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.

Canonical correlation analysis (CCA) is one of popular statistical methodologies in multivariate analysis, especially, in studying relation of two sets of variables. However, if sample sizes are smaller than the maximum of the dimensions of two sets of variables, it is not plausible to construct canonical coefficient matrices due to failure of inverting sample covariance matrices. In this article, we develop a two step procedure of CCA implemented in such situation. For this, seeded dimension reduction is adapted into CCA. Numerical studies confirm the approach, and two real data analyses are presented. Copyright © 2014 John Wiley & Sons, Ltd.

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Canonical correlation analysis (CCA) has a long history as an explanatory statistical method in high-dimensional data analysis and has been successfully applied in many scientific fields such as chemometrics, pattern recognition, genomic sequence analysis, and so on. The so-called seedCCA is a newly developed R package that implements not only the standard and seeded CCA but also partial least squares. The package enables us to fit CCA to large-p and small-n data. The paper provides a complete guide. Also, the seeded CCA application results are compared with the regularized CCA in the existing R package. It is believed that the package, along with the paper, will contribute to highdimensional data analysis in various science field practitioners and that the statistical methodologies in multivariate analysis become more fruitful.

Background: The value of immune checkpoint inhibitors (PD1/PDL1 inhibitors; ICI) in treating prostate cancer (PC) is limited. We examined data from US Veterans with PC to assess disease response to ICIs as monotherapy or combined with abiraterone or enzalutamide. We compared results with reference datasets to assess ICI efficacy in the real-world. Methods: 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. Results: We identified 787 Veterans with PC and ≥1 PSA measurement while receiving an ICI. The 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. Conclusion: Our data align with clinical trial data in PC, demonstrating limited benefit from ICI monotherapy and predicting no survival benefit from simultaneous administration of abiraterone or enzalutamide with an ICI using g-rate. We demonstrate the value of estimating g-rates and of our reference database in approaching challenging clinical questions and as aids in drug development.