the contributions of the predictor variables in a fitted random forest. In this article we reconsider a frequently used variable importance measure, the Conditional Permutation Importance (CPI). We argue and illustrate that the CPI corresponds to a more partial quantification of variable importance and suggest several improvements in its methodology and implementation that enhance its practical value. In addition, we introduce the threshold value in the CPI algorithm as a parameter that can make the CPI more partial or more marginal. RESULTS: By means of extensive simulations, where the original version of the CPI is used as the reference, we examine the impact of the proposed methodological improvements. The simulation results show how the improved CPI methodology increases the interpretability and stability of the computations. In addition, the newly proposed implementation decreases the computation times drastically and is more widely applicable. The improved CPI algorithm is made freely available as an add-on package to the open-source software R. CONCLUSION: The proposed methodology and implementation of the CPI is computationally faster and leads to more stable results. It has a beneficial impact on practical research by making random forest analyses more interpretable.

Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.

Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random forest, PREs retain a small subset of tree nodes in the final predictive model. These nodes can be written as simple rules of the form if [condition] then [prediction]. As a result, PREs are often much less complex than full decision tree ensembles, while they have been found to provide similar predictive performance in many situations. The current article introduces the methodology and shows how PREs can be fitted using the R package pre through several real-data examples from psychological research. The examples also illustrate a number of features of package pre that may be particularly useful for applications in psychology: support for categorical, multivariate and count responses, application of (non)negativity constraints, inclusion of confirmatory rules and standardized variable importance measures. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Selective PRMT5 Inhibitors Suppress Human CD8+ T Cells by Upregulation of p53 and Impairment of the AKT Pathway Similar to the Tumor Metabolite MTA.

Genetic alterations in tumor cells provide promising targets for antitumor therapy. Recently, loss of methylthioadenosine phosphorylase (MTAP), a deletion frequently occurring in cancer, has been shown to create vulnerability to the inhibition of the protein arginine methyltransferase 5 (PRMT5). MTAP deficiency leads to accumulation of methylthioadenosine (MTA), which reduces PRMT5 activity, and thus, sensitizes the tumor cells to selective PRMT5 inhibitors (PRMT5i). PRMT5i are investigated as a new strategy to selectively kill MTAP-deficient tumor cells by blocking residual PRMT5 activity, but also to treat PRMT5-overexpressing tumors. Although many studies investigated the role of PRMT5 in cancer, only little data exist about the effect of PRMT5 inhibition on immune cells. As we could show that the tumor metabolite MTA suppresses T cells, we asked whether selective PRMT5 inhibition is detrimental for T-cell immune responses. Therefore, we examined the effect of the synthetic PRMT5 inhibitor EPZ015666 on human CD8+ T cells in direct comparison with the naturally occurring PRMT5-inhibiting molecule MTA. Both compounds reduced T-cell proliferation, viability, and functionality. In addition, T-cell metabolism was impaired upon PRMT5 inhibition. These effects coincided with the induction of p53 expression and reduced AKT/mTOR signaling. Our data clearly demonstrate that PRMT5 activity is involved in various cellular processes of human CD8+ T cells associated with essential T-cell functions. Therefore, not only tumor cells, but also antitumor immune responses, are compromised by PRMT5 inhibitors. This emphasizes the importance of considering side effects on the immune system when developing new strategies to specifically target not only MTAP-deficient tumors.

Human Double-Negative Regulatory T-Cells Induce a Metabolic and Functional Switch in Effector T-Cells by Suppressing mTOR Activity.

The recently discovered population of TCRαß+ CD4-/CD8- (double-negative, DN) T-cells are highly potent suppressor cells in mice and humans. In preclinical transplantation models, adoptive transfer of DN T-cells specifically inhibits alloreactive T-cells and prevents transplant rejection or graft-vs.-host disease (GvHD). Interestingly, clinical studies in patients who underwent allogeneic stem cell transplantation reveal an inverse correlation between the frequency of circulating DN T-cells and the severity of GvHD, suggesting a therapeutic potential of human DN T-cells. However, their exact mode of action has not been elucidated yet. Investigating the impact of DN T-cells on conventional T-cells, we found that human DN T-cells selectively inhibit mTOR signaling in CD4 T-cells. Given that mTOR is a critical regulator of cellular metabolism, we further determined the impact of DN T-cells on the metabolic framework of T-cells. Intriguingly, DN T-cells diminished expression of glucose transporters and glucose uptake, whereas fatty acid uptake was not modified, indicating that DN T-cells prevent metabolic adaptation of CD4 T-cells upon activation (i.e., glycolytic switch) thereby contributing to their suppression. Further analyses demonstrated that CD4 T-cells also do not upregulate homing receptors associated with inflammatory processes. In contrast, expression of central memory-cell associated cell surface markers and transcription factors were increased by DN T-cells. Moreover, CD4 T-cells failed to produce inflammatory cytokines after co-culture with DN T-cells, whereas IL-2 secretion was enhanced. Taken together DN T-cells impair metabolic reprogramming of conventional CD4 T-cells by abrogating mTOR signaling, thereby modulating CD4 T-cell functionality. These results uncover a new mechanism of DN T-cell-mediated suppression, pointing out that DN T-cells could serve as cell-based therapy to limit alloreactive immune response.

Investigating Measurement Invariance by Means of Parameter Instability Tests for 2PL and 3PL Models.

M-fluctuation tests are a recently proposed method for detecting differential item functioning in Rasch models. This article discusses a generalization of this method to two additional item response theory models: the two-parametric logistic model and the three-parametric logistic model with a common guessing parameter. The Type I error rate and the power of this method were evaluated by a variety of simulation studies. The results suggest that the new method allows the detection of various forms of differential item functioning in these models, which also includes differential discrimination and differential guessing effects. It is also robust against moderate violations of several assumptions made in the item parameter estimation.

Tree-Based Global Model Tests for Polytomous Rasch Models.

Psychometric measurement models are only valid if measurement invariance holds between test takers of different groups. Global model tests, such as the well-established likelihood ratio (LR) test, are sensitive to violations of measurement invariance, such as differential item functioning and differential step functioning. However, these traditional approaches are only applicable when comparing previously specified reference and focal groups, such as males and females. Here, we propose a new framework for global model tests for polytomous Rasch models based on a model-based recursive partitioning algorithm. With this approach, a priori specification of reference and focal groups is no longer necessary, because they are automatically detected in a data-driven way. The statistical background of the new framework is introduced along with an instructive example. A series of simulation studies illustrates and compares its statistical properties to the well-established LR test. While both the LR test and the new framework are sensitive to differential item functioning and differential step functioning and respect a given significance level regardless of true differences in the ability distributions, the new data-driven approach is more powerful when the group structure is not known a priori-as will usually be the case in practical applications. The usage and interpretation of the new method are illustrated in an empirical application example. A software implementation is freely available in the R system for statistical computing.

Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation.

Measurement invariance is a fundamental assumption in item response theory models, where the relationship between a latent construct (ability) and observed item responses is of interest. Violation of this assumption would render the scale misinterpreted or cause systematic bias against certain groups of persons. While a number of methods have been proposed to detect measurement invariance violations, they typically require advance definition of problematic item parameters and respondent grouping information. However, these pieces of information are typically unknown in practice. As an alternative, this paper focuses on a family of recently proposed tests based on stochastic processes of casewise derivatives of the likelihood function (i.e., scores). These score-based tests only require estimation of the null model (when measurement invariance is assumed to hold), and they have been previously applied in factor-analytic, continuous data contexts as well as in models of the Rasch family. In this paper, we aim to extend these tests to two-parameter item response models, with strong emphasis on pairwise maximum likelihood. The tests' theoretical background and implementation are detailed, and the tests' abilities to identify problematic item parameters are studied via simulation. An empirical example illustrating the tests' use in practice is also provided.

Suppressive effects of tumor cell-derived 5'-deoxy-5'-methylthioadenosine on human T cells.

The immunosuppressive tumor microenvironment represents one of the main obstacles for immunotherapy of cancer. The tumor milieu is among others shaped by tumor metabolites such as 5'-deoxy-5'-methylthioadenosine (MTA). Increased intratumoral MTA levels result from a lack of the MTA-catabolizing enzyme methylthioadenosine phosphorylase (MTAP) in tumor cells and are found in various tumor entities. Here, we demonstrate that MTA suppresses proliferation, activation, differentiation, and effector function of antigen-specific T cells without eliciting cell death. Conversely, if MTA is added to highly activated T cells, MTA exerts cytotoxic effects on T cells. We identified the Akt pathway, a critical signal pathway for T cell activation, as a target of MTA, while, for example, p38 remained unaffected. Next, we provide evidence that MTA exerts its immunosuppressive effects by interfering with protein methylation in T cells. To confirm the relevance of the suppressive effects of exogenously added MTA on human T cells, we used an MTAP-deficient tumor cell-line that was stably transfected with the MTAP-coding sequence. We observed that T cells stimulated with MTAP-transfected tumor cells revealed a higher proliferative capacity compared to T cells stimulated with Mock-transfected cells. In conclusion, our findings reveal a novel immune evasion strategy of human tumor cells that could be of interest for therapeutic targeting.