Assessment of PSA responses and changes in the rate of tumor growth (g-rate) with immune checkpoint inhibitors in US Veterans with prostate cancer.

We examined data from US Veterans with prostate cancer (PC) to assess disease response to immune checkpoint inhibitors (ICI) as monotherapy or combined with abiraterone or enzalutamide to assess ICI efficacy in the real-world. We queried the VA corporate data warehouse (CDW) to identify Veterans with a diagnosis of PC who received ICI for any malignancy and had ≥1 PSA measurement while receiving ICI. To evaluate ICI monotherapy, we restricted analysis to Veterans who had not received LHRH agonists/antagonists, PC-directed medical therapy, or radiation/extirpative surgery of the bladder/prostate within and preceding the duration of ICI administration. For ICI combination analysis, we identified Veterans who received abiraterone or enzalutamide for PC while on ICI. We calculated rates of tumor (PSA) growth (g-rates), comparing them to a 1:2 matched reference cohort. We identified 787 Veterans with PC and ≥1 PSA measurement while receiving an ICI. Median duration of ICI therapy was 155 days. 223 Veterans received ICI monotherapy, with only 17(8%) having a reduction in PSA (median decline = 43%). 12 (5%) had PSA declines >30% (PSA30) which included 6 (3%) who had PSA reductions greater than 50% (PSA50). Median g-rates for ICI plus abiraterone (n = 20) or enzalutamide (n = 31) were 0.000689/d-1 and 0.002819/d-1, respectively, and were statistically insignificant compared to g-rates of matched cohorts receiving abiraterone (g = 0.000925/d-1, P = 0.73) or enzalutamide (g = 0.001929/d-1, P = 0.58) alone. Our data align with clinical trial data in PC, demonstrating limited benefit from ICI monotherapy and predicting no survival benefit from simultaneous abiraterone or enzalutamide with an ICI using g-rate.

Partial Hepatectomy and Ablation for Survival of Early-Stage Hepatocellular Carcinoma Patients: A Bayesian Emulation Analysis.

Partial hepatectomy and ablation therapy are two widely used surgical procedures for localized early-stage hepatocellular carcinoma (HCC) patients. This article aimed to evaluate their relative effectiveness in terms of overall survival. An emulation analysis approach was first developed based on the Bayesian technique. We estimated propensity scores via Bayesian logistic regression and adopted a weighted Bayesian Weibull accelerated failure time (AFT) model incorporating prior information contained in the published literature. With the Surveillance, Epidemiology, and End Results (SEER)-Medicare data, an emulated target trial with rigorously defined inclusion/exclusion criteria and treatment regimens for early-stage HCC patients over 66 years old was developed. For the main cohort with tumor size less than or equal to 5 cm, a total of 1146 patients were enrolled in the emulated trial, with 301 and 845 in the partial hepatectomy and ablation arms, respectively. The analysis suggested ablation to be significantly associated with inferior overall survival (hazard ratio [HR] = 1.35; 95% credible interval [CrI]: 1.14, 1.60). For the subgroup with tumor size less than or equal to 3 cm, there was no significant difference in overall survival between the two arms (HR = 1.15; 95% CrI: 0.88, 1.52). Overall, the comparative treatment effect of ablation and partial hepatectomy on survival remains inconclusive. This finding may provide further insight into HCC clinical treatment.

Bayesian finite mixture of regression analysis for cancer based on histopathological imaging-environment interactions.

Cancer is a heterogeneous disease. Finite mixture of regression (FMR)-as an important heterogeneity analysis technique when an outcome variable is present-has been extensively employed in cancer research, revealing important differences in the associations between a cancer outcome/phenotype and covariates. Cancer FMR analysis has been based on clinical, demographic, and omics variables. A relatively recent and alternative source of data comes from histopathological images. Histopathological images have been long used for cancer diagnosis and staging. Recently, it has been shown that high-dimensional histopathological image features, which are extracted using automated digital image processing pipelines, are effective for modeling cancer outcomes/phenotypes. Histopathological imaging-environment interaction analysis has been further developed to expand the scope of cancer modeling and histopathological imaging-based analysis. Motivated by the significance of cancer FMR analysis and a still strong demand for more effective methods, in this article, we take the natural next step and conduct cancer FMR analysis based on models that incorporate low-dimensional clinical/demographic/environmental variables, high-dimensional imaging features, as well as their interactions. Complementary to many of the existing studies, we develop a Bayesian approach for accommodating high dimensionality, screening out noises, identifying signals, and respecting the "main effects, interactions" variable selection hierarchy. An effective computational algorithm is developed, and simulation shows advantageous performance of the proposed approach. The analysis of The Cancer Genome Atlas data on lung squamous cell cancer leads to interesting findings different from the alternative approaches.

HIV viral transcription and immune perturbations in the CNS of people with HIV despite ART.

People with HIV (PWH) on antiretroviral therapy (ART) experience elevated rates of neurological impairment, despite controlling for demographic factors and comorbidities, suggesting viral or neuroimmune etiologies for these deficits. Here, we apply multimodal and cross-compartmental single-cell analyses of paired cerebrospinal fluid (CSF) and peripheral blood in PWH and uninfected controls. We demonstrate that a subset of central memory CD4+ T cells in the CSF produced HIV-1 RNA, despite apparent systemic viral suppression, and that HIV-1-infected cells were more frequently found in the CSF than in the blood. Using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), we show that the cell surface marker CD204 is a reliable marker for rare microglia-like cells in the CSF, which have been implicated in HIV neuropathogenesis, but which we did not find to contain HIV transcripts. Through a feature selection method for supervised deep learning of single-cell transcriptomes, we find that abnormal CD8+ T cell activation, rather than CD4+ T cell abnormalities, predominated in the CSF of PWH compared with controls. Overall, these findings suggest ongoing CNS viral persistence and compartmentalized CNS neuroimmune effects of HIV infection during ART and demonstrate the power of single-cell studies of CSF to better understand the CNS reservoir during HIV infection.

Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis.

Cancer is heterogeneous, and for seemingly similar cancer patients, the associations between an outcome/phenotype and covariates can be different. To describe such differences, finite mixture of regression (FMR) and other modeling techniques have been developed. "Classic" FMR analysis has usually been based on clinical, demographic, and molecular variables. More recently, histopathological imaging data-which is a byproduct of biopsy and enjoys broader data availability and higher cost-effectiveness-has been increasingly used in cancer modeling, although it is noted that its application to cancer FMR analysis still remains limited. In this article, we further advance cancer FMR analysis based on histopathological imaging data. Significantly advancing from the existing analyses under heterogeneity and homogeneity, our goal is to simultaneously use two types of histopathological imaging features, which are extracted based on domain-specific biomedical knowledge and using automated signal processing software, respectively. A significant modeling/methodological advancement is that, to reflect the "increased resolution" of the second type of imaging features over the first type, we impose a hierarchy in the mixture structures. An effective and flexible Bayesian approach is proposed. Simulation shows its competitiveness over several alternatives. The TCGA lung cancer data is analyzed, and interesting heterogeneous structures different from using the alternatives are found. Overall, this study provides a new venue for FMR analysis for cancer and other complex diseases.