No abstract

As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package , with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility.

Sutimlimab, a first-in-class humanized immunoglobulin G4 (IgG4) monoclonal antibody that selectively inhibits the classical complement pathway at C1s, rapidly halted hemolysis in the single-arm CARDINAL study in recently transfused patients with cold agglutinin disease (CAD). CADENZA was a 26-week randomized, placebo-controlled phase 3 study to assess safety and efficacy of sutimlimab in CAD patients without recent (within 6 months prior to enrollment) transfusion history. Forty-two patients with screening hemoglobin ≤10 g/dL, elevated bilirubin, and ≥1 CAD symptom received sutimlimab (n=22) or placebo (n=20) on Days 0 and 7, then biweekly. Composite primary endpoint criteria (hemoglobin increase ≥1.5 g/dL at treatment assessment timepoint [mean of Weeks 23, 25, 26], avoidance of transfusion and study-prohibited CAD therapy [Weeks 5-26]) were met by 16 patients (73%) on sutimlimab and three patients (15%) on placebo (odds ratio, 15.9 [95% confidence interval 2.9, 88.0; P<0.001]). Sutimlimab, but not placebo, significantly increased mean hemoglobin and FACIT-Fatigue scores at treatment assessment timepoint. Sutimlimab normalized mean bilirubin by Week 1. Improvements correlated with near-complete inhibition of the classical complement pathway (2.3% mean activity at Week 1) and C4 normalization. Twenty-one (96%) sutimlimab patients and 20 (100%) placebo patients experienced ≥1 treatment emergent adverse event. Headache, hypertension, rhinitis, Raynaud's phenomenon, and acrocyanosis were more frequent with sutimlimab versus placebo, with a difference of ≥3 patients between groups. Three sutimlimab patients discontinued due to adverse events; no placebo patients discontinued. These data demonstrate that sutimlimab has potential to be an important advancement in the treatment of CAD. (ClinicalTrials.gov: NCT03347422)

Caffeic acid possesses multiple biological effects, such as antibacterial, antioxidant, antiinflammatory, and anticancer growth; however, what effects it has on bovine mastitis have not been investigated. The aim of this study was to verify the antiinflammatory properties of caffeic acid on the inflammatory response of primary bovine mammary epithelial cells (bMEC) induced by lipopolysaccharide (LPS), and to clarify the possible underlying mechanism. Bovine mammary epithelial cells were treated with various concentrations (10, 50, 100, and 200 μg/mL) of LPS for 3, 6, 12, and 18 h; the results showed that LPS significantly inhibited cell viability in a time- and dose-dependent manner. When cells were treated with LPS (50 μg/mL) for 12h, the cell membrane permeability significantly increased, which promoted cell apoptosis. Various concentrations (10, 25, and 50 μg/mL) of caffeic acid could weaken the inflammation injury of bMEC induced by LPS without cytotoxicity. Proinflammatory cytokines (IL-8, IL-1β, IL-6, and tumor necrosis factor α) from bMEC were decreased. Nuclear transcription factor κB activity was weakened via blocking κB inhibitor α degradation and p65 phosphorylation. All these showed that the protective effect of caffeic acid on LPS-induced inflammation injury in bMEC was at least partly achieved by the decreased production of proinflammatory cytokines mediated by the effect of reducing the κB inhibitor α degradation and p65 phosphorylation in the nuclear transcription factor κB pathway. The use of caffeic acid would be beneficial in dairy cows during Escherichia coli mastitis as a safe and natural antiinflammatory drug.

BackgroundDifferential gene expression is important to understand the biological differences between healthy and diseased states. Two common sources of differential gene expression data are microarray studies and the biomedical literature.MethodsWith the aid of text mining and gene expression analysis we have examined the comparative properties of these two sources of differential gene expression data.ResultsThe literature shows a preference for reporting genes associated to higher fold changes in microarray data, rather than genes that are simply significantly differentially expressed. Thus, the resemblance between the literature and microarray data increases when the fold-change threshold for microarray data is increased. Moreover, the literature has a reporting preference for differentially expressed genes that (1) are overexpressed rather than underexpressed; (2) are overexpressed in multiple diseases; and (3) are popular in the biomedical literature at large. Additionally, the degree to which diseases are similar depends on whether microarray data or the literature is used to compare them. Finally, vaguely-qualified reports of differential expression magnitudes in the literature have only small correlation with microarray fold-change data.ConclusionsReporting biases of differential gene expression in the literature can be affecting our appreciation of disease biology and of the degree of similarity that actually exists between different diseases.Electronic supplementary materialThe online version of this article (10.1186/s12920-017-0293-y) contains supplementary material, which is available to authorized users.