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Reznichenko A, Nair V, Eddy S, Fermin D, Tomilo M, Slidel T, Ju W, Henry I, Badal SS, Wesley JD, Liles JT, Moosmang S, Williams JM, Quinn CM, Bitzer M, Hodgin JB, Barisoni L, Karihaloo A, Breyer MD, Duffin KL, Patel UD, Magnone MC, Bhat R, Kretzler M. Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine. Kidney Int 2024; 105:1263-1278. [PMID: 38286178 DOI: 10.1016/j.kint.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/14/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Current 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.
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Affiliation(s)
- Anna Reznichenko
- Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
| | - Viji Nair
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sean Eddy
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Damian Fermin
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Mark Tomilo
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Timothy Slidel
- Early Computational Oncology, Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Wenjun Ju
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Ian Henry
- Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Johnna D Wesley
- Novo Nordisk Research Center Seattle, Seattle, Washington, USA
| | | | - Sven Moosmang
- Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Julie M Williams
- Bioscience Renal, Research and Early Development, Cardiovascular, Renal & Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Carol Moreno Quinn
- Medical Affairs Cardiovascular, Renal & Metabolism, Biopharmaceuticals Business, AstraZeneca, Cambridge, UK
| | - Markus Bitzer
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Jeffrey B Hodgin
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA; Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Laura Barisoni
- Department of Pathology, Division of AI and Computational Pathology, Duke University, Durham, North Carolina, USA; Department of Medicine, Division of Nephrology, Duke University, Durham, North Carolina, USA
| | - Anil Karihaloo
- Novo Nordisk Research Center Seattle, Seattle, Washington, USA
| | | | | | | | | | - Ratan Bhat
- Search and Evaluation, Cardiovascular Renal & Metabolism, Business Development & Licensing, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Matthias Kretzler
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
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Trevisani F, Simeoni M, Bettiga A, Cinque A, Floris M. Measurement of Glomerular Filtration Rate in Patients Undergoing Renal Surgery for Cancer: Estimated Glomerular Filtration Rate versus Measured Glomerular Filtration Rate in the Era of Precision Medicine. Kidney Blood Press Res 2024; 49:336-344. [PMID: 38636485 DOI: 10.1159/000538854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 03/20/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND In the era of precision medicine, determining reliable renal function assessment remains a critical and debatable issue, especially in nephrology and oncology. SUMMARY This paper delves into the significance of accurately measured glomerular filtration rate (mGFR) in clinical practice, highlighting its essential role in guiding medical decisions and managing kidney health, particularly in the context of renal cancer (RC) patients undergoing nephrotoxic anti-cancer drugs. The limitations and advantages of traditional glomerular filtration rate (GFR) estimation methods, primarily using serum biomarkers like creatinine and cystatin C, are discussed, emphasizing their possible inadequacy in cancer patients. Specifically, newer formulae designed for GFR estimation in cancer patients may not perform at best in RC patients. The paper explores various methods for direct GFR measurement, including the gold standard inulin clearance and alternatives like iohexol plasma clearance. KEY MESSAGE Despite the logistical challenges of these methods, their implementation is crucial for accurate renal function assessment. The paper concludes by emphasizing the need for continued research and innovation in GFR measurement methodologies to improve patient outcomes, particularly in populations with complex medical needs.
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Affiliation(s)
- Francesco Trevisani
- Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Unit of Urology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Biorek srl, San Raffaele Scientific Institute, Milan, Italy
| | - Mariadelina Simeoni
- Department of Translational Medical Sciences University of Campania "Luigi Vanvitelli, Naples, Italy
| | - Arianna Bettiga
- Unit of Urology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Matteo Floris
- Department of Nephrology, Dialysis, and Transplantation, ARNAS G. Brotzu, Cagliari, Italy
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Trevisani F, Di Marco F, Capitanio U, Dell'Antonio G, Cinque A, Larcher A, Lucianò R, Bettiga A, Vago R, Briganti A, Salonia A, Montorsi F, Porrini E. Renal histology across the stages of chronic kidney disease. J Nephrol 2021; 34:699-707. [PMID: 33394348 DOI: 10.1007/s40620-020-00905-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 11/04/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND The chronic kidney disease (CKD) classification represents a simple tool to evaluate kidney disease. However, it is not based on kidney histology and this might limit the correlation between renal function and histological damage. The aim of this study was to examine the presence and magnitude of the discordance between CKD classification and kidney histology. MATERIALS AND METHODS We retrospectively analyzed kidney parenchyma histology in a cohort of 200 patients who underwent radical nephrectomy for a kidney mass to observe its correlation with CKD classification. Kidney tissue of the unaffected part of the removed kidney was analyzed and classified with a chronicity score as described by Sethi et al. Then, all patients were classified according to the respective CKD stage using different equations: CKD-EPI, MDRD, FAS and MCQ. RESULTS Median age was 67 (57-75). Diabetes, hypertension and overweight were observed in 23%, 60% and 61%, respectively. The CKD-EPI equation stratified 30.5% (n = 61) of the subjects into CKD stage 1, 41.5% (n = 83) into CKD stage 2, 25.5% into CKD stage 3 (n = 51) and 2.5% into CKD stage 4-5 (n = 5). About 30-40% of the patients with CKD stage 3 had mild or no lesions in the histological evaluation (Chronicity Score = 0-1), whereas 7-10% of those with CKD stage 1 had moderate or severe histological lesions (Chronicity Score ≥ 3). Different patients with the same value of estimated glomerular filtration rate (eGFR) had either severe or no histological damage. CONCLUSIONS The variability of kidney histology observed within each CKD stage is not negligible. This may limit the reliability of the current CKD classification. More research is needed to clarify the relationship between CKD stages and kidney damage.
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Affiliation(s)
- Francesco Trevisani
- Unit of Urology, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federico Di Marco
- Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Umberto Capitanio
- Unit of Urology, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Alessandra Cinque
- Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Larcher
- Unit of Urology, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberta Lucianò
- Unit of Pathology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Arianna Bettiga
- Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Riccardo Vago
- Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alberto Briganti
- Unit of Urology, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Salonia
- Unit of Urology, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco Montorsi
- Unit of Urology, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Urological Research Institute (URI), Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Esteban Porrini
- Facultad de Medicina, ITB: Instituto de Tecnologías Biomédicas, Universidad de La Laguna, 38320, La Laguna, Spain.
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