Depression and Anxiety Mediate the Relationship between Discrimination and Well-Being in a Sample of Latinx Adults with Type 2 Diabetes: Results from a Dual Mediation Analysis.

Latinxs experience greater risk for type 2 diabetes, discrimination, and poor mental health. The pathways linking these factors, however, are not well understood. This study tested whether depression and anxiety mediated the relationship between discrimination and well-being. Bootstrapped mediation tests were conducted using a sample of Latinx adults with type 2 diabetes (n = 121) and regression models adjusted for demographic and health covariates. Depression and anxiety fully and jointly mediated the effect of discrimination on well-being; everyday discrimination was linked to elevated symptoms of depression and anxiety which were, in turn, independently linked to reduced emotional well-being. Moreover, the effect size for the anxiety pathway (ß=-0.13) was 60% larger than for depression (ß=-0.08). Dual mediation suggests depression, and especially anxiety, may be important targets for interventions seeking to mitigate the deleterious effects of discrimination. Findings have important implications for psychotherapeutic treatments and public health policy.

CD203c is expressed by human fetal hepatoblasts and distinguishes subsets of hepatoblastoma.

Background & Aims: Hepatocytic cells found during prenatal development have unique features compared to their adult counterparts, and are believed to be the precursors of pediatric hepatoblastoma. The cell-surface phenotype of hepatoblasts and hepatoblastoma cell lines was evaluated to discover new markers of these cells and gain insight into the development of hepatocytic cells and the phenotypes and origins of hepatoblastoma. Methods: Human midgestation livers and four pediatric hepatoblastoma cell lines were screened using flow cytometry. Expression of over 300 antigens was evaluated on hepatoblasts defined by their expression of CD326 (EpCAM) and CD14. Also analyzed were hematopoietic cells, expressing CD45, and liver sinusoidal-endothelial cells (LSECs), expressing CD14 but lacking CD45 expression. Select antigens were further examined by fluorescence immunomicroscopy of fetal liver sections. Antigen expression was also confirmed on cultured cells by both methods. Gene expression analysis by liver cells, 6 hepatoblastoma cell lines, and hepatoblastoma cells was performed. Immunohistochemistry was used to evaluate CD203c, CD326, and cytokeratin-19 expression on three hepatoblastoma tumors. Results: Antibody screening identified many cell surface markers commonly or divergently expressed by hematopoietic cells, LSECs, and hepatoblasts. Thirteen novel markers expressed on fetal hepatoblasts were identified including ectonucleotide pyrophosphatase/phosphodiesterase family member 3 (ENPP-3/CD203c), which was found to be expressed by hepatoblasts with widespread expression in the parenchyma of the fetal liver. In culture CD203c+CD326++ cells resembled hepatocytic cells with coexpression of albumin and cytokeratin-19 confirming a hepatoblast phenotype. CD203c expression declined rapidly in culture whereas the loss of CD326 was not as pronounced. CD203c and CD326 were co-expressed on a subset of hepatoblastoma cell lines and hepatoblastomas with an embryonal pattern. Conclusions: CD203c is expressed on hepatoblasts and may play a role in purinergic signaling in the developing liver. Hepatoblastoma cell lines were found to consist of two broad phenotypes consisting of a cholangiocyte-like phenotype that expressed CD203c and CD326 and a hepatocyte-like phenotype with diminished expression of these markers. CD203c was expressed by some hepatoblastoma tumors and may represent a marker of a less differentiated embryonal component.

Bromodomain 4 inhibition leads to MYCN downregulation in Wilms tumor.

BACKGROUND: Wilms tumor is the most common childhood kidney cancer. Two distinct histological subtypes of Wilms tumor have been described: tumors lacking anaplasia (the favorable subtype) and tumors displaying anaplastic features (the unfavorable subtype). Children with favorable disease generally have a very good prognosis, whereas those with anaplasia are oftentimes refractory to standard treatments and suffer poor outcomes, leading to an unmet clinical need. MYCN dysregulation has been associated with a number of pediatric cancers including Wilms tumor. PROCEDURES: In this context, we undertook a functional genomics approach to uncover novel therapeutic strategies for those patients with anaplastic Wilms tumor. Genomic analysis and in vitro experimentation demonstrate that cell growth can be reduced by modulating MYCN overexpression via bromodomain 4 (BRD4) inhibition in both anaplastic and nonanaplastic Wilms tumor models. RESULTS: We observed a time-dependent reduction of MYCN and MYCC protein levels upon BRD4 inhibition in Wilms tumor cell lines, which led to cell death and proliferation suppression. BRD4 inhibition significantly reduced tumor volumes in Wilms tumor patient-derived xenograft (PDX) mouse models. CONCLUSIONS: We suggest that AZD5153, a novel dual-BRD4 inhibitor, can reduce MYCN levels in both anaplastic and nonanaplastic Wilms tumor cell lines, reduces tumor volume in Wilms tumor PDXs, and should be further explored for its therapeutic potential.

Changes in quality of life following hypoglycaemia in adults with type 2 diabetes: A systematic review of longitudinal studies.

AIM: To conduct a systematic review of published studies reporting on the longitudinal impacts of hypoglycaemia on quality of life (QoL) in adults with type 2 diabetes. METHOD: Database searches with no restrictions by language or date were conducted in MEDLINE, Cochrane Library, CINAHL and PsycINFO. Studies were included for review if they used a longitudinal design (e.g. cohort studies, randomised controlled trials) and reported on the association between hypoglycaemia and changes over time in patient-reported outcomes related to QoL. RESULTS: In all, 20 longitudinal studies published between 1998 and 2020, representing 50,429 adults with type 2 diabetes, were selected for review. A descriptive synthesis following Synthesis Without Meta-analysis guidelines indicated that self-treated symptomatic hypoglycaemia was followed by impairments in daily functioning along with elevated symptoms of generalised anxiety, diabetes distress and fear of hypoglycaemia. Severe hypoglycaemic events were associated with reduced confidence in diabetes self-management and lower ratings of perceived health over time. Frequent hypoglycaemia was followed by reduced energy levels and diminished emotional well-being. There was insufficient evidence, however, to conclude that hypoglycaemia impacted sleep quality, depressive symptoms, general mood, social support or overall diabetes-specific QoL. CONCLUSIONS: Longitudinal evidence in this review suggests hypoglycaemia is a common occurrence among adults with type 2 diabetes that impacts key facets in the physical and psychological domains of QoL. Nonetheless, additional longitudinal research is needed-in particular, studies targeting diverse forms of hypoglycaemia, more varied facets of QoL and outcomes assessed using hypoglycaemia-specific measures.

Defining an embryonal rhabdomyosarcoma endotype.

Rhabdomyosarcoma (RMS) is the most common childhood soft-tissue sarcoma. The largest subtype of RMS is embryonal rhabdomyosarcoma (ERMS) and accounts for 53% of all RMS. ERMS typically occurs in the head and neck region, bladder, or reproductive organs and portends a promising prognosis when localized; however, when metastatic the 5-yr overall survival rate is ∼43%. The genomic landscape of ERMS demonstrates a range of putative driver mutations, and thus the recognition of the pathological mechanisms driving tumor maintenance should be critical for identifying effective targeted treatments at the level of the individual patients. Here, we report genomic, phenotypic, and bioinformatic analyses for a case of a 3-yr-old male who presented with bladder ERMS. Additionally, we use an unsupervised agglomerative clustering analysis of RNA and whole-exome sequencing data across ERMS and undifferentiated pleomorphic sarcoma (UPS) tumor samples to determine several major endotypes inferring potential targeted treatments for a spectrum of pediatric ERMS patient cases.

Evaluating the consistency of large-scale pharmacogenomic studies.

Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data.

Volasertib preclinical activity in high-risk hepatoblastoma.

Relapsed and metastatic hepatoblastoma represents an unmet clinical need with limited chemotherapy treatment options. In a chemical screen, we identified volasertib as an agent with in vitro activity, inhibiting hepatoblastoma cell growth while sparing normal hepatocytes. Volasertib targets PLK1 and prevents the progression of mitosis, resulting in eventual cell death. PLK1 is overexpressed in hepatoblastoma biopsies relative to normal liver tissue. As a potential therapeutic strategy, we tested the combination of volasertib and the relapse-related hepatoblastoma chemotherapeutic irinotecan. We found both in vitro and in vivo efficacy of this combination, which may merit further preclinical investigation and exploration for a clinical trial concept.

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma.

BACKGROUND: Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments. METHODS: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. RESULTS: Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). CONCLUSIONS: These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

Sstack: an R package for stacking with applications to scenarios involving sequential addition of samples and features.

SUMMARY: Biological processes are characterized by a variety of different genomic feature sets. However, often times when building models, portions of these features are missing for a subset of the dataset. We provide a modeling framework to effectively integrate this type of heterogeneous data to improve prediction accuracy. To test our methodology, we have stacked data from the Cancer Cell Line Encyclopedia to increase the accuracy of drug sensitivity prediction. The package addresses the dynamic regime of information integration involving sequential addition of features and samples. AVAILABILITY AND IMPLEMENTATION: The framework has been implemented as a R package Sstack, which can be downloaded from https://cran.r-project.org/web/packages/Sstack/index.html, where further explanation of the package is available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Application of transfer learning for cancer drug sensitivity prediction.

BACKGROUND: In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context. RESULTS: In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first approach is based on latent variable cost optimization and the second approach considers polynomial mapping between the two databases. Utilizing CCLE and GDSC databases, we illustrate that the proposed approaches accomplish a better prediction of drug sensitivities for different scenarios as compared to the existing approaches. CONCLUSION: We have compared the performance of the proposed predictive models with database-specific individual models as well as existing transfer learning approaches. We note that our proposed approaches exhibit superior performance compared to the abovementioned alternative techniques for predicting sensitivity for different anti-cancer compounds, particularly the nonlinear mapping model shows the best overall performance.

Investigation of model stacking for drug sensitivity prediction.

BACKGROUND: A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types. RESULTS: We explore the predictive performance of model stacking and the effect of stacking on the predictive bias and squared error. In addition we discuss the analytical underpinnings supporting the advantages of stacking in reducing squared error and inherent bias of random forests in prediction of outliers. The framework is tested on a setup including gene expression, drug target, physical properties and drug response information for a set of drugs and cell lines. CONCLUSION: The performance of individual and stacked models are compared. We note that stacking models built on two heterogeneous datasets provide superior performance to stacking different models built on the same dataset. It is also noted that stacking provides a noticeable reduction in the bias of our predictors when the dominant eigenvalue of the principle axis of variation in the residuals is significantly higher than the remaining eigenvalues.

Heterogeneity Aware Random Forest for Drug Sensitivity Prediction.

Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the samples in model training and prediction. We explore this hypothesis and observe that ensemble model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower than the combined sample size. To incorporate the heterogeneity idea in the commonly used ensemble based predictive model of Random Forests, we propose Heterogeneity Aware Random Forests (HARF) that assigns weights to the trees based on the category of the sample. We treat heterogeneity as a latent class allocation problem and present a covariate free class allocation approach based on the distribution of leaf nodes of the model ensemble. Applications on CCLE and GDSC databases show that HARF outperforms traditional Random Forest when the average drug responses of cancer types are different.

Combination therapy design for maximizing sensitivity and minimizing toxicity.

BACKGROUND: Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells. RESULTS: The problem is formulated as maximizing sensitivity over tumor cell models while maintaining sensitivity below a threshold over normal cell models. We utilize the constrained structure of tumor proliferation models to design an accelerated lexicographic search algorithm for generating the optimal solution. For comparison purposes, we also designed two suboptimal search algorithms based on evolutionary algorithms and hill-climbing based techniques. Results over synthetic models and models generated from Genomics of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to arrive at optimal or close to optimal solutions in significantly lower number of steps as compared to exhaustive search. We also present the theoretical analysis of the expected number of comparisons required for the proposed Lexicographic search that compare favorably with the observed number of computations. CONCLUSIONS: The proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.