Disruption of lysosomal nutrient sensing scaffold contributes to pathogenesis of a fatal neurodegenerative lysosomal storage disease.

The ceroid lipofuscinosis neuronal 1 (CLN1) disease, formerly called infantile neuronal ceroid lipofuscinosis, is a fatal hereditary neurodegenerative lysosomal storage disorder. This disease is caused by loss-of-function mutations in the CLN1 gene, encoding palmitoyl-protein thioesterase-1 (PPT1). PPT1 catalyzes depalmitoylation of S-palmitoylated proteins for degradation and clearance by lysosomal hydrolases. Numerous proteins, especially in the brain, require dynamic S-palmitoylation (palmitoylation-depalmitoylation cycles) for endosomal trafficking to their destination. While 23 palmitoyl-acyl transferases in the mammalian genome catalyze S-palmitoylation, depalmitoylation is catalyzed by thioesterases such as PPT1. Despite these discoveries, the pathogenic mechanism of CLN1 disease has remained elusive. Here, we report that in the brain of Cln1-/- mice, which mimic CLN1 disease, the mechanistic target of rapamycin complex-1 (mTORC1) kinase is hyperactivated. The activation of mTORC1 by nutrients requires its anchorage to lysosomal limiting membrane by Rag GTPases and Ragulator complex. These proteins form the lysosomal nutrient sensing scaffold to which mTORC1 must attach to activate. We found that in Cln1-/- mice, two constituent proteins of the Ragulator complex (vacuolar (H+)-ATPase and Lamtor1) require dynamic S-palmitoylation for endosomal trafficking to the lysosomal limiting membrane. Intriguingly, Ppt1 deficiency in Cln1-/- mice misrouted these proteins to the plasma membrane disrupting the lysosomal nutrient sensing scaffold. Despite this defect, mTORC1 was hyperactivated via the IGF1/PI3K/Akt-signaling pathway, which suppressed autophagy contributing to neuropathology. Importantly, pharmacological inhibition of PI3K/Akt suppressed mTORC1 activation, restored autophagy, and ameliorated neurodegeneration in Cln1-/- mice. Our findings reveal a previously unrecognized role of Cln1/Ppt1 in regulating mTORC1 activation and suggest that IGF1/PI3K/Akt may be a targetable pathway for CLN1 disease.

Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.

We consider the problem of combining multiple biomarkers to improve the diagnostic accuracy of detecting a disease when only group-tested data on the disease status are available. There are several challenges in addressing this problem, including unavailable individual disease statuses, differential misclassification depending on group size and number of diseased individuals in the group, and extensive computation due to a large number of possible combinations of multiple biomarkers. To tackle these issues, we propose a pairwise model fitting approach to estimating the distribution of the optimal linear combination of biomarkers and its diagnostic accuracy under the assumption of a multivariate normal distribution. The approach is evaluated in simulation studies and applied to data on chlamydia detection and COVID-19 diagnosis.

Retrospective ACEs predict complex PTSD symptoms in a large sample of Chinese young adults longitudinally: the moderating role of self-compassion.

This longitudinal study in Mainland China (2021-2022) explored the impact of adverse childhood experiences (ACEs) on complex posttraumatic stress disorder (CPTSD) symptoms, with a focus on the role of self-compassion. Among 18,933 surveyed university students, 21.2% reported experiencing at least one ACE. Results revealed a clear relationship between ACEs and CPTSD symptoms. Furthermore, self-compassion, particularly the dimensions of self-judgment and isolation, moderated the association between retrospective ACEs and posttraumatic stress disorder (PTSD) and disturbance in self-organization (DSO) symptoms. These findings highlight the enduring impact of ACEs on CPTSD symptoms and emphasize the importance of early identification and targeted interventions, especially addressing self-judgment and isolation, to mitigate CPTSD risk among young Chinese adults.

An adaptive direction-assisted test for microbiome compositional data.

MOTIVATION: Microbial communities have been shown to be associated with many complex diseases, such as cancers and cardiovascular diseases. The identification of differentially abundant taxa is clinically important. It can help understand the pathology of complex diseases, and potentially provide preventive and therapeutic strategies. Appropriate differential analyses for microbiome data are challenging due to its unique data characteristics including compositional constraint, excessive zeros and high dimensionality. Most existing approaches either ignore these data characteristics or only account for the compositional constraint by using log-ratio transformations with zero observations replaced by a pseudocount. However, there is no consensus on how to choose a pseudocount. More importantly, ignoring the characteristic of excessive zeros may result in poorly powered analyses and therefore yield misleading findings. RESULTS: We develop a novel microbiome-based direction-assisted test for the detection of overall difference in microbial relative abundances between two health conditions, which simultaneously incorporates the characteristics of relative abundance data. The proposed test (i) divides the taxa into two clusters by the directions of mean differences of relative abundances and then combines them at cluster level, in light of the compositional characteristic; and (ii) contains a burden type test, which collapses multiple taxa into a single one to account for excessive zeros. Moreover, the proposed test is an adaptive procedure, which can accommodate high-dimensional settings and yield high power against various alternative hypotheses. We perform extensive simulation studies across a wide range of scenarios to evaluate the proposed test and show its substantial power gain over some existing tests. The superiority of the proposed approach is further demonstrated with real datasets from two microbiome studies. AVAILABILITY AND IMPLEMENTATION: An R package for MiDAT is available at https://github.com/zhangwei0125/MiDAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimizing treatment allocation in randomized clinical trials by leveraging baseline covariates.

We consider the problem of optimizing treatment allocation for statistical efficiency in randomized clinical trials. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern machine learning methods make it increasingly feasible to approach full efficiency. Accordingly, we derive the optimal allocation ratio by maximizing the design efficiency of a randomized trial, assuming that an efficient estimator will be used for analysis. We then expand the scope of optimization by considering covariate-dependent randomization (CDR), which has some flavor of an observational study but provides the same level of scientific rigor as a standard randomized trial. We describe treatment effect estimators that are consistent, asymptotically normal, and (nearly) efficient under CDR, and derive the optimal propensity score by maximizing the design efficiency of a CDR trial (under the assumption that an efficient estimator will be used for analysis). Our optimality results translate into optimal designs that improve upon standard practice. Real-world examples and simulation results demonstrate that the proposed designs can produce substantial efficiency improvements in realistic settings.

Significance tests for covariates in the diagnostic accuracy index of a biomarker against a continuous gold standard.

Receiver operating characteristic (ROC) curve is a popular tool to describe and compare the diagnostic accuracy of biomarkers when a binary-scale gold standard is available. However, there are many examples of diagnostic tests whose gold standards are continuous. Hence, Several extensions of receiver operating characteristic (ROC) curve are proposed to evaluate the diagnostic potential of biomarkers when the gold standard is continuous-scale. Moreover, in evaluating these biomarkers, it is often necessary to consider the effects of covariates on the diagnostic accuracy of the biomarker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. Applying the covariate adjustment to the case that the gold standard is continuous is challenging and has not been addressed in the literature. To fill the gap, we propose two general testing frameworks to account for the covariates effect on diagnostic accuracy. Simulation studies are conducted to compare the proposed tests. Data from a study that assessed three types of imaging modalities with the purpose of detecting neoplastic colon polyps and cancers are used to illustrate the proposed methods.

Low-rank latent matrix-factor prediction modeling for generalized high-dimensional matrix-variate regression.

Motivated by diagnosing the COVID-19 disease using two-dimensional (2D) image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict responses that may come from an exponential distribution family, where covariates include high-dimensional matrix-variate biomarkers. A latent generalized matrix regression (LaGMaR) is formulated, where the latent predictor is a low-dimensional matrix factor score extracted from the low-rank signal of the matrix variate through a cutting-edge matrix factor model. Unlike the general spirit of penalizing vectorization plus the necessity of tuning parameters in the literature, instead, our prediction modeling in LaGMaR conducts dimension reduction that respects the geometric characteristic of intrinsic 2D structure of the matrix covariate and thus avoids iteration. This greatly relieves the computation burden, and meanwhile maintains structural information so that the latent matrix factor feature can perfectly replace the intractable matrix-variate owing to high-dimensionality. The estimation procedure of LaGMaR is subtly derived by transforming the bilinear form matrix factor model onto a high-dimensional vector factor model, so that the method of principle components can be applied. We establish bilinear-form consistency of the estimated matrix coefficient of the latent predictor and consistency of prediction. The proposed approach can be implemented conveniently. Through simulation experiments, the prediction capability of LaGMaR is shown to outperform some existing penalized methods under diverse scenarios of generalized matrix regressions. Through the application to a real COVID-19 dataset, the proposed approach is shown to predict efficiently the COVID-19.

The reciprocal relations among self-compassion, and depression among adolescents after the Jiuzhaigou earthquake: A three-wave cross-lagged study.

BACKGROUND: Previous cross-sectional studies have examined the relationship between self-compassion and depression. Although it is often implicitly assumed that self-compassion may increase the vulnerability of an individual to depression, only a few studies have assessed whether self-compassion is a cause or a consequence of depression or both. METHOD: To examine such reciprocal effects, we assessed self-compassion and depression via self-report measures. At the baseline assessment (Time 1, T1), 450 students (M = 13.72, SD = 0.83, 54.2% females) participated 10 months after the Jiuzhaigou earthquake. We reassessed the T1 sample after 6- and 12-month intervals. At Time 2 (T2) assessment, 398 (56.0% female participants) of the Wave 1 participants were retained, and at Time 3 (T3) assessment, 235 (52.5% female participants) of the T1 and T2 participants were retained. RESULTS: Cross-lagged analyses indicated that positive self-compassion could significantly reduce subsequent depression. However, depression did not significantly predict later positive self-compassion. Negative self-compassion at T1 increased depression at T2, but negative self-compassion at T2 did not significantly predict depression at T3. In addition, positive self-compassion significantly reduced subsequent negative self-compassion. CONCLUSIONS: Positive self-compassion appears to protect adolescents against depression and maintain this protection over time, whereas negative self-compassion may worsen depression in adolescents during the initial stages of traumatic events. Additionally, positive self-compassion may decrease the level of negative self-compassion.

The reciprocal relations among self-compassion, posttraumatic stress disorder, and posttraumatic growth among adolescents after the Jiuzhaigou earthquake: a three-wave cross-lagged study.

BACKGROUND: Previous cross-sectional studies have examined the relationship between self-compassion, post-traumatic stress disorder (PTSD), and post-traumatic growth (PTG). But no study has tested whether self-compassion is a cause or a consequence of PTSD, PTG, or both. AIMS: The cross-lag analysis was used to examine the reciprocal effects among self-compassion, PTSD, and PTG. METHOD: We used data from 244 adolescents who had experienced earthquakes. We assessed self-compassion, PTSD, and PTG via self-report measures after the earthquake in Jiuzhaigou, as well as 6 and 12 months later. RESULTS: Cross-lagged analyses indicated that positive self-compassion could significantly predict subsequent PTSD and PTG. Meanwhile, PTSD and PTG also predicted later positive self-compassion. Negative self-compassion at T1 increased PTSD at T2, and neither PTSD nor PTG significantly predicted subsequent negative self-compassion. In addition, negative self-compassion at T1 significantly predicted positive self-compassion at T2, while positive self-compassion at T2 significantly predicted negative self-compassion at T3. CONCLUSIONS: Positive self-compassion is a protective factor of post-traumatic psychological response, and it is maintained for a long time, while negative self-compassion may aggravate the negative psychological outcomes of adolescents in the early stage of experiencing traumatic events. In addition, positive and negative self-compassion can influence each other over time.

Younger age and early puberty are associated with cognitive function decline in children with Cushing disease.

OBJECTIVE: To investigate the effect of hypercortisolism on the developing brain we performed clinical, cognitive, and psychological evaluation of children with Cushing disease (CD) at diagnosis and 1 year after remission. STUDY DESIGN: Prospective study of 41 children with CD. Children completed diverse sets of cognitive measures before and 1 year after remission. Neuropsychological evaluation included the Wechsler Intelligence Scale, California Verbal Learning Test, Trail Making Test, the combined subset scores of Wide Range Achievement Test and Woodcock-Johnson Psychoeducational Battery Test of Achievement, and the Behavioral Assessment System for Children. RESULTS: Comprehensive cognitive evaluations at baseline and 1 year following cure revealed significant decline mostly in nonverbal skills. Decrements occurred in most of the various indices that measure all aspects of cognitive function and younger age and early pubertal stage largely contributed to most of this decline. Results indicated that age at baseline was associated with positive regression weights for changes in scores for verbal, performance, and full intelligence quotient (IQ) scores and for subtests arithmetic, picture completion, coding, block design, scores; indicating that older age at baseline was associated with less of a deterioration in cognitive scores from pre- to posttreatment. CONCLUSION: Our findings suggest that chronic glucocorticoid excess and accompanying secondary hormonal imbalances followed by eucortisolemia have detrimental effects on cognitive function in the developing brain; younger age and pubertal stage are risk factors for increased vulnerability, while older adolescents have cognitive vulnerabilities like that of adult patients affected with CD.

Marginal, conditional, and pseudo likelihood ratio approaches for biomarker combination to predict a binary disease outcome.

It is a common practice in public health research that multiple biomarkers are collected to diagnose or predict a disease outcome. A natural question is how to combine multiple biomarkers to improve the diagnostic accuracy. It has been shown by Neyman-Pearson lemma that the likelihood ratio statistic achieves the optimal AUC in theory. However, practical difficulty often lies in the estimation of the multivariate density functions. We propose three novel methods for the biomarker combination, with the idea of breaking down the joint densities to a series of univariate densities. The marginal likelihood ratio approach only assumes the marginal distribution of each biomarker. While the conditional likelihood ratio (CLR) and pseudo likelihood ratio (PLR) approaches assume the conditional distributions of a marker given others, and hence make use of the correlation structure to estimate the combination rules. The proposed methods make it much easier to assume and validate the univariate distributions of a biomarker than making multivariate distributional assumptions. Extensive simulation studies demonstrate that the CLR and the PLR approaches outperform many existing methods, and are therefore recommended for practical use. The proposed methods are motivated by and applied to a biomarker study to diagnose childhood autism/autism spectrum disorder.

Ppt1-deficiency dysregulates lysosomal Ca++ homeostasis contributing to pathogenesis in a mouse model of CLN1 disease.

Inactivating mutations in the PPT1 gene encoding palmitoyl-protein thioesterase-1 (PPT1) underlie the CLN1 disease, a devastating neurodegenerative lysosomal storage disorder. The mechanism of pathogenesis underlying CLN1 disease has remained elusive. PPT1 is a lysosomal enzyme, which catalyzes the removal of palmitate from S-palmitoylated proteins (constituents of ceroid lipofuscin) facilitating their degradation and clearance by lysosomal hydrolases. Thus, it has been proposed that Ppt1-deficiency leads to lysosomal accumulation of ceroid lipofuscin leading to CLN1 disease. While S-palmitoylation is catalyzed by palmitoyl acyltransferases (called ZDHHCs), palmitoyl-protein thioesterases (PPTs) depalmitoylate these proteins. We sought to determine the mechanism by which Ppt1-deficiency may impair lysosomal degradative function leading to infantile neuronal ceroid lipofuscinosis pathogenesis. Here, we report that in Ppt1-/- mice, which mimic CLN1 disease, low level of inositol 3-phosphate receptor-1 (IP3R1) that mediates Ca++ transport from the endoplasmic reticulum to the lysosome dysregulated lysosomal Ca++ homeostasis. Intriguingly, the transcription factor nuclear factor of activated T-cells, cytoplasmic 4 (NFATC4), which regulates IP3R1-expression, required S-palmitoylation for trafficking from the cytoplasm to the nucleus. We identified two palmitoyl acyltransferases, ZDHHC4 and ZDHHC8, which catalyzed S-palmitoylation of NFATC4. Notably, in Ppt1-/- mice, reduced ZDHHC4 and ZDHHC8 levels markedly lowered S-palmitoylated NFATC4 (active) in the nucleus, which inhibited IP3R1-expression, thereby dysregulating lysosomal Ca++ homeostasis. Consequently, Ca++ -dependent lysosomal enzyme activities were markedly suppressed. Impaired lysosomal degradative function impaired autophagy, which caused lysosomal storage of undigested cargo. Importantly, IP3R1-overexpression in Ppt1-/- mouse fibroblasts ameliorated this defect. Our results reveal a previously unrecognized role of Ppt1 in regulating lysosomal Ca++ homeostasis and suggest that this defect contributes to pathogenesis of CLN1 disease.

Associations of ultra-processed food intake with maternal weight change and cardiometabolic health and infant growth.

BACKGROUND: Excessive intake of ultra-processed foods, formulated from substances extracted from foods or derived from food constituents, may be a modifiable behavioral risk factor for adverse maternal and infant health outcomes. Prior work has predominately examined health correlates of maternal ultra-processed food intake in populations with substantially lower ultra-processed food intake compared to the US population. This longitudinal study investigated relations of ultra-processed food intake with maternal weight change and cardiometabolic health and infant growth in a US cohort. METHODS: Mothers in the Pregnancy Eating Attributes Study were enrolled at ≤12 weeks gestation and completed multiple 24-Hour Dietary Recalls within six visit windows through one-year postpartum (458 mothers enrolled, 321 retained at one-year postpartum). The NOVA (not an acronym) system categorized food and underlying ingredient codes based on processing level. Maternal anthropometrics were measured throughout pregnancy and postpartum, and infant anthropometrics were measured at birth and ages 2 months, 6 months, and 1 year. Maternal cardiometabolic markers were analyzed from blood samples obtained during the second and third trimesters. RESULTS: Holding covariates and total energy intake constant, a 1-SD greater percent energy intake from ultra-processed foods during pregnancy was associated with 31% higher odds of excessive gestational weight gain (p = .045, 95% CI [1.01, 1.70]), 0.68±0.29 mg/L higher c-reactive protein during pregnancy (p = .021, 95% CI [0.10, 1.26]), 6.7±3.4% greater gestational weight gain retained (p = .049, 95% CI [0.03, 13.30]), and 1.09±0.36 kg greater postpartum weight retention (p = .003, 95% CI [0.38, 1.80]). No other significant associations emerged. CONCLUSIONS: Ultra-processed food intake during pregnancy may be a modifiable behavioral risk factor for adverse maternal weight outcomes and inflammation. Randomized controlled trials are needed to test whether targeting ultra-processed food intake during pregnancy may support optimal maternal health. TRIAL REGISTRATION: Clinicaltrials.gov. Registration ID - NCT02217462 . Date of registration - August 13, 2014.

Prospective relations of maternal reward-related eating, pregnancy ultra-processed food intake and weight indicators, and feeding mode with infant appetitive traits.

BACKGROUND: Infant appetitive traits including eating rate, satiety responsiveness, food responsiveness, and enjoyment of food predict weight gain in infancy and early childhood. Although studies show a strong genetic influence on infant appetitive traits, the association of parent and infant appetite is understudied. Furthermore, little research examines the influence of maternal pregnancy dietary intake, weight indicators, and feeding mode on infant appetite. The present study investigated relations of maternal reward-related eating, pregnancy ultra-processed food intake and weight indicators, and feeding mode with infant appetitive traits. METHODS: Mothers in the Pregnancy Eating Attributes Study (458 mothers enrolled, 367 retained through delivery) completed self-report measures of reward-related eating, and principal component analysis yielded two components: (1) food preoccupation and responsiveness and (2) reinforcing value of food. Mothers completed 24-h dietary recalls across pregnancy, and the standardized NOVA (not an acronym) system categorized recalled foods based on processing level. Maternal anthropometrics were measured across pregnancy. At infant age 6 months, mothers reported on feeding mode and infant appetitive traits. Linear regressions were conducted predicting infant appetitive traits from household income-poverty ratio (step 1); maternal reward-related eating components (step 2); pregnancy ultra-processed food intake (% of energy intake), early pregnancy body mass index, and gestational weight gain (step 3); and exclusive breastfeeding duration (step 4). RESULTS: A 1-SD greater maternal food preoccupation and responsiveness was associated with 0.20-SD greater infant satiety responsiveness (p = .005). A 1-SD greater % energy intake from ultra-processed foods during pregnancy was associated with 0.16-SD lower infant satiety responsiveness (p = .031). A 1-SD longer exclusive breastfeeding duration was associated with 0.18-SD less infant food responsiveness (p = .014). Other associations of maternal reward-related eating, pregnancy ultra-processed food intake and weight indicators, and feeding mode with infant appetitive traits were non-significant. CONCLUSIONS: Proximal early-life environmental factors including maternal pregnancy dietary intake and feeding mode may facilitate or protect against obesogenic infant appetitive traits, whereas infant appetite may not parallel maternal reward-related eating. Further investigation into the etiology of appetitive traits early in development, particularly during solid food introduction, may elucidate additional modifiable risk factors for child obesity. TRIAL REGISTRATION: Clinicaltrials.gov. Registration ID - NCT02217462 . Date of registration - August 13, 2014.

DNA Methyltransferase 3A Is Involved in the Sustained Effects of Chronic Stress on Synaptic Functions and Behaviors.

Emerging evidence suggests that epigenetic mechanisms regulate aberrant gene transcription in stress-associated mental disorders. However, it remains to be elucidated about the role of DNA methylation and its catalyzing enzymes, DNA methyltransferases (DNMTs), in this process. Here, we found that male rats exposed to chronic (2-week) unpredictable stress exhibited a substantial reduction of Dnmt3a after stress cessation in the prefrontal cortex (PFC), a key target region of stress. Treatment of unstressed control rats with DNMT inhibitors recapitulated the effect of chronic unpredictable stress on decreased AMPAR expression and function in PFC. In contrast, overexpression of Dnmt3a in PFC of stressed animals prevented the loss of glutamatergic responses. Moreover, the stress-induced behavioral abnormalities, including the impaired recognition memory, heightened aggression, and hyperlocomotion, were partially attenuated by Dnmt3a expression in PFC of stressed animals. Finally, we found that there were genome-wide DNA methylation changes and transcriptome alterations in PFC of stressed rats, both of which were enriched at several neural pathways, including glutamatergic synapse and microtubule-associated protein kinase signaling. These results have therefore recognized the potential role of DNA epigenetic modification in stress-induced disturbance of synaptic functions and cognitive and emotional processes.

Exosomal Carboxypeptidase E (CPE) and CPE-shRNA-Loaded Exosomes Regulate Metastatic Phenotype of Tumor Cells.

BACKGROUND: Exosomes promote tumor growth and metastasis through intercellular communication, although the mechanism remains elusive. Carboxypeptidase E (CPE) supports the progression of different cancers, including hepatocellular carcinoma (HCC). Here, we investigated whether CPE is the bioactive cargo within exosomes, and whether it contributes to tumorigenesis, using HCC cell lines as a cancer model. METHODS: Exosomes were isolated from supernatant media of cancer cells, or human sera. mRNA and protein expression were analyzed using PCR and Western blot. Low-metastatic HCC97L cells were incubated with exosomes derived from high-metastatic HCC97H cells. In other experiments, HCC97H cells were incubated with CPE-shRNA-loaded exosomes. Cell proliferation and invasion were assessed using MTT, colony formation, and matrigel invasion assays. RESULTS: Exosomes released from cancer cells contain CPE mRNA and protein. CPE mRNA levels are enriched in exosomes secreted from high- versus low-metastastic cells, across various cancer types. In a pilot study, significantly higher CPE copy numbers were found in serum exosomes from cancer patients compared to healthy subjects. HCC97L cells, treated with exosomes derived from HCC97H cells, displayed enhanced proliferation and invasion; however, exosomes from HCC97H cells pre-treated with CPE-shRNA failed to promote proliferation. When HEK293T exosomes loaded with CPE-shRNA were incubated with HCC97H cells, the expression of CPE, Cyclin D1, a cell-cycle regulatory protein and c-myc, a proto-oncogene, were suppressed, resulting in the diminished proliferation of HCC97H cells. CONCLUSIONS: We identified CPE as an exosomal bioactive molecule driving the growth and invasion of low-metastatic HCC cells. CPE-shRNA loaded exosomes can inhibit malignant tumor cell proliferation via Cyclin D1 and c-MYC suppression. Thus, CPE is a key player in the exosome transmission of tumorigenesis, and the exosome-based delivery of CPE-shRNA offers a potential treatment for tumor progression. Notably, measuring CPE transcript levels in serum exosomes from cancer patients could have potential liquid biopsy applications.

A multiple robust propensity score method for longitudinal analysis with intermittent missing data.

Longitudinal data are very popular in practice, but they are often missing in either outcomes or time-dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semiparametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood-based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.

On the design and the analysis of stratified biomarker trials in the presence of measurement error.

A major emphasis in precision medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. And as such, enormous resources and innovative clinical trials designs in oncology are devoted to identifying predictive biomarkers. Predictive biomarkers are ones that will identify patients that are more likely to respond to specific therapies and they are usually discovered through retrospective analysis from large randomized phase II or phase III trials. One important design to consider is the stratified biomarker design, where patients will have their specimens obtained at baseline and the biomarker status will be assessed prior to random assignment. Regardless of their biomarker status, patients will be randomized to either an experimental arm or the standard of care arm. The stratified biomarker design can be used to test for a treatment-biomarker interaction in predicting a time-to event outcome. Many biomarkers, however, are derived from tissues from patients, and their levels may be heterogeneous. As a result, biomarker levels may be measured with error and this would have an adverse impact on the power of a stratified biomarker clinical trial. We present a trial design and an analysis framework for the stratified biomarker design. We show that the naïve test is biased and provide bias-corrected estimators for computing the sample size and the 95% confidence interval when testing for a treatment-biomarker interaction in predicting a time to event outcome. We propose a sample size formula that adjusts for misclassification and apply it in the design of a phase III clinical trial in renal cancer.

Error-corrected estimation of a diagnostic accuracy index of a biomarker against a continuous gold standard.

This article concerns evaluating the effectiveness of a continuous diagnostic biomarker against a continuous gold standard that is measured with error. Extending the work of Obuchowski (2005, 2016), Wu et al (2016) suggested an accuracy index and proposed an estimator for the index with error-prone standard when the reliability coefficient is known. Combining with additional measurements (without measurement errors) on the continuous gold standard collected from some subjects, this article proposes two adaptive estimators of the accuracy index when the reliability coefficient is unknown, and further establish the consistency and asymptotic normality of these estimators. Simulation studies are conducted to compare various estimators. Data from an intervention trial on glycemic control among children with type 1 diabetes are used to illustrate the proposed methods.