Browsing by Author "Eddy, Sean"
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Item Open Access Rationale and design of the Nephrotic Syndrome Study Network (NEPTUNE) Match in glomerular diseases: designing the right trial for the right patient, today.(Kidney international, 2024-02) Trachtman, Howard; Desmond, Hailey; Williams, Amanda L; Mariani, Laura H; Eddy, Sean; Ju, Wenjun; Barisoni, Laura; Ascani, Heather K; Uhlmann, Wendy R; Spino, Cathie; Holzman, Lawrence B; Sedor, John R; Gadegbeku, Crystal; Subramanian, Lalita; Lienczewski, Chrysta C; Manieri, Tina; Roberts, Scott J; Gipson, Debbie S; Kretzler, Matthias; NEPTUNE investigatorsGlomerular diseases are classified using a descriptive taxonomy that is not reflective of the heterogeneous underlying molecular drivers. This limits not only diagnostic and therapeutic patient management, but also impacts clinical trials evaluating targeted interventions. The Nephrotic Syndrome Study Network (NEPTUNE) is poised to address these challenges. The study has enrolled >850 pediatric and adult patients with proteinuric glomerular diseases who have contributed to deep clinical, histologic, genetic, and molecular profiles linked to long-term outcomes. The NEPTUNE Knowledge Network, comprising combined, multiscalar data sets, captures each participant's molecular disease processes at the time of kidney biopsy. In this editorial, we describe the design and implementation of NEPTUNE Match, which bridges a basic science discovery pipeline with targeted clinical trials. Noninvasive biomarkers have been developed for real-time pathway analyses. A Molecular Nephrology Board reviews the pathway maps together with clinical, laboratory, and histopathologic data assembled for each patient to compile a Match report that estimates the fit between the specific molecular disease pathway(s) identified in an individual patient and proposed clinical trials. The NEPTUNE Match report is communicated using established protocols to the patient and the attending nephrologist for use in their selection of available clinical trials. NEPTUNE Match represents the first application of precision medicine in nephrology with the aim of developing targeted therapies and providing the right medication for each patient with primary glomerular disease.Item Open Access Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.(Kidney international, 2024-01) Reznichenko, Anna; Nair, Viji; Eddy, Sean; Fermin, Damian; Tomilo, Mark; Slidel, Timothy; Ju, Wenjun; Henry, Ian; Badal, Shawn S; Wesley, Johnna D; Liles, John T; Moosmang, Sven; Williams, Julie M; Quinn, Carol Moreno; Bitzer, Markus; Hodgin, Jeffrey B; Barisoni, Laura; Karihaloo, Anil; Breyer, Matthew D; Duffin, Kevin L; Patel, Uptal D; Magnone, Maria Chiara; Bhat, Ratan; Kretzler, MatthiasCurrent classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.