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.

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.

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.

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.

Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests.

In an interesting and quite exhaustive review on Random Forests (RF) methodology in bioinformatics Touw et al. address--among other topics--the problem of the detection of interactions between variables based on RF methodology. We feel that some important statistical concepts, such as 'interaction', 'conditional dependence' or 'correlation', are sometimes employed inconsistently in the bioinformatics literature in general and in the literature on RF in particular. In this letter to the Editor, we aim to clarify some of the central statistical concepts and point out some confusing interpretations concerning RF given by Touw et al. and other authors.

Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations.

The use of random forests is increasingly common in genetic association studies. The variable importance measure (VIM) that is automatically calculated as a by-product of the algorithm is often used to rank polymorphisms with respect to their ability to predict the investigated phenotype. Here, we investigate a characteristic of this methodology that may be considered as an important pitfall, namely that common variants are systematically favoured by the widely used Gini VIM. As a consequence, researchers may overlook rare variants that contribute to the missing heritability. The goal of the present article is 3-fold: (i) to assess this effect quantitatively using simulation studies for different types of random forests (classical random forests and conditional inference forests, that employ unbiased variable selection criteria) as well as for different importance measures (Gini and permutation based); (ii) to explore the trees and to compare the behaviour of random forests and the standard logistic regression model in order to understand the statistical mechanisms behind the preference for common variants; and (iii) to summarize these results and previously investigated properties of random forest VIMs in the context of genetic association studies and to make practical recommendations regarding the choice of the random forest and variable importance type. All our analyses can be reproduced using R code available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/ginibias/.

An AUC-based permutation variable importance measure for random forests.

BACKGROUND: The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. RESULTS: We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings. CONCLUSIONS: The standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html.

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.

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).

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.

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.

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.

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.

The behaviour of random forest permutation-based variable importance measures under predictor correlation.

BACKGROUND: Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results. RESULTS: In the case when both predictor correlation was present and predictors were associated with the outcome (HA), the unconditional RF VIM attributed a higher share of importance to correlated predictors, while under the null hypothesis that no predictors are associated with the outcome (H0) the unconditional RF VIM was unbiased. Conditional VIMs showed a decrease in VIM values for correlated predictors versus the unconditional VIMs under HA and was unbiased under H0. Scaled VIMs were clearly biased under HA and H0. CONCLUSIONS: Unconditional unscaled VIMs are a computationally tractable choice for large datasets and are unbiased under the null hypothesis. Whether the observed increased VIMs for correlated predictors may be considered a "bias" - because they do not directly reflect the coefficients in the generating model - or if it is a beneficial attribute of these VIMs is dependent on the application. For example, in genetic association studies, where correlation between markers may help to localize the functionally relevant variant, the increased importance of correlated predictors may be an advantage. On the other hand, we show examples where this increased importance may result in spurious signals.

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).

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.

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.

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction.

BACKGROUND: In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data), since such analyses are particularly exposed to this kind of bias. METHODS: In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps) within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. RESULTS: We assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case) and the bias resulting from the choice of the classification method are examined both separately and jointly. CONCLUSIONS: The median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.