Summary of adverse experiences during treatment (14±4 days) in the carotid endarterectomy study.

<p>Summary of adverse experiences during treatment (14±4 days) in the carotid endarterectomy study.</p

The influence of darapladib treatment (14±4 days) on the expression (mRNA) of several plaque biomarkers.

<p>Vertical bars represent 95% confidence intervals (CI). CD40L, CD40 ligand; MMP, matrix metalloproteinase; PAI, plasminogen activator inhibitor; ICAM, intercellular adhesion molecule; IL, interleukin; Lp-PLA, lipoprotein-associated phospholipase A₂.</p

Disposition of patients throughout the carotid endarterectomy study.

<p>Disposition of patients throughout the carotid endarterectomy study.</p

The influence of darapladib treatment (14±4 days) on plaque (A) caspase-3 activity and (B) caspase-8 activity (mean ± SEM).

<p><i>P</i> values represent comparisons of darapladib 80 mg with placebo. AU, activity units.</p

Analysis of plaque lysophosphatidylcholine (lysoPC) content following treatment (14±4 days) with darapladib.

<p>Data represent mean ± standard deviation.</p

Effect of darapladib treatment (14±4 days) on plaque and plasma lipoprotein-associated phospholipase A₂ activity.

<p>Vertical bars for plaque and plasma data points represent 97.5% and 95% confidence intervals (CI), respectively. Baseline plasma Lp-PLA₂ activity levels are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089034#pone-0089034-t001" target="_blank">Table 1</a>. At Day 15, plasma Lp-PLA₂ activity levels (mean ± SD) for placebo, 40 mg darapladib, and 80 mg darapladib were 141.4±39.3, 63.4±28.3, and 27.0±11.4 nmol/min/ml, respectively. At Day 15, plaque Lp-PLA₂ activity levels (mean ± SEM) for placebo, 40 mg darapladib, and 80 mg darapladib were 0.94±0.14, 0.26±0.13, and 0.11±0.14 nmol/min/mg protein, respectively.</p

Additional file 3: of A computational framework for complex disease stratification from multiple large-scale datasets

Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kappa values of the Random Forest models in the training set. Table S9. Performances values for the Random Forest model in the testing set. Figure S11. Relative importance of the top 20 predictors building the final model of the RF. The importance axis is scaled, with the mRNA expression of CD3D scaled to 100% and the methylation state of POLA2 to 0% (not shown). (DOCX 18 kb

Additional file 4: of A computational framework for complex disease stratification from multiple large-scale datasets

DIABLO sPLSDA model results. (DOCX 18966 kb

Biomarker Predictors of Clinical Efficacy of the Anti-IgE Biologic, Omalizumab, in Severe Asthma in Adults: Results of the SoMOSA Study

The anti-IgE monoclonal, omalizumab, is widely used for severe asthma. This study aimed to identify biomarkers that predict clinical improvement during one year of omalizumab treatment.1-year, open-label, Study of Mechanisms of action of Omalizumab in Severe Asthma (SoMOSA) involving 216 severe (GINA step 4/5) uncontrolled atopic asthmatics (≥2 severe exacerbations in previous year) on high-dose inhaled corticosteroids, long-acting β-agonists, ± mOCS. It had two phases: 0-16 weeks, to assess early clinical improvement by Global Evaluation of Therapeutic Effectiveness (GETE), and 16-52 weeks, to assess late responses by ≥50% reduction in exacerbations or dose of maintenance oral corticosteroids (mOCS). All participants provided samples (exhaled breath, blood, sputum, urine) before and after 16 weeks of omalizumab treatment.191 patients completed phase 1; 63% had early improvement. Of 173 who completed phase 2, 69% had reduced exacerbations by ≥50%, while 57% (37/65) on mOCS reduced their dose by ≥50%. The primary outcome 2, 3-dinor-11-β-PGF2α, GETE and standard clinical biomarkers (blood and sputum eosinophils, exhaled nitric oxide, serum IgE) did not predict either clinical response. Five breathomics (GC-MS) and 5 plasma lipid biomarkers strongly predicted the ≥50% reduction in exacerbations (receiver operating characteristic area under the curve (AUC): 0.780 and 0.922, respectively) and early responses (AUC:0.835 and 0.949, respectively). In independent cohorts, the GC-MS biomarkers differentiated between severe and mild asthma. Conclusions This is the first discovery of omics biomarkers that predict improvement to a biologic for asthma. Their prospective validation and development for clinical use is justified. Background Methods Results</p