SBIR-STTR Award

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. Public Health Relevance Statement PROJECT NARRATIVE Membranous nephropathy is a heterogenous autoimmune kidney disease that is caused in most cases by the presence of circulating pathogenic autoantibodies that react with antigens in podocytes or circulating autoantigens, leading to the formation and accumulation of pathogenic immune complex deposits around glomerular capillary loops. New findings suggest that these pathogenic autoantibodies are formed in each case by an autoimmune response against a single autoantigen, and that the titer of autoantibodies can serve as a useful biomarker for activity of disease that can in turn be used to monitor disease status and guide therapy. In this Phase I application, we will develop analytical methods using Data Independent Mass Spectrometry (DIA- MS) and machine learning that will ultimately enable efficient identification of autoantigen types in kidney biopsies from patients with membranous nephropathy of any type.

Project Terms:
Accounting ; Algorithms ; Antibodies ; Antigen-Antibody Complex ; Immune Complex ; Antigens ; immunogen ; Archives ; Autoantibodies ; autoimmune antibody ; autoreactive antibody ; self reactive antibody ; Autoantigens ; Autologous Antigens ; Self-Antigens ; Autoimmune Responses ; Biopsy ; Statistical Data Interpretation ; Statistical Data Analyses ; Statistical Data Analysis ; statistical analysis ; Diagnosis ; Disease ; Disorder ; Explosion ; Freezing ; Future ; Patient Care ; Patient Care Delivery ; Membranous Glomerulonephritis ; Extramembranous Glomerulopathy ; Membranous Glomerulonephropathy ; Membranous Glomerulopathy ; Membranous Nephropathy ; Goals ; Immunoglobulin G ; 7S Gamma Globulin ; IgG ; Immunoassay ; Kidney ; Kidney Urinary System ; renal ; Kidney Diseases ; Nephropathy ; Renal Disease ; kidney disorder ; renal disorder ; Laboratories ; Literature ; Lupus Nephritis ; Lupus Glomerulonephritis ; Methods ; United States National Institutes of Health ; NIH ; National Institutes of Health ; Nephritis ; Pathology ; Patients ; Peptides ; Proteins ; Proteinuria ; Publishing ; Resources ; Research Resources ; Mass Spectrum Analysis ; Mass Photometry/Spectrum Analysis ; Mass Spectrometry ; Mass Spectroscopy ; Mass Spectrum ; Mass Spectrum Analyses ; Testing ; Time ; Tissue Extracts ; Tissues ; Body Tissues ; G-substrate ; cerebellum protein substrate for cGMP dependent protein kinase ; protein G ; Measures ; Data Set ; Dataset ; analytical method ; base ; Phase ; Pythons ; Evaluation ; Training ; Glomerular Capillary ; Databases ; Data Bases ; data base ; Funding ; clinical Diagnosis ; Morphology ; Deposit ; Deposition ; tool ; Diagnostic ; machine learned ; Machine Learning ; Immunes ; Immune ; Techniques ; early detection ; Early Diagnosis ; rapid diagnosis ; cohort ; Biopsy Sample ; Biopsy Specimen ; novel ; Reporting ; Modeling ; Sampling ; Proteomics ; Pathogenicity ; Visceral Epithelial Cell ; glomerular visceral epithelial cell ; podocyte ; Incubated ; Data ; Reproducibility ; Flare ; Immunologics ; Immunochemical Immunologic ; Immunologic ; Immunological ; Immunologically ; Monitor ; Process ; Development ; developmental ; cost ; predictive modeling ; computer based prediction ; prediction model ; data acquisition ; treatment response ; response to treatment ; therapeutic response ; Biological Markers ; bio-markers ; biologic marker ; biomarker ; clinical practice ; disease diagnosis ; precision medicine ; precision-based medicine ; clinical diagnostics ; classification algorithm ; kidney biopsy ; renal biopsy ; data pipeline ; data analysis pipeline ; data processing pipeline ; learning classifier ; feature selection ; Autoimmune ; pathogenic autoantibodies ; Prognosis ;

The goal of this program is to initiate commercialization of a mass spectrometry (MS)-based workflow forthe diagnosis and classification of membranous nephropathy (MN), a leading cause of nephrotic syndrome,leading to kidney failure in a third of patients. Since the 1950's, most forms of MN were considered idiopathic inorigin, however, based on numerous publications in recent years, it is now broadly accepted that MN is causedby autoantibodies against approximately 20 different endogenous human proteins. The most common MNautoantigen is the phospholipase A2 receptor (PLA2R), and serological analysis of autoantibody titers againstthis protein has proven to be an indispensable serological tool for monitoring disease progression, severity, andremission (1-3). Arkana and others have identified additional novel autoantigens in MN cases (4-9), and giventhe success of PLA2R serological testing for personalized treatment, the characterization and clinical validationof the remaining MN autoantigens is urgently required. Furthermore, follow-up investigation of patients positivefor these newly identified autoantigens have been linked to life-threatening comorbidities including cancer andspecific autoimmune diseases. Because classifying patients based on MN autoantigen may reveal severeunderlying conditions, and PLA2R serology will identify only 70% of primary cases, Arkana has partnered withproteomics experts at the University of Arkansas for Medical Sciences (UAMS) to develop an unbiased, MS-based approach to classifying MN of all antigen types. Phase I studies evaluated nearly 300 tissue biopsies fromMN patients with previously determined autoantigens including PLA2R, thrombospondin type-1 domaincontaining 7A (THSD7A) (10), and exostosin1/2 (EXT1/2) (11), with triple negative cases also included. Afterevaluating a range of computational, statistical, and artificial intelligence data analysis modalities, ranked Z-scores were found to be an effective metric for autoantigen classification, correctly classifying over 95% ofsamples. During the proposed Phase II program, Arkana will continue collaborating with UAMS to translate thispowerful method to clinical practice and commercial availability. Quality control metrics will be developed for theinclusion/exclusion of incoming samples, to ensure the reliability and repeatability of MS analysis and supportverification of autoantigen classifications. The analytical pipeline will also be broadened to accommodateadditional autoantigen targets, many of which were identified by Arkana's recent MS analysis of hundreds ofbiobanked MN samples. Finally, the method will be transferred to Arkana's CLIA laboratory and validated in acomparative study with UAMS comprising approximately 300 blinded samples representing the full breadth ofMN autoantigen classes. Launching this workflow as a commercially available service will not only offer cliniciansunprecedented detail for the diagnosis and treatment of MN patients, but also reveal potentially life-threateningcomorbidities undetectable by prior generation strategies.

Public Health Relevance Statement:
Project Narrative Membranous nephropathy (MN) is due to autoantibodies against a range of endogenous proteins and leads to kidney failure in an alarming one-third of patients. Classifying a patient's disease by autoantigen can lead to dramatic improvements in prognosis through more personalized treatment as well as reveal underlying and life-threatening comorbidities such as cancer and specific autoimmune diseases. Arkana Laboratories has developed an MS based workflow capable of identifying autoantigens responsible for MN in a given patient with approximately 95% accuracy. Here, we will translate the assay and analytical workflow to the company's CLIA- certified laboratory prior to commercial launch, providing clinicians with a new approach to MN based on specific molecular etiologies to support improved, personalized treatment.

Project Terms: