The complexity of distribution of inflammatory cells in the kidney is not well captured by conventional semiquantitative visual assessment. This study aims to computationally quantify the topology of lymphocytic inflammation and tested its clinical relevance.

N=333 NEPTUNE/CureGN participants (N=155 focal segmental glomerulosclerosis (FSGS) and N=178 Minimal Change Disease (MCD) with available clinical/demographic data and 1 Hematoxylin & Eosin-stained whole slide image (WSI), were included. Deep learning models were applied to segment cortex and lymphocytes. Graph modeling, where nodes were defined as lymphocytes and edges as the spatial connections between cortical lymphocytes, were applied to all WSIs. We then developed a novel graph-based habitat clustering algorithm to identify dense vs. sparse lymphocytic habitats. From each habitat, 26 high-throughput quantitative pathomic features were extracted to capture cell density, connectivity, clustering, and centrality.

The association of these pathomic features with disease progression (40% eGFR decline or kidney replacement therapy) was assessed using LASSO-regularized Cox proportional hazards models. Clinical and demographic characteristics were added as potential confounders. Kaplan-Meier survival analysis with log-rank test was used to evaluate risk stratification. Two validation strategies were applied: (i) training on NEPTUNE with external validation on CureGN data, and (ii) using an 80/20 data partition of the combined datasets for training and validation, respectively.

Multivariable Cox models integrating clinical/demographic variables with graph features achieved validation concordance index of 0.736±0.072 in the CureGN external validation and 0.757±0.071 in the combined validation dataset. The average degree feature (overall connectivity) in dense habitat and k-core feature (clustering pattern strength) in sparse habitat revealed consistent association with clinical outcome.

The topological characterization of lymphocytic inflammation identifies immune habits, capturing the complexity of pattern of inflammation beyond human vision. These pathomic/topology signatures represent potential digital biomarkers that can enhance our ability to prognosticate/predict clinical outcome in MCD/FSGS.

JZ has received financial support from NIDDK and NCATS for the submitted work and received grants from Boehringer- Ingelheim, Travere Therapeutics, Reliant Glycosciences, HiBio, and Takeda Pharmaceuticals in the past 3 years. JZ has also received an honorarium for technical expert panel participation from Booz Allen Hamilton. LM has received financial support from NIDDK and NCATS for the submitted work and received grants from Boehringer- Ingelheim, Travere Therapeutics, Reliant Glycosciences, HiBio and Takeda Pharmaceuticals. LM has also received consulting fee from Novartis, Calliditas and Travere and payment for educational events from WebMD/Medscape and MedLive/PlatformQ. LBH has received grants from NIDDK CureGN-Penn PCC, NIDDK Nephrotic Syndrome Rare Disease Clinical Research Network III and NIDDK Computational Pathology for Proteinuric Glomerulopathies. Additionally, LBH holds a leadership role in the Scientific Advisory Board of NephCure Kidney International. JH has received grants from NIH and Department of Defense. LB has received grants from NIH fundings listed in Acknowledgment, Nephcure and Haller Foundation. LB has also participated on a Data Safety Monitoring Board or Advisory Board for Vertex and holds a leadership role in the International Society of Glomerular Diseases.

1. The National Institute of Health (NIH) under the following awards: (i) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) under the award number 2R01DK118431-04; and (ii) the National Library of Medicine (NLM) under award numbers R01LM013864. 2. The Nephrotic Syndrome Study Network (NEPTUNE) is part of the Rare Diseases Clinical Research Network (RDCRN), which is funded by the National Institutes of Health (NIH) and led by the National Center for Advancing Translational Sciences (NCATS) through its Division of Rare Diseases Research Innovation (DRDRI). NEPTUNE is funded under grant number U54DK083912 as a collaboration between NCATS and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Additional funding and/or programmatic support is provided by the University of Michigan, NephCure Kidney International, Alport Syndrome Foundation, and the Halpin Foundation. RDCRN consortia are supported by the RDCRN Data Management and Coordinating Center (DMCC), funded by NCATS and the National Institute of Neurological Disorders and Stroke (NINDS) under U2CTR002818. 3. Funding for the CureGN consortium is provided by U24DK100845 (formerly UM1DK100845), U01DK100846 (formerly UM1DK100846), U01DK100876 (formerly UM1DK100876), U01DK100866 (formerly UM1DK100866), and U01DK100867 (formerly UM1DK100867) from the NIDDK/NIH. Dates of funding for first phase of CureGN was 9/16/2013-5/31/2019. 4. Patient Recruitment is supported by NephCure. 5. Additional support was also provided by NephCure and the Henry E. Haller, Jr. Foundation.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The DUHS IRB has determined that the following protocol meets the definition of research not involving human subjects as described in 45 CFR 46.102(f), 21 CFR 56.102(e) and 21 CFR 812.3(p) and satisfies the Privacy Rule as described in 45CFR164.514. Protocol ID: Pro00108417 Reference ID: Pro00108417-INIT-1.0 Protocol Title: Computational Pathology of Proteinuric Disease II Principal Investigator: Laura Barisoni 6810708, M.D. This IRB Declaration is in effect from May 31, 2021 and does not expire. However, please be advised that any changes to the proposed research will require re-review by the IRB.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All data produced in the present study are available upon reasonable request to the authors