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As recently as 2002, most cases of primary membranous nephropathy (MN), a relatively common cause of nephrotic syndrome in adults, were considered idiopathic. We now recognize that MN is an organ-specific autoimmune disease in which circulating autoantibodies bind to an intrinsic antigen on glomerular podocytes and form deposits of immune complexes in situ in the glomerular capillary walls. Here we define the clinical and pathological features of MN and describe the experimental models that enabled the discovery of the major target antigen, the M-type phospholipase A2 receptor 1 (PLA2R). We review the pathophysiology of experimental MN and compare and contrast it with the human disease. We discuss the diagnostic value of serological testing for anti-PLA2R and tissue staining for the redistributed antigen, and their utility for differentiating between primary and secondary MN, and between recurrent MN after kidney transplant and de novo MN. We end with consideration of how knowledge of the antigen might direct future therapeutic strategies.

In this study of patients with membranous nephropathy, serum samples from 70% of patients with idiopathic, but not secondary, membranous nephropathy were found to have antibodies against a 185-kD glycoprotein in nonreduced glomerular extracts, identified as the M-type phospholipase A 2 receptor (PLA 2 R). PLA 2 R is present in normal podocytes and in immune deposits in patients with idiopathic membranous nephropathy, indicating that PLA 2 R is a major antigen in this disease. Phospholipase A 2 receptor (PLA 2 R) is present in normal podocytes and in immune deposits in patients with idiopathic membranous nephropathy, indicating that it is a major antigen in this disease. Idiopathic membranous nephropathy, a common cause of the nephrotic syndrome in adults, is an organ-specific autoimmune disease. Despite extensive investigation, a target antigen has been elusive. Studies of membranous nephropathy in a rat model (Heymann's nephritis) established that the subepithelial immune deposits that characterize the disease are formed in situ, as a result of capping and shedding of the target antigen, megalin, from the basal surface of podocytes when it forms a complex with circulating antimegalin antibodies. 1 – 8 Although megalin is not expressed on human podocytes, we hypothesized that a similar process, albeit with an unknown antigen, is operative in . . .

Idiopathic membranous nephropathy is associated with autoantibodies against the phospholipase A2 receptor (PLA2R1) in about 70% of patients. This study identifies another antigen, thrombospondin type-1 domain-containing 7A (THSD7A), which accounts for about 10% of cases. Idiopathic membranous nephropathy is an autoimmune disease and a common cause of the nephrotic syndrome in adults. 1 In 2009, the phospholipase A2 receptor 1 (PLA2R1), a protein that is expressed in glomerular podocytes, was discovered as the major antigen involved in the pathogenesis of adult idiopathic membranous nephropathy. 2 As confirmed by a number of subsequent studies, about 70% of patients with idiopathic membranous nephropathy have circulating autoantibodies against PLA2R1. 2 – 6 The remaining patients, approximately 30% of those with idiopathic membranous nephropathy, have no obvious secondary cause of the disease, and it is thought that other endogenous glomerular antigens may be . . .

Primary membranous nephropathy (pMN) is a leading cause of nephrotic syndrome in adults. In most cases, this autoimmune kidney disease is associated with autoantibodies against the M-type phospholipase A2 receptor (PLA2R1) expressed on kidney podocytes, but the mechanisms leading to glomerular damage remain elusive. Here, we developed a cell culture model using human podocytes and found that anti-PLA2R1-positive pMN patient sera or isolated IgG4, but not IgG4-depleted sera, induced proteolysis of the 2 essential podocyte proteins synaptopodin and NEPH1 in the presence of complement, resulting in perturbations of the podocyte cytoskeleton. Specific blockade of the lectin pathway prevented degradation of synaptopodin and NEPH1. Anti-PLA2R1 IgG4 directly bound mannose-binding lectin in a glycosylation-dependent manner. In a cohort of pMN patients, we identified increased levels of galactose-deficient IgG4, which correlated with anti-PLA2R1 titers and podocyte damage induced by patient sera. Assembly of the terminal C5b-9 complement complex and activation of the complement receptors C3aR1 or C5aR1 were required to induce proteolysis of synaptopodin and NEPH1 by 2 distinct proteolytic pathways mediated by cysteine and aspartic proteinases, respectively. Together, these results demonstrated a mechanism by which aberrantly glycosylated IgG4 activated the lectin pathway and induced podocyte injury in primary membranous nephropathy.

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601–0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to ‘black-box’ methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment. Computational methods have made progress in improving classification accuracy and throughput of pathology workflows, but lack of interpretability remains a barrier to clinical integration. Here, the authors present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features.

Membranous nephropathy (MN) is an immune complex-mediated cause of the nephrotic syndrome that can occur in all age groups, from infants to the very elderly. However, nephrotic syndrome in children is more frequently caused by conditions such as minimal change disease or focal segmental glomerulosclerosis, and much less commonly by MN. While systemic conditions such as lupus or infections such as hepatitis B may more commonly be associated as secondary causes with MN in the younger population, primary or “idiopathic” MN has generally been considered a disease of adults. Autoantibodies both to the M-type phospholipase A2 receptor (PLA2R) and to thrombospondin type-1 domain-containing 7A (THSD7A), initially described in adult MN, have now been identified in children and adolescents with MN and serve as a useful diagnostic and monitoring tool in this younger population as well. Whereas definitive therapy for secondary forms of MN should be targeted at the underlying cause, immunosuppressive therapy is often necessary for primary disease. Rituximab has been successfully used in the treatment of MN, and is likely effective in children with MN as well, although dosing in the pediatric population is not well established. This review highlights the new findings in adult and pediatric MN since last reviewed in this journal.

Membranous nephropathy (MN) is an immune complex-mediated cause of the nephrotic syndrome that can occur in all age groups, from infants to the elderly. While systemic disorders such as hepatitis B infection or lupus may more frequently cause secondary MN in the younger population, primary or “idiopathic” MN has generally been considered a disease of adults. Recent progress in our understanding of primary disease was recently made when the target antigen in primary MN was identified as the M-type phospholipase A 2 receptor (PLA 2 R). Circulating anti-PLA 2 R antibodies may serve both as a diagnostic tool for distinguishing primary from secondary disease and as a biomarker for monitoring the immunologic activity of this organ-specific autoimmune disease during treatment. Whereas definitive therapy for secondary forms of MN should be targeted at the underlying cause, immunosuppressive therapy is often necessary for primary disease. Alkylating agents in combination with corticosteroids, as well as calcineurin inhibitors (± steroids), are first line agents due to randomized controlled trials in an adult population with relatively long durations of follow-up. However, rituximab, mycophenolate and adrenocorticotropic hormone have shown promise in smaller and/or observational studies. The optimal therapy for children and adolescents with MN is less well defined.

The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.

The breast stromal microenvironment is a pivotal factor in breast cancer development, growth and metastases. Although pathologists often detect morphologic changes in stroma by light microscopy, visual classification of such changes is subjective and non-quantitative, limiting its diagnostic utility. To gain insights into stromal changes associated with breast cancer, we applied automated machine learning techniques to digital images of 2387 hematoxylin and eosin stained tissue sections of benign and malignant image-guided breast biopsies performed to investigate mammographic abnormalities among 882 patients, ages 40–65 years, that were enrolled in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project. Using deep convolutional neural networks, we trained an algorithm to discriminate between stroma surrounding invasive cancer and stroma from benign biopsies. In test sets (928 whole-slide images from 330 patients), this algorithm could distinguish biopsies diagnosed as invasive cancer from benign biopsies solely based on the stromal characteristics (area under the receiver operator characteristics curve = 0.962). Furthermore, without being trained specifically using ductal carcinoma in situ as an outcome, the algorithm detected tumor-associated stroma in greater amounts and at larger distances from grade 3 versus grade 1 ductal carcinoma in situ. Collectively, these results suggest that algorithms based on deep convolutional neural networks that evaluate only stroma may prove useful to classify breast biopsies and aid in understanding and evaluating the biology of breast lesions.