Jichun Xie

In many applications, a large number of features are collected with the goal to identify a few important ones. Sometimes, these features lie in a metric space with a known distance matrix, which partially reflects their co-importance pattern. Proper use of the distance matrix will boost the power of identifying important features. Hence, we develop a new multiple testing framework named the Distance Assisted Recursive Testing (DART). DART has two stages. In stage 1, we transform the distance matrix into an aggregation tree, where each node represents a set of features. In stage 2, based on the aggregation tree, we set up dynamic node hypotheses and perform multiple testing on the tree. All rejections are mapped back to the features. Under mild assumptions, the false discovery proportion of DART converges to the desired level in high probability converging to one. We illustrate by theory and simulations that DART has superior performance under various models compared to the existing methods. We applied DART to a clinical trial in the allogeneic stem cell transplantation study to identify the gut microbiota whose abundance will be impacted by the after-transplant care.

Rationale: Hypercapnia develops in one third of patients with advanced chronic obstructive pulmonary disease (COPD) and is associated with increased morbidity and mortality. Multiple factors in COPD are thought to contribute to the development of hypercapnia including increased carbon dioxide (CO2) production, increased dead space ventilation, and the complex interactions of deranged respiratory system mechanics, inspiratory muscle overload and the ventilatory control center in the brainstem. However, these factors have not previously been systematically analyzed in a large, well-characterized population of severe COPD patients. Methods: This is a secondary analysis of the clinical, physiologic and imaging data from the National Emphysema Treatment Trial (NETT). All patients with complete baseline data for the key predictor variables were included. An inclusive list of 32 potential predictor variables were selected a priori based on consensus of the investigators and literature review. Stepwise variable selection yielded 10 statistically significant associations in multivariate regression. Results: A total of 1419 patients with severe COPD were included in the analysis; mean age 66.4 years (standard deviation 6.3), 38% females, and 422 (29.7%) had baseline hypercapnia. Key variables associated with hypercapnia were low resting partial pressure of oxygen in blood, low minute ventilation (Ve), high volume of exhaled carbon dioxide, low forced expiratory volume in 1 second, high residual volume, lower % emphysema on chest computed tomography, use of oxygen, low ventilatory reserve (high Ve/maximal voluntary ventilation), and not being at high altitude. Low diffusing capacity for carbon monoxide showed a positive association with hypercapnia in univariate analysis but a negative correlation in multivariate analysis. Measures of dyspnea and quality of life did not associate with degree of hypercapnia in multivariable analysis. Conclusion: Hypercapnia in a well-characterized cohort with severe COPD and emphysema is chiefly related to poor lung mechanics, high CO2 production, and a reduced ventilatory capability. Hypercapnia is less impacted by gas exchange abnormalities or the presence of emphysema.

<jats:p>While B cells are known to contribute to the pathogenesis of chronic graft-versus-host disease (cGVHD) in mice, it has been challenging to elucidate intrinsic mechanisms of tolerance loss in patients. To identify distinct and potentially targetable B-cell subsets in cGVHD, we employed single-cell RNA-Seq along with an unsupervised hierarchical clustering analysis, targeting 10,000 single B cells from each of eight patients who were &amp;gt;12 months post-allogeneic hematopoietic stem cell transplantation (HCT) and either had active cGVHD manifestations (n=4) or never developed cGVHD (n=4). Bioinformatics analysis of pooled cell data (using R with Seurat extension package) identified 6 major B cell clusters common to all patients (Figure 1A). "Intra-cluster" gene comparison (using R package DESeq2, false-discovery rate 0.05) revealed numerous differentially expressed genes between patient groups. The greatest number of differentially-expressed genes occurred in a cluster referred to herein as 'Cluster 6' (Figure 1A, in yellow with asterisk). Within Cluster 6, B cells from active cGVHD patients expressed significantly increased ITGAX (CD11c, Padj =0.007), TNFRSF13B (TACI, a receptor for BAFF, Padj =0.003), IGHG1 (IgG1, Padj =9.3e-06) and IGHG3 (IgG3, Padj =1.7e-12), along with 44 additional genes (to be discussed). Thus, Cluster 6 in cGVHD patients may represent a CD11cpos, BAFF-responsive B cell subset primed to undergo isotype switching in response to alloantigen. Flow cytometry analysis on PBMCs from an independent HCT patient cohort (n=10) confirmed that CD11cpos B cells were indeed significantly expanded in cGVHD (P &amp;lt; 0.01, Figure 1B), and revealed these B cells were also TACIpos, CD19high, forward scatter high (FSChigh) blast-like cells (Figure 1C). We found that these CD11cpos B cells had mixed expression of CD21, CD27, IgD and CD24 (Figure 1C). Remarkably, other recent studies on bulk patient B cells have suggested that similar CD11cposCD21negCD19highT-BETpos cells are critical drivers of humoral autoimmunity in diseases including systemic lupus erythematosus (SLE; Scharer et al. 2019; Rubtsova et al. 2017; Rubtsov et al. 2011). This subset now identified by single-cell RNA-Seq is consistent with a population of TACIhigh B cells that produced IgG in response to BAFF treatment ex vivo (Sarantopoulos 2009). Data suggest we have identified functionally distinct and potentially targetable B cell subpopulations. We are employing functional assays to determine whether the additional molecular pathways now elucidated account for our previous work showing greater ex vivo B cell survival rates and hyper-responsiveness to surrogate antigen (Allen et al. 2012, 2014), certain TLR agonists (Suthers et al. 2017), and NOTCH ligand (Poe et al. 2017).</jats:p> <jats:p>In addition to more deeply characterizing B-cell subsets in cGVHD, our single-cell RNA-Seq analyses identified several genes significantly altered across multiple B cell clusters in the cGVHD group, implicating more broad alterations of some genes in this disease. Among these is CKS2, a critical cell cycle regulator, which was significantly increased in cGVHD B cells (Padj 1.0e-10 to 0.018, depending on the cluster evaluated). Increased CKS2 expression was validated by qPCR analysis on B cells from a separate HCT patient cohort with or without cGVHD (P &amp;lt; 0.001, Figure 1D), suggesting that the majority of cGVHD B cells are primed to enter the cell cycle at multiple stages of differentiation when exposed to the proper stimuli.</jats:p> <jats:p>In summary, we used an unbiased approach to identify and further characterize an extrafollicular CD11cposTACIposCD19high B cell population in cGVHD patients that appears to be activated and undergoing active IgG isotype switching. This plasmablast-like B cell population is potentially amenable to therapeutic intervention to prevent pathogenic antibody production. Importantly, we also identify gene alterations across the cGVHD peripheral B cell compartment that potentially underpin promotion of hyperactivated B cells in this disease. Therapeutic strategies to target these pathways will also be discussed. This work was supported by a National Institutes of Health grant, R01HL129061.</jats:p> <jats:p /> <jats:sec> <jats:title>Disclosures</jats:title> <jats:p>Ho: Omeros Corporation: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Research Funding; Jazz Pharmaceuticals: Consultancy. Horwitz:Abbvie Inc: Membership on an entity's Board of Directors or advisory committees. Rizzieri:Celgene, Gilead, Seattle Genetics, Stemline: Other: Speaker; AbbVie, Agios, AROG, Bayer, Celgene, Gilead, Jazz, Novartis, Pfizer, Sanofi, Seattle Genetics, Stemline, Teva: Other: Advisory Board; AROG, Bayer, Celgene, Celltron, Mustang, Pfizer, Seattle Genetics, Stemline: Consultancy; Stemline: Research Funding.</jats:p> </jats:sec>