Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project

Abstract

Chronic kidney disease (CKD) and acute kidney injury (AKI) are heterogeneous syndromes defined clinically by serial measures of kidney function. Each condition possesses strong histopathologic associations, including glomerular obsolescence or acute tubular necrosis, respectively. Despite such characterization, there remains wide variation in patient outcomes and treatment responses. Precision medicine efforts, as exemplified by the Kidney Precision Medicine Project (KPMP), have begun to establish evolving, spatially anchored, cellular and molecular atlases of the cell types, states, and niches of the kidney in health and disease. The KPMP atlas provides molecular context for CKD and AKI disease drivers and will help define subtypes of disease that are not readily apparent from canonical functional or histopathologic characterization but instead are appreciable through advanced clinical phenotyping, pathomic, transcriptomic, proteomic, epigenomic, and metabolomic interrogation of kidney biopsy samples. This perspective outlines the structure of the KPMP, its approach to the integration of these diverse datasets, and its major outputs relevant to future patient care.

Keywords: Acute kidney injury; chronic kidney disease; precision medicine.

Figure 1:. Overview of the goals of precision medicine efforts.

Precision medicine efforts such as the Kidney Precision Medicine Project (KPMP) seek to unite detailed demographic, social determinants of health (SDOH), clinical phenotyping, with next generation unbiased pathologic phenotyping (pathomics), and deep molecular datasets. Such integration has multiple benefits including refining the definition of disease subtypes based on new molecular information, developing biomarkers, and target selection for drug development.

Figure 2:. Schemata of integration between various data elements contributed by participants in precision medicine studies.

A) Dissociative single cell technologies determine specific cell type information which can be mapped back to tissue through spatial transcriptomic, proteomic, and metabolomic technologies which are further correlated with histologic assessment of the tissue and clinical phenotypes including advanced blood and urine biomarker assessment. B) A second approach to integration is the direct co-registration of measured transcript, protein, and histologic phenotypes in localized regions and cell niches defined within the tissue. C) Quantitative analysis of cell type and state mapping data may be performed in the analytical space without or with various degrees of supervision based on acquired knowledge. This analysis approach seeks to find common readouts between technologies with common outputs like cell class types or cell neighborhoods associated with specific molecular, clinical, or histopathologic features. D) Another analytical option is to adopt a systems biology approach wherein common pathways and disease processes are assessed across technologies.