Predictive model for vitamin C levels in hyperinsulinemic individuals based on age, sex, waist circumference, low-density lipoprotein, and immune-associated serum proteins

Vitamin C (ascorbic acid) is an important water-soluble antioxidant associated with decreased oxidative stress in type 2 diabetes (T2D) patients. A previous targeted plasma proteomic study has indicated that ascorbic acid is associated with markers of the immune system in healthy subjects. However, the association between the levels of ascorbic acid and blood biomarkers in subjects at risk of developing T2D is still unknown. Serum ascorbic acid was measured by ultra-performance liquid chromatography and serum proteins were quantified by untargeted liquid-chromatography mass spectrometry in 25 hyperinsulinemia subjects that were randomly assigned a high dairy intake diet or an adequate dairy intake diet for 6 weeks, then crossed-over after a 6-week washout period. Spearman correlation followed by gene ontology analyses were performed to identify biological pathways associated with ascorbic acid. Finally, machine learning analysis was performed to obtain a specific serum protein signature that could predict ascorbic acid levels. After adjustments for waist circumference, LDL, HDL, fasting insulin, fasting blood glucose, age, gender, and dairy intake; serum ascorbic acid correlated positively with different aspects of the immune system. Machine learning analysis indicated that a signature composed of 21 features that included 17 proteins (mainly from the immune system), age, sex, waist circumference, and LDL could predict serum ascorbic acid levels in hyperinsulinemia subjects. In conclusion, the result reveals a correlation as well as modulation between serum ascorbic acid levels and proteins that play vital roles in regulating different aspects of the immune response in individuals at risk of T2D. The development of a predictive signature for ascorbic acid will further help the assessment of ascorbic acid status in clinical settings.

Exploration of molecular signatures associated with different clinical features of Takayasu arteritis based on a prospective cohort study

Takayasu arteritis (TAK) is complicated disorder without reliable biomarkers. Here, we aimed to explore TAK-associated factor panels and their changes after biologic treatment. Five factor panels were identified: 1. systemic inflammation: C3, ESR, CRP, PLT, IL-6, C4, and IgG; 2. vascular inflammation: YKL40, IL-16, PTX3, and CCL2; 3. immune regulation panel: IL-10, IFN-γ, CCL5, and MMP1; 4. angiogenesis and fibrosis: FGF, PDGFAB, and VEGF; and 5. vascular remodeling: CD19+ B cell ratio, MMP3, and leptin. Panel 1 parameters were closely related to disease activity, while Panel 5 parameters, particularly CD19+ B cell ratio and leptin, were significantly higher in ischemic patients. After treatment, tocilizumab had a stronger inhibitory effect on Panel 1 parameters, PTX3, and YKL-40, while adalimumab led to an increase in IL-16, CCL2, and leptin levels. Altogether, these data expanded our knowledge regarding molecular background in TAK development and shed light on precise treatment in future studies.

Selecting optimum miRNA panel for miRNA signature-based companion diagnostic model to predict the response of R-CHOP treatment in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common type of malignant lymphoma. Although the first-line treatment, R-CHOP treatment, shows efficacy in approximately 80% of patients with DLBCL, some patients have refractory disease or relapse after the initial response to therapy, resulting in a significantly poorer prognosis. In this study, we developed a microRNA (miRNA) signature-based companion diagnostic model to predict the response of patients with DLBCL to R-CHOP treatment by integrating two clinical study datasets. To select the optimum miRNA combination as a panel, we examined three feature selection methods (p-value-based ranking, stepwise method, and Boruta), together with 11 types of classifiers systematically. Boruta selection enabled a higher area under the curve (AUC) with a lower number of miRNAs compared with other feature selection methods, leading to an AUC of 0.751 via the random forest classifier using 36 miRNAs. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that Boruta avoided multiple selection of miRNAs with similar functions, thereby preventing the decrease in diagnostic ability via collinearity. The AUC value first increased with an increasing number of miRNAs and then became almost constant at approximately 30 miRNAs, suggesting the existence of the optimum number of miRNAs as a panel for future clinical translation of multiple miRNA-based diagnostics.

Two-Stage Adaptive Design for Prognostic Biomarker Signatures with a Survival Endpoint

Cancer biomarker discoveries typically involve utilizing patient specimens. In practice, there is often strong desire to preserve high quality biospecimens for studies that are most likely to yield useful information. Previously, we proposed a two-stage adaptive design for binary endpoints which terminates the biomarker study in a futility interim if the model performance is unsatisfactory. In this work, we extend the two-stage design framework to accommodate time-to-event endpoints. The first stage of the procedure involves testing whether the measure of discrimination for survival models (C-index) exceeds a pre-specified threshold. We describe the computation of cross-validated C-index and evaluation of the statistical significance using re-sampling techniques. The second stage involves an independent model validation. Our simulation studies show that under the null hypothesis, the proposed design maintains type I error at the nominal level and has high probabilities of terminating the study early. Under the alternative hypothesis, power of the design is a function of the true event proportion, the sample size, and the targeted improvement in the discriminant measure. We apply the method to design of a prognostic biomarker study in patients with triple-negative breast cancer. Some practical aspects of the proposed method are discussed.

Distinct patterns of cellular immune response elicited by influenza non-adjuvanted and AS03-adjuvanted monovalent H1N1(pdm09) vaccine

The study aimed at identifying biomarkers of immune response elicited by non-adjuvanted-(NAV) and adjuvanted-(AV) H1N1(pdm09) vaccines. The results showed that despite both vaccines elicited similar levels of anti-H1N1 antibodies at day30 after vaccination, higher reactivity was observed in AV at day180. While AV induced early changes in cell-surface molecules on monocytes, CD4⁺, CD8⁺ T-cells and B-cells, NAV triggered minor changes, starting later on at day3. Furthermore, AV induced a late and persistent increase in TLR gene expression after day3, except for tlr4, while NAV displayed earlier but transient tlr3/4/7/9 up-regulation. Contrasting with NAV, prominent chemokine gene expression (cxcl8,cxcl9,ccl5) and a broad spectrum up-regulation of plasmatic biomarkers (CXCL8,IL-6,IL-1β,IL-12,IL-10) was evident in AV, which showed a major involvement of TNF and IL-10. Similarly, AV induced a robust IL-10-modulated proinflammatory storm, with early and persistent involvement of TNF-α/IL-12/IFN-γ axis derived from NK-cells, CD4⁺ and CD8⁺ T-cells along with promiscuous production of IL-4/IL-5/IL-13. Conversely, NAV promotes a concise and restricted intracytoplasmic chemokine/cytokine response, essentially mediated by TNF-α and IL-4, with late IL-10 production by CD8⁺ T-cells. Systems biology approach underscored that AV guided the formation of an imbricate network characterized by a progressive increase in the number of neighborhood connections amongst innate and adaptive immunity. In AV, the early cross-talk between innate and adaptive immunity, followed by the triad NK/CD4⁺/CD8⁺ T-cells at day3, sponsored a later/robust biomarker network. These findings indicate the relevance of adjuvanted vaccination to orchestrate broad, balanced and multifactorial cellular immune events that lead ultimately to a stronger H1N1 humoral immunity.

Misclassification Errors in Unsupervised Classification Methods. Comparison Based on the Simulation of Targeted Proteomics Data

Unsupervised classification methods are gaining acceptance in omics studies of complex common diseases, which are often vaguely defined and are likely the collections of disease subtypes. Unsupervised classification based on the molecular signatures identified in omics studies have the potential to reflect molecular mechanisms of the subtypes of the disease and to lead to more targeted and successful interventions for the identified subtypes. Multiple classification algorithms exist but none is ideal for all types of data. Importantly, there are no established methods to estimate sample size in unsupervised classification (unlike power analysis in hypothesis testing). Therefore, we developed a simulation approach allowing comparison of misclassification errors and estimating the required sample size for a given effect size, number, and correlation matrix of the differentially abundant proteins in targeted proteomics studies. All the experiments were performed in silico. The simulated data imitated the expected one from the study of the plasma of patients with lower urinary tract dysfunction with the aptamer proteomics assay Somascan (SomaLogic Inc, Boulder, CO), which targeted 1129 proteins, including 330 involved in inflammation, 180 in stress response, 80 in aging, etc. Three popular clustering methods (hierarchical, k-means, and k-medoids) were compared. K-means clustering performed much better for the simulated data than the other two methods and enabled classification with misclassification error below 5% in the simulated cohort of 100 patients based on the molecular signatures of 40 differentially abundant proteins (effect size 1.5) from among the 1129-protein panel.