A data-driven approach to build a predictive model of cancer patients' disease outcome by utilizing co-expression networks.

BACKGROUND: Next Generation Sequencing (NGS) technologies have revolutionized genomics data research over the last decades by facilitating high-throughput sequencing of genetic material such as RNA Sequencing (RNAseq). A significant challenge is to explore innovative methods for further exploitation of these large-scale datasets. The approach described in this paper utilizes the results of RNAseq analysis to identify biomarkers related to the disease and deploy a disease outcome predictive model. METHOD: Chronic Lymphocytic Leukemia (CLL) was used as an example in the implementation of this approach. The approach proposed follows this methodology: (1) Analysis of RNAseq raw data, (2) Construction of a gene correlation network, (3) Identification of modules and hub genes in this network, which constitute the features for the classification algorithm, (4) Deployment of an efficient predictive model, with the use of state-of-the-art machine learning techniques and the association of the indicators with the clinical information. RESULTS: The features/hub genes finally selected were 25 in total and were used as the input to the classifiers. The models, then, were validated leading to very satisfactory results, with the best performing of them achieving 95% cross-validation and 93,75% external validation accuracy. CONCLUSIONS: Concluding, this exploratory data-driven approach attempts to make use of big genomic data by summarizing them in a way that is more understandable and facilitates their use by other techniques, such as Machine Learning. This method manages to extract a gene set that can predict the disease progression. The validation results of the proposed data-driven predictive models are very promising and constitute a significant contribution to medical research and personalized medicine.

Restrictions in the T-cell repertoire of chronic lymphocytic leukemia: high-throughput immunoprofiling supports selection by shared antigenic elements.

Immunoglobulin (IG) gene repertoire restrictions strongly support antigen selection in the pathogenesis of chronic lymphocytic leukemia (CLL). Given the emerging multifarious interactions between CLL and bystander T cells, we sought to determine whether antigen(s) are also selecting T cells in CLL. We performed a large-scale, next-generation sequencing (NGS) study of the T-cell repertoire, focusing on major stereotyped subsets representing CLL subgroups with undisputed antigenic drive, but also included patients carrying non-subset IG rearrangements to seek for T-cell immunogenetic signatures ubiquitous in CLL. Considering the inherent limitations of NGS, we deployed bioinformatics algorithms for qualitative curation of T-cell receptor rearrangements, and included multiple types of controls. Overall, we document the clonal architecture of the T-cell repertoire in CLL. These T-cell clones persist and further expand overtime, and can be shared by different patients, most especially patients belonging to the same stereotyped subset. Notably, these shared clonotypes appear to be disease-specific, as they are found in neither public databases nor healthy controls. Altogether, these findings indicate that antigen drive likely underlies T-cell expansions in CLL and may be acting in a CLL subset-specific context. Whether these are the same antigens interacting with the malignant clone or tumor-derived antigens remains to be elucidated.

A data model to support the evaluation of coordinating care EU programmes in the context of the ACT programme.

Advancing Care Coordination and Telehealth Deployment (ACT) is a European Union (EU) project, completed last October, which has developed a framework for evaluating and improving pioneering health care programs regarding coordinating care and telehealth (CC & TH) across specific EU regions. In this paper we present the key design decisions of the project's data model and the challenges faced. We focus on the definition of the multi-dimensional indicators in order to overcome data incompleteness and heterogeneity issues. Finally, we also suggest a graph based approach that could facilitate development of such data models in similar projects.