Computational modeling of the multiple scales of disease processes is at the core of systems biomedicine (). To capture the quantitative properties of diseases (i.e., severity, manifestation of various signs and symptoms of the disease, and rate of progression), investigators in the field of oncology have linked clinical data from patients to molecular signatures identified from malignant cells or tissues.100
High-quality data on clinical and pathological features and specific molecular makers of diseases, which permitted the linking of phenotype-to-genotypic variation in the study of cancer biology, is still largely lacking in the field of nephrology. One notable exception is the European Renal cDNA Bank-Kröner–Fresenius Biopsy Bank, which has collected renal biopsy samples, as well as clinical and histology reports. Data compiled by the European Renal cDNA Bank-Kröner–Fresenius Biopsy Bank have been used in the study of several kidney diseases (reviewed in Neusser et al.101
). However, none of the published studies have fully linked clinical data to molecular makers in a systems-wide manner. Some studies have used one type of clinical data (for example, histological diagnosis, severity of renal impairment, rate of disease progression, extent of proteinuria, or specific lab values) and correlated with data on gene expression.102–104
Although this is a necessary starting point, the next step is to use a systems approach to profile the relationship of multiple clinical data points with omic data sets (for example, genome, transcriptome, or proteome) and construct a genotype–phenotype interaction map as it had been described for oncologic diseases.105,106
This relationship between clinical characteristics and molecular features can serve as bookend for the development of the kidney physiome and increase our understanding of the regulatory networks underlying the disease processes.
Systems biomedicine of kidney diseases
The major challenge in the field of systems medicine is the imbalance between the large number of omic data sets and the small sample size of patients with a well-defined disease category. This causes significant issues for data analysis and interpretation, leading to a so-called ‘multiple testing’ problem. To overcome this issue, it is critical to develop national and international collaborative studies for collecting large number of samples with strict disease classification criteria. An example of this approach is the Nephrotic Syndrome Study Network, which is a major multicenter initiative funded by the National Institutes of Health to enroll patients with nephrotic syndrome (http://clinicaltrials.gov
NCT01209000). The aims of the project are to investigate the underlying causes of the FSGS, minimal change disease, and membranous nephropathy, identify effective treatment for each disease, and develop meaningful approach to classify these disorders in order to inform the selection of the most effective treatments. Clinical data and biological samples (kidney biopsy, urine, and blood) will be collected prospectively. Transcriptomic profiles generated from the biological samples will be used to identify transcriptional networks and classify participants into distinct molecular subgroups. Such an approach to prospectively enroll patients, gather biological samples, and collect clinical data will be an invaluable resource for future investigations as newer technologies become available. With this we can expect to fill in the voids in our molecular understandings of a group of clinical disease categories that do not necessarily share the same molecular underpinning.
In the current practice of renal medicine, nephrologists formulate a clinical diagnosis, treatment plan, or prediction on disease progression using a rather restricted set of clinical parameters. These clinical findings are direct and indirect reflections of the underlying molecular and cellular processes. Different diseases often share similar clinical manifestation and cannot be distinguished from each other using the currently available knowledge and tools. To better characterize disease processes, software systems that are capable of handling the analysis of multivariant data and predict disease classification by machine-learning approaches have been developed. The goal is to achieve further characterization and classification of disease processes using currently available clinical and molecular data sets as biological classifiers. Instead of sorting patients into broad clinical categories, such as responder versus non-responder to a particular treatment, the systems biomedicine approach is to refine the categorization of diseases and be able to distinguish the genetic and phenotypic individuality of any given patient and tailor a specific therapeutic plan. Systems level analysis may identify a particular set of single-nucleotide polymorphism variants, transcriptomic profile, and urine proteomic and metabolomic characteristics that could be translated into clinical tests to help identify patients who are more or less likely to respond to a particular treatment or have a faster or slower progression of renal function loss leading to more personalized medicine.
Another major challenge in the field of chronic kidney disease is the lack of the early hard outcomes for clinical studies because disease progression usually takes decades. Biomarkers derived from omics studies could help predict the response of drugs at an early stage without waiting for the development of ‘hard outcomes’ such as doubling of creatinine or progression to end-stage renal disease, which might take years. Validation of omics biomarkers as early predictors of disease in prospective studies is required but hard to achieve, given the relatively low incidence of glomerular diseases, with the exception of DN. Therefore, the national and international collaborative studies are needed to study these diseases with low incidence.