Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction. METHOD: This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods. RESULTS: Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques. CONCLUSIONS: This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future.

Network analysis with biological data of cancer patients: A scoping review.

BACKGROUND & OBJECTIVE: Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases. METHODS & MATERIALS: The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected. RESULTS: & Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

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.

Two dimensional wavelet energy analysis on a beat to beat basis: application to Atrial Fibrillation.

Atrial Fibrillation (AF) is a condition in which heart rhythm is not associated with normal sinoatrial (SA) node pacemaker but it derives from different areas on the atrium, often from the area of Pulmonary veins (PVs) A way to eliminate the influence of PVs in the inducement of AF is the PVs isolation surgery. In this study, an effort is made towards investigating the morphology and dynamics of P-waves, when the potentially arrhythmogenic tissue in PVs is involved or isolated via ablation. For this reason, 20 patients who were subjected to PVs isolation were studied, via vectrorcardiography recordings obtained before and after the ablation. Wavelet energies for five frequency bands were analyzed, using a two dimensional representation. The proposed technique was applied for the analysis of wavelet energies in consecutive beats, and their correlation with the RR interval. Features for the evaluation of those plots were extracted, such as the axes of a fitted to the plot ellipse and the center of the mass. The statistical analysis demonstrated significant differences between the groups, which imply the modification of the atrial substrate concerning electrical conduction toward to a more stable condition.