Sepsis-trained macrophages promote antitumoral tissue-resident T cells.

Sepsis induces immune alterations, which last for months after the resolution of illness. The effect of this immunological reprogramming on the risk of developing cancer is unclear. Here we use a national claims database to show that sepsis survivors had a lower cumulative incidence of cancers than matched nonsevere infection survivors. We identify a chemokine network released from sepsis-trained resident macrophages that triggers tissue residency of T cells via CCR2 and CXCR6 stimulations as the immune mechanism responsible for this decreased risk of de novo tumor development after sepsis cure. While nonseptic inflammation does not provoke this network, laminarin injection could therapeutically reproduce the protective sepsis effect. This chemokine network and CXCR6 tissue-resident T cell accumulation were detected in humans with sepsis and were associated with prolonged survival in humans with cancer. These findings identify a therapeutically relevant antitumor consequence of sepsis-induced trained immunity.

Latent pathway-based Bayesian models to identify intervenable factors of racial disparities in breast cancer stage at diagnosis.

PURPOSE: We built Bayesian Network (BN) models to explain roles of different patient-specific factors affecting racial differences in breast cancer stage at diagnosis, and to identify healthcare related factors that can be intervened to reduce racial health disparities. METHODS: We studied women age 67-74 with initial diagnosis of breast cancer during 2006-2014 in the National Cancer Institute's SEER-Medicare dataset. Our models included four measured variables (tumor grade, hormone receptor status, screening utilization and biopsy delay) expressed through two latent pathways-a tumor biology path, and health-care access/utilization path. We used various Bayesian model assessment tools to evaluate these two latent pathways as well as each of the four measured variables in explaining racial disparities in stage-at-diagnosis. RESULTS: Among 3,010 Black non-Hispanic (NH) and 30,310 White NH breast cancer patients, respectively 70.2% vs 76.9% were initially diagnosed at local stage, 25.3% vs 20.3% with regional stage, and 4.56% vs 2.80% with distant stage-at-diagnosis. Overall, BN performed approximately 4.7 times better than Classification And Regression Tree (CART) (Breiman L, Friedman JH, Stone CJ, Olshen RA. Classification and regression trees. CRC press; 1984) in predicting stage-at-diagnosis. The utilization of screening mammography is the most prominent contributor to the accuracy of the BN model. Hormone receptor (HR) status and tumor grade are useful for explaining racial disparity in stage-at diagnosis, while log-delay in biopsy impeded good prediction. CONCLUSIONS: Mammography utilization had a significant effect on racial differences in breast cancer stage-at-diagnosis, while tumor biology factors had less impact. Biopsy delay also aided in predicting local and regional stages-at-diagnosis for Black NH women but not for white NH women.

A cluster of broadly neutralizing IgG against BK polyomavirus in a repertoire dominated by IgM.

The BK polyomavirus (BKPyV) is an opportunistic pathogen, which is only pathogenic in immunosuppressed individuals, such as kidney transplant recipients, in whom BKPyV can cause significant morbidity. To identify broadly neutralizing antibodies against this virus, we used fluorescence-labeled BKPyV virus-like particles to sort BKPyV-specific B cells from the PBMC of KTx recipients, then single-cell RNAseq to obtain paired heavy- and light-chain antibody sequences from 2,106 sorted B cells. The BKPyV-specific repertoire was highly diverse in terms of both V-gene usage and clonotype diversity and included most of the IgM B cells, including many with extensive somatic hypermutation. In two patients where sufficient data were available, IgM B cells in the BKPyV-specific dataset had significant differences in V-gene usage compared with IgG B cells from the same patient. CDR3 sequence-based clustering allowed us to identify and characterize three broadly neutralizing "41F17-like" clonotypes that were predominantly IgG, suggesting that some specific BKPyV capsid epitopes are preferentially targeted by IgG.

Bayesian additive regression trees for multivariate skewed responses.

This paper introduces a nonparametric regression approach for univariate and multivariate skewed responses using Bayesian additive regression trees (BART). Existing BART methods use ensembles of decision trees to model a mean function, and have become popular recently due to their high prediction accuracy and ease of use. The usual assumption of a univariate Gaussian error distribution, however, is restrictive in many biomedical applications. Motivated by an oral health study, we provide a useful extension of BART, the skewBART model, to address this problem. We then extend skewBART to allow for multivariate responses, with information shared across the decision trees associated with different responses within the same subject. The methodology accommodates within-subject association, and allows varying skewness parameters for the varying multivariate responses. We illustrate the benefits of our multivariate skewBART proposal over existing alternatives via simulation studies and application to the oral health dataset with bivariate highly skewed responses. Our methodology is implementable via the R package skewBART, available on GitHub.

Bayesian regression analysis of skewed tensor responses.

Tensor regression analysis is finding vast emerging applications in a variety of clinical settings, including neuroimaging, genomics, and dental medicine. The motivation for this paper is a study of periodontal disease (PD) with an order-3 tensor response: multiple biomarkers measured at prespecified tooth-sites within each tooth, for each participant. A careful investigation would reveal considerable skewness in the responses, in addition to response missingness. To mitigate the shortcomings of existing analysis tools, we propose a new Bayesian tensor response regression method that facilitates interpretation of covariate effects on both marginal and joint distributions of highly skewed tensor responses, and accommodates missing-at-random responses under a closure property of our tensor model. Furthermore, we present a prudent evaluation of the overall covariate effects while identifying their possible variations on only a sparse subset of the tensor components. Our method promises Markov chain Monte Carlo (MCMC) tools that are readily implementable. We illustrate substantial advantages of our proposal over existing methods via simulation studies and application to a real data set derived from a clinical study of PD. The R package BSTN available in GitHub implements our model.

CLEC-1 is a death sensor that limits antigen cross-presentation by dendritic cells and represents a target for cancer immunotherapy.

Tumors exploit numerous immune checkpoints, including those deployed by myeloid cells to curtail antitumor immunity. Here, we show that the C-type lectin receptor CLEC-1 expressed by myeloid cells senses dead cells killed by programmed necrosis. Moreover, we identified Tripartite Motif Containing 21 (TRIM21) as an endogenous ligand overexpressed in various cancers. We observed that the combination of CLEC-1 blockade with chemotherapy prolonged mouse survival in tumor models. Loss of CLEC-1 reduced the accumulation of immunosuppressive myeloid cells in tumors and invigorated the activation state of dendritic cells (DCs), thereby increasing T cell responses. Mechanistically, we found that the absence of CLEC-1 increased the cross-presentation of dead cell-associated antigens by conventional type-1 DCs. We identified antihuman CLEC-1 antagonist antibodies able to enhance antitumor immunity in CLEC-1 humanized mice. Together, our results demonstrate that CLEC-1 acts as an immune checkpoint in myeloid cells and support CLEC-1 as a novel target for cancer immunotherapy.

Median regression models for clustered, interval-censored survival data - An application to prostate surgery study.

Genitourinary surgeons and oncologists are particularly interested in whether a robotic surgery improves times to Prostate Specific Antigen (PSA) recurrence compared to a non-robotic surgery for removing the cancerous prostate. Time to PSA recurrence is an example of a survival time that is typically interval-censored between two consecutive clinical inspections with opposite test results. In addition, success of medical devices and technologies often depends on factors such as experience and skill level of the medical service providers, thus leading to clustering of these survival times. For analyzing the effects of surgery types and other covariates on median of clustered interval-censored time to post-surgery PSA recurrence, we present three competing novel models and associated frequentist and Bayesian analyses. The first model is based on a transform-both-sides of survival time with Gaussian random effects to account for the within-cluster association. Our second model assumes an approximate marginal Laplace distribution for the transformed log-survival times with a Gaussian copula to accommodate clustering. Our third model is a special case of the second model with Laplace distribution for the marginal log-survival times and Gaussian copula for the within-cluster association. Simulation studies establish the second model to be highly robust against extreme observations while estimating median regression coefficients. We provide a comprehensive comparison among these three competing models based on the model properties and the computational ease of their Frequentist and Bayesian analysis. We also illustrate the practical implementations and uses of these methods via analysis of a simulated clustered interval-censored data-set similar in design to a post-surgery PSA recurrence study.

Rural-Urban Differences in Breast Cancer Stage at Diagnosis.

Purpose: To analyze the extent to which rural-urban differences in breast cancer stage at diagnosis are explained by factors including age, race, tumor grade, receptor status, and insurance status. Methods: Using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 18 database, analysis was performed using data from women aged 50-74 diagnosed with breast cancer between the years 2013 and 2016. Patient rurality of residence was coded according to SEER's Rural-Urban Continuum Code 2013: Large Urban (RUCC 1), Small Urban (RUCC 2,3), and Rural (RUCC 4,5,6,7,8,9). Stage at diagnosis was coded according to SEER's Combined Summary Stage 2000 (2004+) criteria: Localized (0,1), Regional (2,3,4,5), and Distant (7). Descriptive statistics were analyzed, and variations were tested for across rural-urban categories using Kruskall-Wallis and Kendall's tau-b tests. Additionally, odds ratios (ORs) and 95% confidence intervals for the three ordinal levels of rural-urban residence were calculated while adjusting for other independent variables using ordinal logistic regression. Results: The rural residence category showed the largest proportion of women diagnosed with distant stage breast cancer. Additionally, we determined that patients with residence in both large and small urban areas had statistically significantly lower odds of higher stage diagnosis compared to rural patients even after controlling for age, race, tumor grade, receptor status, and insurance status. Conclusions: Rural women with breast cancer show small but statistically significant disparities in stage-at-diagnosis. Further research is needed to understand local area variation in these disparities across a wide range of rural communities, and to identify the most effective interventions to eliminate these disparities.

A note on median regression for complex surveys.

There is a great need for statistical methods for analyzing skewed responses in complex sample surveys. Quantile regression is a logical option in addressing this problem but is often accompanied by incorrect variance estimation. We show how the variance can be estimated correctly by including the survey design in the variance estimation process. In a simulation study, we illustrate that the variance of the median regression estimator has a very small relative bias with appropriate coverage probability. The motivation for our work stems from the National Health and Nutrition Examination Survey where we demonstrate the impact of our results on iodine deficiency in females compared with males adjusting for other covariates.

Semiparametric analysis of clustered interval-censored survival data using soft Bayesian additive regression trees (SBART).

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval-censored survival data under a paradigm of Bayesian ensemble learning, called soft Bayesian additive regression trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left-censored, right-censored, and interval-censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high-dimensional data with complex underlying associations.

Naïve Bayesian network-based contribution analysis of tumor biology and healthcare factors to racial disparity in breast cancer stage-at-diagnosis.

BACKGROUND: Variation in breast cancer stage at initial diagnosis (including racial disparities) is driven both by tumor biology and healthcare factors. METHODS: We studied women age 67-74 with initial diagnosis of breast cancer from 2006 through 2014 in the SEER-Medicare database. We extracted variables related to tumor biology (histologic grade and hormone receptor status) and healthcare factors (screening mammography [SM] utilization and time delay from mammography to diagnostic biopsy). We used naïve Bayesian networks (NBNs) to illustrate the relationships among patient-specific factors and stage-at-diagnosis for African American (AA) and white patients separately. After identifying and controlling confounders, we conducted counterfactual inference through the NBN, resulting in an unbiased evaluation of the causal effects of individual factors on the expected utility of stage-at-diagnosis. An NBN-based decomposition mechanism was developed to evaluate the contributions of each patient-specific factor to an actual racial disparity in stage-at-diagnosis. 2000 bootstrap samples from our training patients were used to compute the 95% confidence intervals (CIs) of these contributions. RESULTS: Using a causal-effect contribution analysis, the relative contributions of each patient-specific factor to the actual racial disparity in stage-at-diagnosis were as follows: tumor grade, 45.1% (95% CI: 44.5%, 45.8%); hormone receptor status, 5.0% (4.5%, 5.4%); mammography utilization, 23.1% (22.4%, 24.0%); and biopsy delay 26.8% (26.1%, 27.3%). CONCLUSION: The modifiable mechanisms of mammography utilization and biopsy delay drive about 49.9% of racial difference in stage-at-diagnosis, potentially guiding more targeted interventions to eliminate cancer outcome disparities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00165-5.

Bayesian analysis of survival data with missing censoring indicators.

In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to survival data with missing censoring indicators where the probability of missing may depend on time of last monitoring and some covariates. We present a fully Bayesian semi-parametric method for such survival data to estimate regression parameters of the proportional hazards model of Cox. Theoretical investigation and simulation studies show that our method performs better than competing methods. We apply the proposed method to analyze the survival data with missing censoring indicators from the Orofacial Pain: Prospective Evaluation and Risk Assessment study.

Joint analysis of recurrence and termination: A Bayesian latent class approach.

Like many other clinical and economic studies, each subject of our motivating transplant study is at risk of recurrent events of non-fatal tissue rejections as well as the terminating event of death due to total graft rejection. For such studies, our model and associated Bayesian analysis aim for some practical advantages over competing methods. Our semiparametric latent-class-based joint model has coherent interpretation of the covariate (including race and gender) effects on all functions and model quantities that are relevant for understanding the effects of covariates on future event trajectories. Our fully Bayesian method for estimation and prediction uses a complete specification of the prior process of the baseline functions. We also derive a practical and theoretically justifiable partial likelihood-based semiparametric Bayesian approach to deal with the analysis when there is a lack of prior information about baseline functions. Our model and method can accommodate fixed as well as time-varying covariates. Our Markov Chain Monte Carlo tools for both Bayesian methods are implementable via publicly available software. Our Bayesian analysis of transplant study and simulation study demonstrate practical advantages and improved performance of our approach.

Using Cox regression to develop linear rank tests with zero-inflated clustered data.

Zero-inflated data arise in many fields of study. When comparing zero-inflated data between two groups with independent subjects, a two degree-of-freedom test has been developed, which is the sum of a 1 degree-of-freedom Pearson chi-square test for the 2×2 table of group vs dichotomized outcome (0,> 0) and a 1 degree-of-freedom Wilcoxon rank-sum test for the values of the outcome > 0. Here, we extend this 2 degree-of-freedom test to clustered data settings. We first propose using an estimating equations score statistic from a time-varying weighted Cox regression model under naive independence, with a robust sandwich variance estimator to account for clustering. Since our proposed test statistics can be put in the framework of a Cox model, to gain efficiency over naive independence, we apply a generalized estimating equations (GEE) Cox model with a non-independence 'working correlation' between observations in a cluster. The proposed methods are applied to a General Social Survey study of days with mental health problems in a month, in which 52.3% of subjects report they have no days with problems, a zero-inflated outcome. A simulation study is used to compare our proposed test statistics to previously proposed zero-inflated test statistics.

A New Bayesian Single Index Model with or without Covariates Missing at Random.

For many biomedical, environmental, and economic studies, the single index model provides a practical dimension reaction as well as a good physical interpretation of the unknown nonlinear relationship between the response and its multiple predictors. However, widespread uses of existing Bayesian analysis for such models are lacking in practice due to some major impediments, including slow mixing of the Markov Chain Monte Carlo (MCMC), the inability to deal with missing covariates and a lack of theoretical justification of the rate of convergence of Bayesian estimates. We present a new Bayesian single index model with an associated MCMC algorithm that incorporates an efficient Metropolis-Hastings (MH) step for the conditional distribution of the index vector. Our method leads to a model with good interpretations and prediction, implementable Bayesian inference, fast convergence of the MCMC and a first-time extension to accommodate missing covariates. We also obtain, for the first time, the set of sufficient conditions for obtaining the optimal rate of posterior convergence of the overall regression function. We illustrate the practical advantages of our method and computational tool via reanalysis of an environmental study.

Semiparametric mixed-scale models using shared Bayesian forests.

This paper demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems including multivariate, heteroscedastic, and semicontinuous responses. In this paper, we present a methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum-of-tree models. Our simulation results demonstrate that sharing of information across related model components is often very beneficial, particularly in sparse high-dimensional problems in which variable selection must be conducted. We illustrate our methodology by analyzing medical expenditure data from the Medical Expenditure Panel Survey (MEPS). To facilitate the Bayesian nonparametric regression analysis, we develop two novel models for analyzing the MEPS data using Bayesian additive regression trees-a heteroskedastic log-normal hurdle model with a "shrink-toward-homoskedasticity" prior and a gamma hurdle model.

Improved dropClust R package with integrative analysis support for scRNA-seq data.

SUMMARY: DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. Here we present the improved dropClust, a complete R package that is, fast, interoperable and minimally resource intensive. The new dropClust features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets. AVAILABILITY AND IMPLEMENTATION: dropClust is freely available at https://github.com/debsin/dropClust as an R package. A lightweight online version of the dropClust is available at https://debsinha.shinyapps.io/dropClust/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Semiparametric Bayesian latent variable regression for skewed multivariate data.

For many real-life studies with skewed multivariate responses, the level of skewness and association structure assumptions are essential for evaluating the covariate effects on the response and its predictive distribution. We present a novel semiparametric multivariate model and associated Bayesian analysis for multivariate skewed responses. Similar to multivariate Gaussian densities, this multivariate model is closed under marginalization, allows a wide class of multivariate associations, and has meaningful physical interpretations of skewness levels and covariate effects on the marginal density. Other desirable properties of our model include the Markov Chain Monte Carlo computation through available statistical software, and the assurance of consistent Bayesian estimates of the parameters and the nonparametric error density under a set of plausible prior assumptions. We illustrate the practical advantages of our methods over existing alternatives via simulation studies and the analysis of a clinical study on periodontal disease.

Structure-Aware Principal Component Analysis for Single-Cell RNA-seq Data.

With the emergence of droplet-based technologies, it has now become possible to profile transcriptomes of several thousands of cells in a day. Although such a large single-cell cohort may favor the discovery of cellular heterogeneity, it also brings new challenges in the prediction of minority cell types. Identification of any minority cell type holds a special significance in knowledge discovery. In the analysis of single-cell expression data, the use of principal component analysis (PCA) is surprisingly frequent for dimension reduction. The principal directions obtained from PCA are usually dominated by the major cell types in the concerned tissue. Thus, it is very likely that using a traditional PCA may endanger the discovery of minority populations. To this end, we propose locality-sensitive PCA (LSPCA), a scalable variant of PCA equipped with structure-aware data sampling at its core. Structure-aware sampling provides PCA with a neutral spread of the data, thereby reducing the bias in its principal directions arising from the redundant samples in a data set. We benchmarked the performance of the proposed method on ten publicly available single-cell expression data sets including one very large annotated data set. Results have been compared with traditional PCA and PCA with random sampling. Clustering results on the annotated data sets also show that LSPCA can detect the minority populations with a higher accuracy.