Immune checkpoint inhibitor therapy in patients with cancer and pre-existing systemic sclerosis.

OBJECTIVE: Immune checkpoint inhibitor (ICI) therapies have dramatically improved outcomes in multiple cancers. ICI's mechanism of action involves immune system activation to augment anti-tumor immunity. Patients with pre-existing autoimmune diseases, such as systemic sclerosis (SSc), were excluded from initial ICI clinical trials due to concern that such immune system activation could precipitate an autoimmune disease flare or new, severe immune related adverse events (irAE). In the present study, we report our experience with ICIs in patients with pre-existing SSc. METHODS: Patients with SSc who received ICI therapy for cancer were identified from the Johns Hopkins Scleroderma Center Research Registry. Through chart review and prespecified definitions, we identified whether patients experienced worsening SSc activity or new irAEs. SSc disease activity worsening was pre-defined as an increase in modified Rodnan skin score (mRSS), new scleroderma renal crisis, progression of interstitial lung disease (ILD) on CT scan, increased Raynaud's phenomenon frequency or severity, new pulmonary hypertension, or myositis flare. IrAEs also included active inflammatory arthritis and dermatitis. RESULTS: Eight patients with SSc who received ICI therapy for cancer were included. Overall, SSc symptoms remained stable during and after ICI therapy. None of the patients with long-standing sine or limited cutaneous SSc (lcSSc) had progressive skin thickening after ICI therapy. One patient, who was early in his diffuse cutaneous SSc (dcSSc) disease course, experienced worsening skin thickening and renal crisis. Three patients (38 %) experienced a total of five irAEs (grade 2: diarrhea, mucositis and dermatitis; grade 3: pneumonitis, and grade 4: nephritis). The patient with grade 4 nephritis developed scleroderma renal crisis and immune checkpoint related nephritis simultaneously. There were no deaths due to irAEs. CONCLUSION: In this study, ICI therapy was well tolerated in patients with longstanding, sine or lcSSc. IrAE were common but generally manageable. Patients with early, active SSc may be at greater risk from ICI therapy, but more research is needed.

Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing.

Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured proximal tubule cells fails to undergo normal repair and assumes a proinflammatory and profibrotic phenotype that may promote fibrosis and chronic kidney disease. The healthy to failed repair change is marked by cell state-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq sequencing offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We develop a regularized regression approach to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generate a single nucleus multiomic dataset from seven adult human kidney samples and apply our method to study drivers of a failed injury response associated with kidney disease. We demonstrate that our approach is a highly effective tool for predicting key cis- and trans-regulatory elements underpinning the healthy to failed repair transition and use it to identify NFAT5 as a driver of the maladaptive proximal tubule state.

Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis.

Autosomal dominant polycystic kidney disease (ADPKD) is the leading genetic cause of end stage renal disease characterized by progressive expansion of kidney cysts. To better understand the cell types and states driving ADPKD progression, we analyze eight ADPKD and five healthy human kidney samples, generating single cell multiomic atlas consisting of ~100,000 single nucleus transcriptomes and ~50,000 single nucleus epigenomes. Activation of proinflammatory, profibrotic signaling pathways are driven by proximal tubular cells with a failed repair transcriptomic signature, proinflammatory fibroblasts and collecting duct cells. We identify GPRC5A as a marker for cyst-lining collecting duct cells that exhibits increased transcription factor binding motif availability for NF-κB, TEAD, CREB and retinoic acid receptors. We identify and validate a distal enhancer regulating GPRC5A expression containing these motifs. This single cell multiomic analysis of human ADPKD reveals previously unrecognized cellular heterogeneity and provides a foundation to develop better diagnostic and therapeutic approaches.

Neuroblastoma suppressor of tumorigenicity 1 is a circulating protein associated with progression to end-stage kidney disease in diabetes.

Circulating proteins associated with transforming growth factor-ß (TGF-ß) signaling are implicated in the development of diabetic kidney disease (DKD). It remains to be comprehensively examined which of these proteins are involved in the pathogenesis of DKD and its progression to end-stage kidney disease (ESKD) in humans. Using the SOMAscan proteomic platform, we measured concentrations of 25 TGF-ß signaling family proteins in four different cohorts composed in total of 754 Caucasian or Pima Indian individuals with type 1 or type 2 diabetes. Of these 25 circulating proteins, we identified neuroblastoma suppressor of tumorigenicity 1 (NBL1, aliases DAN and DAND1), a small secreted protein known to inhibit members of the bone morphogenic protein family, to be most strongly and independently associated with progression to ESKD during 10-year follow-up in all cohorts. The extent of damage to podocytes and other glomerular structures measured morphometrically in 105 research kidney biopsies correlated strongly with circulating NBL1 concentrations. Also, in vitro exposure to NBL1 induced apoptosis in podocytes. In conclusion, circulating NBL1 may be involved in the disease process underlying progression to ESKD, and its concentration in circulation may identify subjects with diabetes at increased risk of progression to ESKD.

Circulating Plasma Biomarkers in Biopsy-Confirmed Kidney Disease.

BACKGROUND AND OBJECTIVES: Biomarkers for noninvasive assessment of histopathology and prognosis are needed in patients with kidney disease. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using a proteomics assay, we measured a multimarker panel of 225 circulating plasma proteins in a prospective cohort study of 549 individuals with biopsy-confirmed kidney diseases and semiquantitative assessment of histopathology. We tested the associations of each biomarker with histopathologic lesions and the risks of kidney disease progression (defined as ≥40% decline in eGFR or initiation of KRT) and death. RESULTS: After multivariable adjustment and correction for multiple testing, 46 different proteins were associated with histopathologic lesions. The top-performing markers positively associated with acute tubular injury and interstitial fibrosis/tubular atrophy were kidney injury molecule-1 (KIM-1) and V-set and Ig domain-containing protein 2 (VSIG2), respectively. Thirty proteins were significantly associated with kidney disease progression, and 35 were significantly associated with death. The top-performing markers for kidney disease progression were placental growth factor (hazard ratio per doubling, 5.4; 95% confidence interval, 3.4 to 8.7) and BMP and activin membrane-bound inhibitor (hazard ratio, 3.0; 95% confidence interval, 2.1 to 4.2); the top-performing markers for death were TNF-related apoptosis-inducing ligand receptor-2 (hazard ratio, 2.9; 95% confidence interval, 2.0 to 4.0) and CUB domain-containing protein-1 (hazard ratio, 2.4; 95% confidence interval, 1.8 to 3.3). CONCLUSION: We identified several plasma protein biomarkers associated with kidney disease histopathology and adverse clinical outcomes in individuals with a diverse set of kidney diseases. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_12_28_CJN09380721.mp3.

Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens.

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists' estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard.

Beyond Panel-Based Testing: Exome Analysis Increases Sensitivity for Diagnosis of Genetic Kidney Disease.

Background: Next-generation sequencing (NGS) is a useful tool for evaluating patients with suspected genetic kidney disease. Clinical practice relies on the use of targeted gene panels that are ordered based on patient presentation. We compare the diagnostic yield of clinical panel-based testing to exome analysis. Methods: In total, 324 consecutive patients underwent physician-ordered, panel-based NGS testing between December 2014 and October 2018. Gene panels were available for four clinical phenotypes, including atypical hemolytic uremic syndrome (n=224), nephrotic syndrome (n=56), cystic kidney disease (n=26), and Alport syndrome (n=13). Variants were analyzed and clinical reports were signed out by a pathologist or clinical geneticist at the time of testing. Subsequently, all patients underwent retrospective exome analysis to detect additional clinically significant variants in kidney disease genes that were not analyzed as part of the initial clinical gene panel. Resulting variants were classified according to the American College of Medical Genetics and Genomics 2015 guidelines. Results: In the initial physician-ordered gene panels, we identified clinically significant pathogenic or likely pathogenic variants in 13% of patients (n=42/324). CFHR3-CFHR1 homozygous deletion was detected in an additional 13 patients with aHUS without a pathogenic or likely pathogenic variant. Diagnostic yield of the initial physician-ordered gene panel was 20% and varied between groups. Retrospective exome analysis identified 18 patients with a previously unknown pathogenic or likely pathogenic variant in a kidney disease gene and eight patients with a high-risk APOL1 genotype. Overall, retrospective exome analysis increased the diagnostic yield of panel-based testing from 20% to 30%. Conclusions: These results highlight the importance of a broad and collaborative approach between the clinical laboratory and their physician clients that employs additional analysis when a targeted panel of kidney disease-causing genes does not return a clinically meaningful result.

Kidney and organoid single-cell transcriptomics: the end of the beginning.

Single-cell RNA sequencing (scRNA-seq) technologies are increasingly being applied to reveal cellular heterogeneity in kidney development and disease. In just the last year, multiple scRNA-seq datasets have been generated from kidney organoids, developing mouse and human kidney, adult kidney, and kidney cancer. The data generated enables a much deeper understanding of biological processes within and between cells. It has also elucidated unforeseen cell lineage relationships, defined the presence of off-target cell types in kidney organoids, and revealed a diverse inflammatory response in a human kidney allograft undergoing rejection. This review summarizes the recent rapid progress in scRNA-seq of the kidney and outlines future directions for single-cell technologies as applied to the kidney.

The single-cell transcriptomic landscape of early human diabetic nephropathy.

Diabetic nephropathy is characterized by damage to both the glomerulus and tubulointerstitium, but relatively little is known about accompanying cell-specific changes in gene expression. We performed unbiased single-nucleus RNA sequencing (snRNA-seq) on cryopreserved human diabetic kidney samples to generate 23,980 single-nucleus transcriptomes from 3 control and 3 early diabetic nephropathy samples. All major cell types of the kidney were represented in the final dataset. Side-by-side comparison demonstrated cell-type-specific changes in gene expression that are important for ion transport, angiogenesis, and immune cell activation. In particular, we show that the diabetic thick ascending limb, late distal convoluted tubule, and principal cells all adopt a gene expression signature consistent with increased potassium secretion, including alterations in Na+/K+-ATPase, WNK1, mineralocorticoid receptor, and NEDD4L expression, as well as decreased paracellular calcium and magnesium reabsorption. We also identify strong angiogenic signatures in glomerular cell types, proximal convoluted tubule, distal convoluted tubule, and principal cells. Taken together, these results suggest that increased potassium secretion and angiogenic signaling represent early kidney responses in human diabetic nephropathy.

Breast cancer histopathology is predictive of low-risk Oncotype Dx recurrence score.

BACKGROUND: Oncotype Dx is a genetic test that has been incorporated into the 2017 AJCC breast cancer staging system for ER positive, HER2-negative, lymph node-negative patients to predict the risk of recurrence. Recent data suggest that immunohistochemistry (ER, PR, HER2, and Ki-67) and histologic subtype may identify patients that will not benefit from Oncotype Dx testing. METHODS: A total of 371 patients underwent Oncotype Dx testing at our institution from 2012 to 2016. Oncotype recurrence score was categorized as low- (ORS = 0-10), intermediate- (11-25), or high risk (26-100). Invasive carcinomas were categorized based on histologic subtype as "favorable" (mucinous, tubular, cribriform, tubulolobular, and lobular) and "unfavorable" (ductal, mixed ductal and lobular, and micropapillary carcinoma). All cases were estrogen receptor positive and HER2-negative. Clinical and histologic predictors of low-risk ORS were assessed in univariate and multivariate logistic regression. RESULTS: A total of 371 patients were categorized by ORS as low risk (n = 85, 22.9%), intermediate risk (n = 244, 65.8%), and high risk (n = 42, 11.3%). The histologic subtypes with the highest percentage of high-risk ORS were invasive micropapillary (n = 4/17, 23.5%), pleomorphic lobular (n = 2/10, 20%), and ductal carcinoma (n = 28/235, 11.9%). Low-grade invasive carcinomas with favorable histology rarely had a high-risk ORS (n = 1/97, 1%). In a simple multivariable model, favorable histologic subtype (OR = 2.39, 95% CI: 1.10 to 5.15, P = 0.026), and histologic grade (OR = 1.76, 95% CI: 1.07 to 2.90, P = 0.025) were the only significant predictors of an ORS less than 11 in estrogen receptor positive, HER2-negative, and lymph node-negative patients. CONCLUSION: We question the utility of performing Oncotype Dx in subtypes of invasive carcinoma that are associated with excellent prognosis. We propose that immunohistochemistry for ER, PR, and HER2 is sufficient for patients with low-grade invasive carcinomas and can be used as a surrogate for Oncotype Dx.

Interstitial inflammation and interstitial fibrosis and tubular atrophy predict renal survival in lupus nephritis.

BACKGROUND: This study examines the effect of interstitial inflammation and interstitial fibrosis and tubular atrophy on renal survival in lupus nephritis. METHODS: Baseline characteristics, initial (n = 301) and repeat biopsies (n = 94) and clinical outcomes for patients with biopsy-proven lupus nephritis from 1998 to 2014 were retrospectively collected from the medical record. Clinical and morphologic variables were evaluated using a Cox proportional hazards model and multiple imputation to address missing data. Renal survival was defined as the time from initial biopsy to end-stage renal disease [estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m2], dialysis or transplant. RESULTS: A total of 218 patients had follow-up and Class IV had worse renal survival, especially in patients with active and chronic glomerular lesions {relative to non-IV; Class IV-A: hazard ratio [HR] 0.92 [95% confidence interval (CI) 0.41-2.04], Class IV-AC: HR 5.02 [95% CI 2.70-9.36]}. Interstitial inflammation grade [relative to interstitial inflammation <5%; interstitial inflammation 5-25%: HR 2.36 (95% CI 1.13-4.91), interstitial inflammation 25-50%: HR 3.84 (95% CI 1.53-9.62), interstitial inflammation >50%: HR 7.67 (95% CI 3.75-15.67)] and increased interstitial fibrosis and tubular atrophy (IFTA) category [relative to IFTA <5%; IFTA 5-25%: HR 3.93 (95% CI 1.58-9.75), IFTA 25-50%: HR 4.01 (95% CI 1.37-11.70), IFTA >50%: HR 13.99 (95% CI 4.91-39.83)] predicted worse renal survival among all patients and those with Class IV on initial and repeat biopsy (n = 94) in a dose-dependent manner. Interstitial inflammation grade and IFTA category were significant predictors of renal survival in a multivariable model adjusted for age, gender, race, ethnicity and serum creatinine. CONCLUSIONS: Interstitial inflammation and IFTA independently affect renal survival and grading these lesions stratifies risk within the International Society of Nephrology and Renal Pathology Society classification of lupus nephritis.