Biomarkers

Application of machine learning in understanding atherosclerosis: Emerging insights

Biological processes are incredibly complex—integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.

Computer-aided targeting of the PI3K/Akt/mTOR pathway: toxicity reduction and therapeutic opportunities

The PI3K/Akt/mTOR pathway plays an essential role in a wide range of biological functions, including metabolism, macromolecular synthesis, cell growth, proliferation and survival. Its versatility, however, makes it a conspicuous target of many pathogens; and the consequential deregulations of this pathway often lead to complications, such as tumorigenesis, type 2 diabetes and cardiovascular diseases. Molecular targeted therapy, aimed at modulating the deregulated pathway, holds great promise for controlling these diseases, though side effects may be inevitable, given the ubiquity of the pathway in cell functions. Here, we review a variety of factors found to modulate the PI3K/Akt/mTOR pathway, including gene mutations, certain metabolites, inflammatory factors, chemical toxicants, drugs found to rectify the pathway, as well as viruses that hijack the pathway for their own synthetic purposes. Furthermore, this evidence of PI3K/Akt/mTOR pathway alteration and related pathogenesis has inspired the exploration of computer-aided targeting of this pathway to optimize therapeutic strategies. Herein, we discuss several possible options, using computer-aided targeting, to reduce the toxicity of molecularly-targeted therapy, including mathematical modeling, to reveal system-level control mechanisms and to confer a low-dosage combination therapy, the potential of PP2A as a therapeutic target, the formulation of parameters to identify patients who would most benefit from specific targeted therapies and molecular dynamics simulations and docking studies to discover drugs that are isoform specific or mutation selective so as to avoid undesired broad inhibitions. We hope this review will stimulate novel ideas for pharmaceutical discovery and deepen our understanding of curability and toxicity by targeting the PI3K/Akt/mTOR pathway.

Clinical Outcome 3 Years After Autologous Chondrocyte Implantation Does Not Correlate With the Expression of a Predefined Gene Marker Set in Chondrocytes Prior to Implantation but Is Associated With Critical Signaling Pathways

Background:

There is a need for tools to predict the chondrogenic potency of autologous cells for cartilage repair.

Purpose:

To evaluate previously proposed chondrogenic biomarkers and to identify new biomarkers in the chondrocyte transcriptome capable of predicting clinical success or failure after autologous chondrocyte implantation.

Study Design:

Controlled laboratory study and case-control study; Level of evidence, 3.

Methods:

Five patients with clinical improvement after autologous chondrocyte implantation and 5 patients with graft failures 3 years after implantation were included. Surplus chondrocytes from the transplantation were frozen for each patient. Each chondrocyte sample was subsequently thawed at the same time point and cultured for 1 cell doubling, prior to RNA purification and global microarray analysis. The expression profiles of a set of predefined marker genes (ie, collagen type II α1 [COL2A1], bone morphogenic protein 2 [BMP2], fibroblast growth factor receptor 3 [FGFR3], aggrecan [ACAN], CD44, and activin receptor–like kinase receptor 1 [ACVRL1]) were also evaluated.

Results:

No significant difference in expression of the predefined marker set was observed between the success and failure groups. Thirty-nine genes were found to be induced, and 38 genes were found to be repressed between the 2 groups prior to autologous chondrocyte implantation, which have implications for cell-regulating pathways (eg, apoptosis, interleukin signaling, and β-catenin regulation).

Conclusion:

No expressional differences that predict clinical outcome could be found in the present study, which may have implications for quality control assessments of autologous chondrocyte implantation. The subtle difference in gene expression regulation found between the 2 groups may strengthen the basis for further research, aiming at reliable biomarkers and quality control for tissue engineering in cartilage repair.

Clinical Relevance:

The present study shows the possible limitations of using gene expression before transplantation to predict the chondrogenic and thus clinical potency of the cells. This result is especially important as the chondrogenic potential of the chondrocytes is currently part of quality control measures according to European and American legislations regarding advanced therapies.

Genetic biomarkers in epilepsy

The identification of valid biomarkers for outcome prediction of diseases and improvement of drug response, as well as avoidance of side effects is an emerging field of interest in medicine. The concept of individualized therapy is becoming increasingly important in the treatment of patients with epilepsy, as predictive markers for disease prognosis and treatment outcome are still limited. Currently, the clinical decision process for selection of an antiepileptic drug (AED) is predominately based on the patient's epileptic syndrome and side effect profiles of the AEDs, but not on effectiveness data. Although standard dosages of AEDs are used, supplemented, in part, by therapeutic monitoring, the response of an individual patient to a specific AED is generally unpredictable, and the standard care of patients in antiepileptic treatment is more or less based on trial and error. Therefore, there is an urgent need for valid predictive biomarkers to guide patient-tailored individualized treatment strategies in epilepsy, a research area that is still in its infancy. This review focuses on genomic factors as part of an individual concept for AED therapy summarizing examples that influence the prognosis of the disease and the response to AEDs, including side effects.