Systems biology approaches to identify disease mechanisms and facilitate targeted therapy in the management of glomerular disease

Abstract

Purpose of review: Current clinical pathological classifications of glomerular diseases are inadequate at predicting patient disease progression or response to therapy. With the advent of precision medicine and its successes in oncology, it is important to understand if similar approaches in glomerular diseases can improve patient management. The purpose of this review is to summarize approaches to obtain comprehensive molecular profiles from human biopsies and utilize them to define the pathophysiology of glomerular failure.

Recent findings: Multicenter research networks have provided the framework to capture both prospective clinical disease course and patterns of end organ damage in biopsy cohorts. With these sample and data sets in hand, efforts are progressing towards molecular disease characterization, identification of novel prognostic marker, development of more precise clinical trials and discovery of predictive biomarkers to more effectively stratify patients to appropriate treatment regiments. Partnerships between academia, public funding agencies and private companies seek to improve timelines and maximize resources while also leveraging domain expertise in an integrated framework to holistically understand disease.

Summary: The application of system biology techniques within team science frameworks across disciplines and continents will seek to realize the impact of precision medicine to bring urgently needed novel therapeutic options to patients with glomerular disease.

FIGURE 1.

Precision medicine in glomerular diseases. In glomerular disease, molecular profiling technologies can be key to defining prospective tissue at the different layers of molecular characterization. This approach can paint a more global multidimensional image of a patient’s disease, which can lead to improved molecular disease characterization and trial design as well as leading to novel prognostic and predictive biomarkers.