Development of a Precision Medicine-based Diagnostic Tool for Membranous Nephropathy

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Summary/Abstract The goal of this project is to develop a precision medicine approach to the rapid diagnosis of membranous nephropathy (MN) using automated statistical analysis of proteomic data obtained from kidney biopsies. This approach uses data-independent acquisition mass spectrometry (DIA-MS) and an algorithmic data pipeline capable of efficiently determining the most likely MN antigen types present in kidney biopsy tissue. MN is a heterogenous autoimmune kidney disease that is caused in most cases by the presence of circulating pathogenic autoantibodies that react with podocyte antigens leading to the formation and accumulation of pathogenic immune complexes around glomerular capillary loops. Using the example of PLA2R-type MN, determination of antigen type has been shown to be important for diagnosis, monitoring response to treatment and early detection of disease flares. Historically, determination of MN antigen type has been performed by immunostaining; however, this has become impractical due to the discovery of at least 17 antigen types. There often is not enough tissue in the biopsy sample to conduct this number of immunostains, and moreover the immunostaining process is both time and resource intensive. The use of DIA-MS provides a novel proteomics approach to antigen typing in which immune complexes are captured by elution from frozen biopsy tissue, digested into tryptic peptides, and then measured by DIA-MS. Candidate MN antigens are identified using algorithmic classification and then validated in a final immunostaining step to confirm the candidate antigen. Our preliminary studies indicate that this is a robust approach; however, the method is not scalable without a similarly robust data analysis pipeline. In this Phase I project, we will optimize the DIA-MS method and then collect quantitative data from known cases of the most common types of MN that can be used to develop, train, test and optimize algorithmic classification models using a machine learning (ML) approach. In order to train the ML models, we will collect DIA-MS protein abundance data from 50 samples each of PLA2R, THSD7A and Exostosin types of MN, as well as 50 samples that are negative for each of these antigens as controls. In the Phase II, we will build complete datasets for all known antigen types of MN and optimize the ML classifier model for diagnostic workflows. Successful completion of these aims will result in the development a comprehensive method to efficiently classify MN cases of any antigen type. These tools will advance the practice of renal pathology from a largely morphology-based approach of diagnosing disease to a precision medicine-based proteomics approach that will efficiently provide actionable information to clinicians caring for patients with MN.