A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

Efficacy and safety of olokizumab in patients with rheumatoid arthritis with an inadequate response to TNF inhibitor therapy: outcomes of a randomised Phase IIb study

OBJECTIVES: The aim of this 12-week Phase IIb study was to assess the efficacy and safety of olokizumab (OKZ), a humanised anti-IL6 monoclonal antibody, in patients with rheumatoid arthritis (RA) with moderate-to-severe disease activity who had previously failed tumour necrosis factor (TNF) inhibitor therapy. The dose-exposure-response relationship for OKZ was also investigated. METHODS: Patients were randomised to one of nine treatment arms receiving placebo (PBO) or OKZ (60, 120 or 240 mg) every 4 weeks (Q4W) or every 2 weeks (Q2W), or 8 mg/kg tocilizumab (TCZ) Q4W. The primary endpoint was change from baseline in DAS28(C-reactive protein, CRP) at Week 12. Secondary efficacy endpoints were American College of Rheumatology 20 (ACR20), ACR50 and ACR70 response rates at Week 12. Exploratory analyses included comparisons of OKZ efficacy with TCZ. RESULTS: Across 221 randomised patients, OKZ treatment produced significantly greater reductions in DAS28(CRP) from baseline levels at Week 12, compared to PBO (p<0.001), at all the OKZ doses tested (60 mg OKZ p=0.0001, 120 and 240 mg OKZ p<0.0001). Additionally, ACR20 and ACR50 responses were numerically higher for OKZ than PBO (ACR20: PBO=17.1-29.9%, OKZ=32.5-60.7%; ACR50: PBO=1.3-4.9%, OKZ=11.5-33.2%). OKZ treatment, at several doses, demonstrated similar efficacy to TCZ across multiple endpoints. Most adverse events were mild or moderate and comparable between OKZ and TCZ treatment groups. Pharmacokinetic/pharmacodynamic modelling demonstrated a shallow dose/exposure response relationship in terms of percentage of patients with DAS28(CRP) <2.6. CONCLUSIONS: OKZ produced significantly greater reductions in DAS28(CRP) from baseline at Week 12 compared with PBO. Reported AEs were consistent with the safety profile expected of this class of drug, with no new safety signals identified. TRIAL REGISTER NUMBER: NCT01242488

Additional file 2: of A computational framework for complex disease stratification from multiple large-scale datasets

Complete results of the enrichment analysis between clusters. (XLSX 4293&nbsp;kb)</span

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

A computational framework for complex disease stratification from multiple large-scale datasets

Background: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.Results: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.</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