A Molecular Host Response Assay to Discriminate Between Sepsis and Infection-Negative Systemic Inflammation in Critically Ill Patients: Discovery and Validation in Independent Cohorts.

BACKGROUND: Systemic inflammation is a whole body reaction having an infection-positive (i.e., sepsis) or infection-negative origin. It is important to distinguish between these two etiologies early and accurately because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on peripheral blood RNAs could be discovered that would (1) determine which patients with systemic inflammation had sepsis, (2) be robust across independent patient cohorts, (3) be insensitive to disease severity, and (4) provide diagnostic utility. The goal of this study was to identify and validate such a molecular classifier. METHODS AND FINDINGS: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICUs). Biomarker discovery utilized an Australian cohort (n = 105) consisting of 74 cases (sepsis patients) and 31 controls (post-surgical patients with infection-negative systemic inflammation) recruited at five tertiary care settings in Brisbane, Australia, from June 3, 2008, to December 22, 2011. A four-gene classifier combining CEACAM4, LAMP1, PLA2G7, and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte Lab, was validated using reverse transcription quantitative PCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Patients for validation were selected from the Molecular Diagnosis and Risk Stratification of Sepsis study (ClinicalTrials.gov, NCT01905033), which recruited ICU patients from the Academic Medical Center in Amsterdam and the University Medical Center Utrecht. Patients recruited from November 30, 2012, to August 5, 2013, were eligible for inclusion in the present study. Validation cohort 1 (n = 59) consisted entirely of unambiguous cases and controls; SeptiCyte Lab gave an area under curve (AUC) of 0.95 (95% CI 0.91-1.00) in this cohort. ROC curve analysis of an independent, more heterogeneous group of patients (validation cohorts 2-5; 249 patients after excluding 37 patients with an infection likelihood of "possible") gave an AUC of 0.89 (95% CI 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility of SeptiCyte Lab was evaluated by comparison to various clinical and laboratory parameters available to a clinician within 24 h of ICU admission. SeptiCyte Lab was significantly better at differentiating cases from controls than all tested parameters, both singly and in various logistic combinations, and more than halved the diagnostic error rate compared to procalcitonin in all tested cohorts and cohort combinations. Limitations of this study relate to (1) cohort compositions that do not perfectly reflect the composition of the intended use population, (2) potential biases that could be introduced as a result of the current lack of a gold standard for diagnosing sepsis, and (3) lack of a complete, unbiased comparison to C-reactive protein. CONCLUSIONS: SeptiCyte Lab is a rapid molecular assay that may be clinically useful in managing ICU patients with systemic inflammation. Further study in population-based cohorts is needed to validate this assay for clinical use.

Antiandrogenic actions of medroxyprogesterone acetate on epithelial cells within normal human breast tissues cultured ex vivo.

OBJECTIVE: Medroxyprogesterone acetate (MPA), a component of combined estrogen-progestin therapy (EPT), has been associated with increased breast cancer risk in EPT users. MPA can bind to the androgen receptor (AR), and AR signaling inhibits cell growth in breast tissues. Therefore, the aim of this study was to investigate the potential of MPA to disrupt AR signaling in an ex vivo culture model of normal human breast tissue. METHODS: Histologically normal breast tissues from women undergoing breast surgical operation were cultured in the presence or in the absence of the native AR ligand 5α-dihydrotestosterone (DHT), MPA, or the AR antagonist bicalutamide. Ki67, bromodeoxyuridine, B-cell CLL/lymphoma 2 (BCL2), AR, estrogen receptor α, and progesterone receptor were detected by immunohistochemistry. RESULTS: DHT inhibited the proliferation of breast epithelial cells in an AR-dependent manner within tissues from postmenopausal women, and MPA significantly antagonized this androgenic effect. These hormonal responses were not commonly observed in cultured tissues from premenopausal women. In tissues from postmenopausal women, DHT either induced or repressed BCL2 expression, and the antiandrogenic effect of MPA on BCL2 was variable. MPA significantly opposed the positive effect of DHT on AR stabilization, but these hormones had no significant effect on estrogen receptor α or progesterone receptor levels. CONCLUSIONS: In a subset of postmenopausal women, MPA exerts an antiandrogenic effect on breast epithelial cells that is associated with increased proliferation and destabilization of AR protein. This activity may contribute mechanistically to the increased risk of breast cancer in women taking MPA-containing EPT.

Optimal replication and the importance of experimental design for gel-based quantitative proteomics.

Quantitative proteomic studies, based on two-dimensional gel electrophoresis, are commonly used to find proteins that are differentially expressed between samples or groups of samples. These proteins are of interest as potential diagnostic or prognostic biomarkers, or as proteins associated with a trait. The complexity of proteomic data poses many challenges, so while experiments may reveal proteins that are differentially expressed, these are often not significant when subjected to rigorous statistical analysis. However, this can be addressed through appropriate experimental design. A good experimental design considers the impact of different sources of variation, both analytical and biological, on the statistical importance of the results. The design should address the number of samples that must be analyzed and the number of replicate gels per sample, in the context of a particular minimum difference that one is seeking to achieve. In this study, we explore the ways to improve the quality of protein expression data from 2-DE gels, and describe an approach for defining the number of samples required and the number of gels per sample. It has been developed for the simplest of situations, two groups of samples with variation at two levels: between samples and between gels. This approach will also be useful as a guide for more complex designs involving more than two groups of samples. We describe some Internet-accessible tools that can assist in the design of proteomic studies.