Group B Streptococcus (GBS; Streptococcus agalactiae) is a major human pathogen that disproportionately affects neonates and women in the peripartum period and is an emerging cause of infection in older adults. The primary toxin of GBS, β-hemolysin/cytolysin (βH/C), has a well-defined role in the pathogenesis of invasive disease, but its role in urinary tract infection (UTI) is unknown. Using both in vitro and in vivo models, we analyzed the importance of βH/C in GBS uropathogenesis. There were no significant differences in bacterial density from the bladders or kidneys from mice infected with wild-type or isogenic βH/C-deficient GBS, and competitive indices from co-infection experiments were near 1. Thus, βH/C is dispensable for the establishment of GBS-UTI. However, βH/C-sufficient GBS induced a more robust proinflammatory cytokine response in cultured bladder epithelial cells and in the urinary tracts of infected mice. Given the near ubiquity of βH/C-expressing strains in epidemiologic studies and the importance of local inflammation in dictating outcomes and sequelae of UTI, we hypothesize that βH/C-driven inflammatory signaling may be important in the clinical course of GBS-UTI.

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

There is variable reporting on the benefits of a 200 μg/d selenium supplementation towards reducing prostate cancer impacts. The current analysis is to understand whether stratified groups receive supplementation benefits on prostate health.

Outcome predictors in use today are prognostic only for hormone receptor-positive (HRpos) breast cancer. Although microarray-derived multigene predictors of hormone receptor-negative (HRneg) and/or triple negative (Tneg) breast cancer recurrence risk are emerging, to date none have been transferred to clinically suitable assay platforms (for example, RT-PCR) or validated against formalin-fixed paraffin-embedded (FFPE) HRneg/Tneg samples.

Obesity has a strong genetic influence, with some variants showing stronger associations among women than men. Women are also more likely to distribute weight in the abdomen following menopause. We investigated whether genetic loci link with obesity-related phenotypes differently by menopausal status.

Percent mammographic density (PD) estimates the proportion of stromal, fat, and epithelial breast tissues on the mammogram image. Adjusted for age and body mass index (BMI), PD is one of the strongest risk factors for breast cancer. Inherited factors are hypothesized to explain between 30 and 60% of the variance in this trait. However, previously identified common genetic variants account for less than 6% of the variance in PD, leaving much of the genetic contribution to this trait unexplained. We performed the first study to examine whether germline copy number variation (CNV) are associated with PD. Two genome-wide association studies (GWAS) of percent density conducted on the Illumina 660W-Quad were used to identify and replicate the association between candidate CNVs and PD: the Minnesota Breast Cancer Family Study (MBCFS) and controls from the Mayo Venous Thromboembolism (Mayo VTE) Case-Control Study, with 585 and 328 women, respectively. Linear models were utilized to examine the association of each probe with PD, adjusted for age, menopausal status and BMI. Segmentation was subsequently performed on the probe-level test statistics to identify candidate CNV regions that were associated with PD.

From March through August 2015, nearly 60 teams from around the world participated in the Prostate Cancer Dream Challenge (PCDC). Participating teams were faced with the task of developing prediction models for patient survival and treatment discontinuation using baseline clinical variables collected on metastatic castrate-resistant prostate cancer (mCRPC) patients in the comparator arm of four phase III clinical trials. In total, over 2,000 mCRPC patients treated with first-line docetaxel comprised the training and testing data sets used in this challenge. In this paper we describe: (a) the sub-challenges comprising the PCDC, (b) the statistical metrics used to benchmark prediction performance, (c) our analytical approach, and finally (d) our team's overall performance in this challenge. Specifically, we discuss our curated, ad-hoc, feature selection (CAFS) strategy for identifying clinically important risk-predictors, the ensemble-based Cox proportional hazards regression framework used in our final submission, and the adaptation of our modeling framework based on the results from the intermittent leaderboard rounds. Strong predictors of patient survival were successfully identified utilizing our model building approach. Several of the identified predictors were new features created by our team via strategically merging collections of weak predictors. In each of the three intermittent leaderboard rounds, our prediction models scored among the top four models across all participating teams and our final submission ranked 9 th place overall with an integrated area under the curve (iAUC) of 0.7711 computed in an independent test set. While the prediction performance of teams placing between 2 nd- 10 th (iAUC: 0.7710-0.7789) was better than the current gold-standard prediction model for prostate cancer survival, the top-performing team, FIMM-UTU significantly outperformed all other contestants with an iAUC of 0.7915.  In summary, our ensemble-based Cox regression framework with CAFS resulted in strong overall performance for predicting prostate cancer survival and represents a promising approach for future prediction problems.

To investigate the safety and efficacy of elagolix, an oral gonadotropin-releasing hormone antagonist, with hormonal add-back therapy for up to 12 months in women with heavy menstrual bleeding associated with uterine leiomyomas.

This study is to investigate the potential effect of aqueous humor on already formed lymphatic vessels of the ocular surface including the conjunctiva and the cornea.

Paroxysmal nocturnal hemoglobinuria (PNH) is characterized by uncontrolled terminal complement activation leading to intravascular hemolysis (IVH), thrombosis, and impairments in quality of life (QoL). The aim of this study was to identify the clinical drivers of improvement in patient-reported outcomes (PROs) in patients with PNH receiving the complement component 5 (C5) inhibitors eculizumab and ravulizumab.This post hoc analysis assessed clinical outcomes and PROs from 246 complement inhibitor-naive patients with PNH enrolled in a phase 3 randomized non-inferiority study that compared the C5 inhibitors ravulizumab and eculizumab (study 301; NCT02946463). The variables of interest were lactate dehydrogenase (LDH) levels, a surrogate measure of IVH, and hemoglobin (Hb) levels. PROs were collected using Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) and European Organisation for Research and Treatment of Cancer, Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) to assess fatigue and QoL, respectively.Improvements in absolute mean LDH levels were significantly associated with improvements in mean FACIT-F score (p = 0.0024) and EORTC QLQ-C30 global health (GH) score (p < 0.0001) from baseline to day 183. Improvements in scores were achieved despite a non-significant increase in Hb levels. To understand the interaction between LDH and Hb, a regression analysis was performed: LDH response with Hb improvements was a significant predictor of improvement in fatigue. The independent effect of improved Hb did not significantly affect FACIT-F or EORTC QLQ-C30 GH scores.These findings suggest that LDH levels are an important determinant of fatigue and QoL outcomes in patients with PNH. CTR: NCT02946463, October 27, 2016.

Severe acute malnutrition (SAM) is a major cause of mortality in children under 5 years and is associated with hepatic steatosis. Bile acids are synthesized in the liver and participate in dietary fat digestion, regulation of energy expenditure, and immune responses. The aim of this work was to investigate whether SAM is associated with clinically relevant changes in bile acid homeostasis.

Although circulating microRNA (miRNAs) have emerged as biomarkers predicting mortality in acute coronary syndrome (ACS), more data are needed to understand these mechanisms. Mapping miRNAs to high-risk traits may identify miRNAs involved in pathways conferring risk for poor outcome in ACS. We aim to investigate the relationship between circulating miRNAs and high-risk traits in non-ST-segment elevation acute coronary syndrome (NSTE-ACS).

It is being debated whether prostate-specific antigen (PSA)-based screening effectively reduces prostate cancer mortality. Some of the uncertainty could be related to deficiencies in the age-based PSA cut-off thresholds used in screening. Current study considered 2779 men with prostate cancer and 1606 men without a cancer diagnosis, recruited for various studies in New Zealand, US, and Taiwan. Association of PSA with demographic, lifestyle, clinical characteristics (for cases), and the aldo-keto reductase 1C3 (AKR1C3) rs12529 genetic polymorphisms were analysed using multiple linear regression and univariate modelling. Pooled multivariable analysis of cases showed that PSA was significantly associated with demographic, lifestyle, and clinical data with an interaction between ethnicity and age further modifying the association. Pooled multivariable analysis of controls data also showed that demographic and lifestyle are significantly associated with PSA level. Independent case and control analyses indicated that factors associated with PSA were specific for each cohort. Univariate analyses showed a significant age and PSA correlation among all cases and controls except for the US-European cases while genetic stratification in cases showed variability of correlation. Data suggests that unique PSA cut-off thresholds factorized with demographics, lifestyle and genetics may be more appropriate for prostate cancer screening.

Androgen deprivation therapy (ADT) for men with prostate cancer (PCa) results in accelerated bone loss and increased risk of bone fracture. The aim of the present study was to evaluate serum bone markers-sclerostin, Dickkopf-1 (DKK-1) and osteoprotegerin (OPG), in a cohort of 88 PCa patients without known bone metastases, managed with and without ADT, and to analyse their relationship with bone mineral density (BMD) and sex steroids. The cross-sectional analysis between acute-, chronic- and former-ADT groups and PCa controls showed that sclerostin and OPG levels significantly differed between them (p = 0.029 and p = 0.032). Groups contributing to these significant changes were recorded. There were no significant differences in serum DKK-1 levels across the four groups (p = 0.683). In the longitudinal analysis, significant % decreases within groups were seen for DKK-1 [chronic-ADT (- 10.06%, p = 0.0057), former-ADT (- 12.77%, p = 0.0239), and in PCa controls group (- 16.73, p = 0.0022); and OPG levels in chronic ADT (- 8.28%, p = 0.003) and PCa controls group (- 12.82%, p = 0.017)]. However, % changes in sclerostin, DKK-1, and OPG did not differ significantly over 6-months across the evaluated groups. Sclerostin levels showed significant positive correlations with BMD at baseline in the ADT group, while in PCa controls this correlation existed at both baseline and 6-month time points. Sclerostin correlated negatively with testosterone in former ADT users and in PCa controls. Possible prognostic features denoted by parallel increases in sclerostin and BMD are discussed.

Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to cure in T-cell Acute Lymphoblastic Leukemia (T-ALL). Biomarker guided risk stratification and targeted therapy have the potential to improve outcomes in high-risk T-ALL; however, cellular and genetic factors contributing to treatment resistance remain unknown. Previous bulk genomic studies in T-ALL have implicated tumor heterogeneity as an unexplored mechanism for treatment failure. To link tumor subpopulations with clinical outcome, we created an atlas of healthy pediatric hematopoiesis and applied single-cell multiomic (CITE-seq/snATAC-seq) analysis to a cohort of 40 cases of T-ALL treated on the Children's Oncology Group AALL0434 clinical trial. The cohort was carefully selected to capture the immunophenotypic diversity of T-ALL, with early T-cell precursor (ETP) and Near/Non-ETP subtypes represented, as well as enriched with both relapsed and treatment refractory cases. Integrated analyses of T-ALL blasts and normal T-cell precursors identified a bone-marrow progenitor-like (BMP-like) leukemia sub-population associated with treatment failure and poor overall survival. The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL within two independent patient cohorts using bulk RNA-sequencing data from over 1300 patients. We defined the mutational landscape of BMP-like T-ALL, finding that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state. We transcriptionally matched BMP-like blasts to early thymic seeding progenitors that have low NR3C1 expression and high stem cell gene expression, corresponding to a corticosteroid and conventional cytotoxic resistant phenotype we observed in ex vivo drug screening. To identify novel targets for BMP-like blasts, we performed in silico and in vitro drug screening against the BMP-like signature and prioritized BMP-like overexpressed cell-surface (CD44, ITGA4, LGALS1) and intracellular proteins (BCL-2, MCL-1, BTK, NF-κB) as candidates for precision targeted therapy. We established patient derived xenograft models of BMP-high and BMP-low leukemias, which revealed vulnerability of BMP-like blasts to apoptosis-inducing agents, TEC-kinase inhibitors, and proteasome inhibitors. Our study establishes the first multi-omic signatures for rapid risk-stratification and targeted treatment of high-risk T-ALL.

Androgen deprivation therapy (ADT) is an effective palliation treatment in men with advanced prostate cancer (PC). However, ADT has well documented side effects that could alter the patient's health-related quality of life (HRQoL). The current study aims to test whether a genetic stratification could provide better knowledge for optimising ADT options to minimize HRQoL effects.

Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients.

Gene fusions play a critical role in some cancers and can serve as important clinical targets. In epithelial ovarian cancer (EOC), the contribution of fusions, especially by histological type, is unclear. We therefore screened for recurrent fusions in a histologically diverse panel of 220 EOCs using RNA sequencing. The Pipeline for RNA-Sequencing Data Analysis (PRADA) was used to identify fusions and allow for comparison with The Cancer Genome Atlas (TCGA) tumors. Associations between fusions and clinical prognosis were evaluated using Cox proportional hazards regression models. Nine recurrent fusions, defined as occurring in two or more tumors, were observed. CRHR1-KANSL1 was the most frequently identified fusion, identified in 6 tumors (2.7% of all tumors). This fusion was not associated with survival; other recurrent fusions were too rare to warrant survival analyses. One recurrent in-frame fusion, UBAP1-TGM7, was unique to clear cell (CC) EOC tumors (in 10%, or 2 of 20 CC tumors). We found some evidence that CC tumors harbor more fusions on average than any other EOC histological type, including high-grade serous (HGS) tumors. CC tumors harbored a mean of 7.4 fusions (standard deviation [sd] = 7.4, N = 20), compared to HGS EOC tumors mean of 2.0 fusions (sd = 3.3, N = 141). Few fusion genes were detected in endometrioid tumors (mean = 0.24, sd = 0.74, N = 55) or mucinous tumors (mean = 0.25, sd = 0.5, N = 4) tumors. To conclude, we identify one fusion at 10% frequency in the CC EOC subtype, but find little evidence for common (> 5% frequency) recurrent fusion genes in EOC overall, or in HGS subtype-specific EOC tumors.

Aldo-keto reductase 1C3 (AKR1C3) is known for multiple functions including its catalytic activity towards producing extra-testicular androgen. The present study is towards understanding interaction between biological, lifestyle and genetic impacts of AKR1C3 and their influence on clinical factors in a prostate cancer (PC) cohort from New Zealand (NZ).

There is a lack of personalized treatment options for women with recurrent platinum-resistant ovarian cancer. Outside of bevacizumab and a group of poly ADP-ribose polymerase inhibitors, few options are available to women that relapse. We propose that efficacious drug combinations can be determined via molecular characterization of ovarian tumors along with pre-established pharmacogenomic profiles of repurposed compounds. To that end, we selectively performed multiple two-drug combination treatments in ovarian cancer cell lines that included reactive oxygen species inducers and HSP90 inhibitors. This allowed us to select cell lines that exhibit disparate phenotypes of proliferative inhibition to a specific drug combination of auranofin and AUY922. We profiled altered mechanistic responses from these agents in both reactive oxygen species and HSP90 pathways, as well as investigated PRKCI and lncRNA expression in ovarian cancer cell line models. Generation of dual multi-gene panels implicated in resistance or sensitivity to this drug combination was produced using RNA sequencing data and the validity of the resistant signature was examined using high-density RT-qPCR. Finally, data mining for the prevalence of these signatures in a large-scale clinical study alluded to the prevalence of resistant genes in ovarian tumor biology. Our results demonstrate that high-throughput viability screens paired with reliable in silico data can promote the discovery of effective, personalized therapeutic options for a currently untreatable disease.