Abstract: PO2429
Molecular Stratification of CKD
Session Information
- CKD: Qualitative and Quantitative Observational Studies
November 04, 2021 | Location: On-Demand, Virtual Only
Abstract Time: 10:00 AM - 12:00 PM
Category: CKD (Non-Dialysis)
- 2102 CKD (Non-Dialysis): Clinical, Outcomes, and Trials
Authors
- Reznichenko, Anna, Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
- Nair, Viji, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Eddy, Sean, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Tomilo, Mark, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Slidel, Tim, Early Computational Oncology, Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Gothenburg, United Kingdom
- Ju, Wenjun, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Conway, James, Early Computational Oncology, Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Gaithersburg, Maryland, United States
- Badal, Shawn S., Gilead Sciences Inc, Foster City, California, United States
- Liles, John T., Gilead Sciences Inc, Foster City, California, United States
- Wesley, Johnna D., Novo Nordisk Inc Seattle, Seattle, Washington, United States
- Moosmang, Sven, Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
- Williams, Julie, Bioscience Renal, Research and Early Development, Cardiovascular, Renal & Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
- Moreno Quinn, Carol Patricia, Medical Affairs Cardiovascular, Renal & Metabolism, Biopharmaceuticals Business, AstraZeneca, Cambridge, United Kingdom
- Karihaloo, Anil K., Novo Nordisk Inc Seattle, Seattle, Washington, United States
- Breyer, Matthew Douglas, Janssen Research and Development LLC, Raritan, New Jersey, United States
- Duffin, Kevin L., Eli Lilly and Company, Indianapolis, Indiana, United States
- Patel, Uptal D., Gilead Sciences Inc, Foster City, California, United States
- Magnone, Maria chiara, Janssen Research and Development LLC, Raritan, New Jersey, United States
- Bhat, Ratan, Search and Evaluation, Cardiovascular Renal & Metabolism, Business Development & Licensing, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
- Kretzler, Matthias, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
Group or Team Name
- Renal Precompetitive Consortium (RPC2)
Background
Current classification of chronic kidney disease (CKD) into stages based on the indirect measures of kidney functional state, estimated glomerular filtration rate and albuminuria, is agnostic to the heterogeneity of underlying etiologies, histopathology, and molecular processes. We used genome-wide transcriptomics from patients’ kidney biopsies, directly reflecting kidney biological processes, to stratify patients from three independent CKD cohorts.
Methods
Self-Organizing Maps (SOM), an artificial neural network machine-learning algorithm, assembled CKD patients into four novel subgroups, molecular categories, based on the similarity of their kidney transcriptomics profiles.
Results
The unbiased, molecular categories were present across CKD stages and histopathological diagnoses, highlighting heterogeneity of conventional clinical subgroups at the molecular level. CKD molecular categories were distinct in terms of biological pathways, transcriptional regulation and associated kidney cell types, indicating that the molecular categorization is founded on biologically meaningful mechanisms. Importantly, our results revealed that not all biological pathways are equally activated in all patients; instead, different pathways could be more dominant in different subgroups and thereby differentially influencing disease progression and outcomes.
Conclusion
This first kidney-centric unbiased categorization of CKD paves the way to an integrated clinical, morphological and molecular diagnosis. This is a key step towards enabling precision medicine for this heterogeneous condition with the potential to advance biological understanding, clinical management, and drug development, as well as establish a roadmap for molecular reclassification of CKD and other complex diseases.
Funding
- Commercial Support –