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Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment.

Kidney complications can occur due to infective endocarditis, one of which is glomerulonephritis. Most often, an immune complex or complement-mediated glomerulonephritis is seen on kidney biopsy. In a minor subset of cases, pauci-immune glomerulonephritis may be present. Most often, such patients will demonstrate the presence of antineutrophil cytoplasmic antibodies (ANCA) on serologic testing. A growing number of cases of ANCA-associated glomerulonephritis due to Bartonella endocarditis have been reported. This type of endocarditis can present diagnostic difficulties given that these patients are often culture negative. Herein, we report a challenging case of ANCA-negative pauci-immune glomerulonephritis showing florid crescents on biopsy that was associated with Bartonella endocarditis.

BACKGROUND: Pathology reports contain key information about the patients diagnosis as well as important gross and microscopic findings. These information-rich clinical reports offer an invaluable resource for clinical studies, but data extraction and analysis from such unstructured texts is often manual and tedious. While neural information retrieval systems (typically implemented as deep learning methods for natural language processing) are automatic and flexible, they typically require a large domain-specific text corpus for training, making them infeasible for many medical subdomains. Thus, an automated data extraction method for pathology reports that does not require a large training corpus would be of significant value and utility. OBJECTIVE: To develop a language model-based neural information retrieval system that can be trained on small datasets and validate it by training it on renal transplant-pathology reports to extract relevant information for two predefined questions: (1) What kind of rejection does the patient show?; (2) What is the grade of interstitial fibrosis and tubular atrophy (IFTA)? METHODS: Kidney BERT was developed by pre-training Clinical BERT on 3.4K renal transplant pathology reports and 1.5M words. Then, exKidneyBERT was developed by extending Clinical BERTs tokenizer with six technical keywords and repeating the pre-training procedure. This extended the models vocabulary. All three models were fine-tuned with information retrieval heads. RESULTS: The model with extended vocabulary, exKidneyBERT, outperformed Clinical BERT and Kidney BERT in both questions. For rejection, exKidneyBERT achieved an 83.3% overlap ratio for antibody-mediated rejection (ABMR) and 79.2% for T-cell mediated rejection (TCMR). For IFTA, exKidneyBERT had a 95.8% exact match rate. CONCLUSION: ExKidneyBERT is a high-performing model for extracting information from renal pathology reports. Additional pre-training of BERT language models on specialized small domains does not necessarily improve performance. Extending the BERT tokenizers vocabulary library is essential for specialized domains to improve performance, especially when pre-training on small corpora.

KEY POINTS: The authors leverage the unique benefits of panoptic segmentation to perform the largest ever quantitation of reference kidney morphometry. Kidney features vary with age and sex; and glomeruli size may intricately link to creatinine, defying prior notions. BACKGROUND: Reference histomorphometric data of healthy human kidneys are largely lacking because of laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning (DL), computational image analysis, and feature analysis to associate the relationship of histomorphometry with patient age, sex, serum creatinine (SCr), and eGFR in a multinational set of reference kidney tissue sections. METHODS: A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid–Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the association of histomorphometric parameters with age, sex, SCr, and eGFR. RESULTS: Our DL model achieved high segmentation performance for all test compartments. The size and density of glomeruli, tubules, and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Glomerular size was significantly correlated with SCr and eGFR. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of increasing age. CONCLUSIONS: Using DL, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics, SCr, and eGFR. DL tools can increase the efficiency and rigor of histomorphometric analysis.

Reference histomorphometric data of healthy human kidneys are lacking due to laborious quantitation requirements. We leveraged deep learning to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine in a multinational set of reference kidney tissue sections. A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in digitized images of 79 periodic acid-Schiff (PAS)-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were measured from the segmented classes. Regression analysis was used to determine the relationship of histomorphometric parameters with age, sex, and serum creatinine. The model achieved high segmentation performance for all test compartments. We found that the size and density of nephrons, arteries/arterioles, and the baseline level of interstitium vary significantly among healthy humans, with potentially large differences between subjects from different geographic locations. Nephron size in any region of the kidney was significantly dependent on patient creatinine. Slight differences in renal vasculature and interstitium were observed between sexes. Finally, glomerulosclerosis percentage increased and cortical density of arteries/arterioles decreased as a function of age. We show that precise measurements of kidney histomorphometric parameters can be automated. Even in reference kidney tissue sections with minimal pathologic changes, several histomorphometric parameters demonstrated significant correlation to patient demographics and serum creatinine. These robust tools support the feasibility of deep learning to increase efficiency and rigor in histomorphometric analysis and pave the way for future large-scale studies.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN-based models, but this is hindered by the logistical challenges of sharing medical data. In this paper, we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. Using a dataset of renal tissue biopsies we show that federated training to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is not found to be different from a training by pooling the data on one server when tested on a fourth (holdout) institution's data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.

BackgroundImage-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces.MethodsWe have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis.ResultsBy segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models.ConclusionsHisto-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan-Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.

Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson's Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost.

Classic antiglomerular basement membrane (anti-GBM) disease is an exceedingly rare but extremely aggressive form of glomerulonephritis, typically caused by autoantibodies directed against cryptic, conformational epitopes within the noncollagenous domain of the type IV collagen alpha-3 subunit. Pathologic diagnosis is established by the presence of strong, diffuse, linear staining for immunoglobulin on immunofluorescence microscopy. Recently, patients with atypical clinical and pathologic findings of anti-GBM disease have been described. These patients tend to have an indolent clinical course, without pulmonary involvement, and laboratory testing rarely reveals the presence of anti-GBM antibodies. Specific guidelines for the treatment and management of these patients are unclear. Here, we describe a case of atypical anti-GBM disease in a young child who presented with hematuria and prominent proteinuria. Throughout the course of his illness, creatinine remained normal. He was conservatively treated with steroids and rituximab, resulting in resolution of his clinical symptoms and normalization of laboratory findings.

Coronavirus disease 2019 (COVID-19) is associated with high morbidity and mortality worldwide in both the general population and kidney transplant recipients. Acute kidney injury is a known complication of COVID-19 and appears to most commonly manifest as acute tubular injury on renal biopsy. Coagulopathy associated with COVID-19 is a known but poorly understood complication that has been reported to cause thrombotic microangiopathy on rare occasions in native kidneys of patients with COVID-19. Here, we report the first case of biopsy-proven thrombotic microangiopathy in a kidney transplant recipient with COVID-19 who developed acute pancreatitis and clinical features of microangiopathic hemolytic anemia. The patient recovered with supportive care alone.

The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67-negative tumor cells within a subjectively picked hot-spot. Intraobserver variability in this procedure as well as difficulty in distinguishing tumor from non-tumor cells can lead to inaccurate Ki-67 indices and possibly incorrect tumor grades. We introduce two computational tools that utilize Ki-67 and synaptophysin double-immunostained (DS) slides to improve the accuracy of Ki-67 index quantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-slide image (WSI) analysis and (2) deep-SKIE, a deep learner-based approach where a Ki-67 index heatmap is generated throughout the tumor. Ki-67 indices for 50 GI-NETs were quantitated using SKIE and compared with DS slide assessments by three pathologists using a microscope and a fourth pathologist via manually ticking off each cell, the latter of which was deemed the gold standard (GS). Compared to the GS, SKIE achieved a grading accuracy of 90% and substantial agreement (linear-weighted Cohen's kappa 0.62). Using DS WSIs, deep-SKIE displayed a training, validation, and testing accuracy of 98.4%, 90.9%, and 91.0%, respectively, significantly higher than using SS WSIs. Since DS slides are not standard clinical practice, we also integrated a cycle generative adversarial network into our pipeline to transform SS into DS WSIs. The proposed methods can improve accuracy and potentially save a significant amount of time if implemented into clinical practice.

Nivolumab is an immune checkpoint inhibitor that targets programmed death-1 on T cells and is designed to amplify an immunologic reaction against cancer cells. However, upregulation of the immune system with checkpoint inhibition is nonspecific, and it can be associated with certain renal side effects, the best documented of which is acute tubulointerstitial nephritis. We present a unique case of a patient with acute kidney injury associated with nephrotic syndrome shortly after starting nivolumab therapy for metastatic anal carcinoma. Subsequent renal biopsy revealed membranoproliferative glomerulonephritis (MPGN). We believe this represents the first reported direct case of nivolumab-associated MPGN. As immunotherapy becomes more widely used in cancer treatment, particular attention must be paid to possible consequences of immune checkpoint inhibitors.

Generative modeling using GANs has gained traction in machine learning literature, as training does not require labeled datasets. This is perfect for applications in biological datasets, where large labeled datasets are often difficult and expensive to acquire. However, generative models offer no easy way to encode real images into feature-sets, something that is desirable for network explainability and may yield potentially informative image features. For this reason, we test a VAE-GAN architecture for label-free modeling of glomerular structural features. We show that this network can generate realistic looking synthetic images, and be used to interpolate between images. To prove the biological relevance of the network encodings, we classify small-labeled sets of encoded glomeruli by biopsy Tervaert class and for the presence of sclerosis, obtaining a Cohen's kappa values of 0.87 and 0.78 respectfully.

Diabetic Nephropathy (DN) progression is stratified into several stages with different levels of proteinuria, albuminuria, and physical characteristics as observed by pathologists. These physical changes are primarily visible within a patient's glomeruli which function as filtration units for blood returning for oxygenation. As DN stage increases, it is possible to observe the thickening of the glomerular basement membrane, expansion of the mesangium, and development of nodular sclerosis. Classification of different stages of DN by pathologists is based on semi-qualitative assessments of these characteristics on an individual glomerulus basis. Being able to probabilistically infer stage membership of individual glomeruli based on a combination of easily observable and hidden image features would be an invaluable tool for furthering our understanding of the drivers of DN progression. Markov Particle filters, included in the bnlearn package in R, were used to query a Bayesian Network (BN) constructed using the structural Hill-Climbing algorithm on a set of glomerular features. These features included both traditional characteristics such as glomerular area and number of mesangial nuclei as well as more abstract features derived from Minimum Spanning Trees (MST) to quantify spatial distribution of mesangial nuclei. Our results using images from multiple institutions suggest that these abstract features exercise a variable influence on DN stage membership over the course of disease progression. Further research incorporating clinical data will give nephrologists a "white box" visual of quantitative factors present in DN patients.

Ionising radiation (IR) is a recognised carcinogen responsible for cancer development in patients previously treated using radiotherapy, and in individuals exposed as a result of accidents at nuclear energy plants. However, the mutational signatures induced by distinct types and doses of radiation are unknown. Here, we analyse the genetic architecture of mammary tumours, lymphomas and sarcomas induced by high (56Fe-ions) or low (gamma) energy radiation in mice carrying Trp53 loss of function alleles. In mammary tumours, high-energy radiation is associated with induction of focal structural variants, leading to genomic instability and Met amplification. Gamma-radiation is linked to large-scale structural variants and a point mutation signature associated with oxidative stress. The genomic architecture of carcinomas, sarcomas and lymphomas arising in the same animals are significantly different. Our study illustrates the complex interactions between radiation quality, germline Trp53 deficiency and tissue/cell of origin in shaping the genomic landscape of IR-induced tumours.

In the clinical practice of pathology, trichrome stains are commonly used to highlight collagen and to help evaluate fibrosis. Such stains do delineate collagen deposits but are not molecularly specific and can suffer from staining inconsistencies. Moreover, performing histochemical stain evaluation requires the preparation of additional sections beyond the original hematoxylin- and eosin-stained slides, as well as additional staining steps, which together add cost, time, and workflow complications. We have developed a new microscopy approach, termed DUET (DUal-mode Emission and Transmission) that can be used to extract signals that would typically require special stains or advanced optical methods. Our preliminary analysis demonstrates the potential of using the resulting signals to generate virtual histochemical images that resemble trichrome-stained slides and can support clinical evaluation. We demonstrate advantages of this approach over images acquired from conventional trichrome-stained slides and compare them with images created using second harmonic generation microscopy.

In the clinical practice of pathology, trichrome stains are commonly used to highlight collagen and to help evaluate fibrosis. Such stains do delineate collagen deposits but are not molecularly specific and can suffer from staining inconsistencies. Moreover, performing histochemical stain evaluation requires the preparation of additional sections beyond the original hematoxylin- and eosin-stained slides, as well as additional staining steps, which together add cost, time, and workflow complications. We have developed a new microscopy approach, termed DUET (DUal-mode Emission and Transmission) that can be used to extract signals that would typically require special stains or advanced optical methods. Our preliminary analysis demonstrates the potential of using the resulting signals to generate virtual histochemical images that resemble trichrome-stained slides and can support clinical evaluation. We demonstrate advantages of this approach over images acquired from conventional trichrome-stained slides and compare them with images created using second harmonic generation microscopy.

ObjectiveThe Collaborative Cross (CC) is a mouse population model with diverse and reproducible genetic backgrounds used to identify novel disease models and genes that contribute to human disease. Since spontaneous tumour susceptibility in CC mice remains unexplored, we assessed tumour incidence and spectrum.DesignWe monitored 293 mice from 18 CC strains for tumour development. Genetic association analysis and RNA sequencing were used to identify susceptibility loci and candidate genes. We analysed genomes of patients with gastric cancer to evaluate the relevance of genes identified in the CC mouse model and measured the expression levels of ISG15 by immunohistochemical staining using a gastric adenocarcinoma tissue microarray. Association of gene expression with overall survival (OS) was assessed by Kaplan-Meier analysis.ResultsCC mice displayed a wide range in the incidence and types of spontaneous tumours. More than 40% of CC036 mice developed gastric tumours within 1 year. Genetic association analysis identified Nfκb1 as a candidate susceptibility gene, while RNA sequencing analysis of non-tumour gastric tissues from CC036 mice showed significantly higher expression of inflammatory response genes. In human gastric cancers, the majority of human orthologues of the 166 mouse genes were preferentially altered by amplification or deletion and were significantly associated with OS. Higher expression of the CC036 inflammatory response gene signature is associated with poor OS. Finally, ISG15 protein is elevated in gastric adenocarcinomas and correlated with shortened patient OS.ConclusionsCC strains exhibit tremendous variation in tumour susceptibility, and we present CC036 as a spontaneous laboratory mouse model for studying human gastric tumourigenesis.

Variations in oral bacterial communities have been linked to oral cancer suggesting that the oral microbiome is an etiological factor that can influence oral cancer development. The 4-nitroquinoline 1-oxide (4-NQO)-induced murine oral and esophageal cancer model is frequently used to assess the effects of preventive and/or therapeutic agents. We used this model to assess the impact of the microbiome on tumorigenesis using axenic (germ-free) and conventionally housed mice. Increased toxicity was observed in germ-free mice, however, no difference in tumor incidence, multiplicity, and size was observed. Transcriptional profiling of liver tissue from germ-free and conventionally housed mice identified 254 differentially expressed genes including ten cytochrome p450 enzymes, the largest family of phase-1 drug metabolizing enzymes in the liver. Gene ontology revealed that differentially expressed genes were enriched for liver steatosis, inflammation, and oxidative stress in livers of germ-free mice. Our observations emphasize the importance of the microbiome in mediating chemical toxicity at least in part by altering host gene expression. Studies on the role of the microbiome in chemical-induced cancer using germ-free animal models should consider the potential difference in dose due to the microbiome-mediated changes in host metabolizing capacity, which might influence the ability to draw conclusions especially for tumor induction models that are dose dependent.

In diabetic nephropathy (DN), hyperglycemia drives a progressive thickening of glomerular filtration surfaces, increased cell proliferation as well as mesangial expansion and a constriction of capillary lumens. This leads to progressive structural changes inside the Glomeruli. In this work, we make a study of structural glomerular changes in DN from a graph-theoretic standpoint, using features extracted from Minimal Spanning Trees (MSTs) constructed over intercellular distances in order to classify the "packing signatures" of different DN stages. We further investigate the significance of the competing effects of Volume change measured here in 2Dimensional Pixel span area (Area) on one hand and increased cell proliferation on the other in determining the packing patterns. Towards that we formulate the problem as Dynamic Bayesian Network (DBN). From our preliminary results we do postulate that volume expansion caused by internal pressure as capillary lumens constriction has perhaps has a greater effect in the early stages.

Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.

BackgroundDespite careful clinical examination, procurement biopsy and assessment on hypothermic machine perfusion, a significant number of potentially useable deceased donor kidneys will be discarded because they are deemed unsuitable for transplantation. Ex vivo normothermic perfusion (EVNP) may be useful as a means to further assess high-risk kidneys to determine suitability for transplantation.MethodsFrom June 2014 to October 2015, 7 kidneys (mean donor age, 54.3 years and Kidney Donor Profile Index, 79%) that were initially procured with the intention to transplant were discarded based on a combination of clinical findings, suboptimal biopsies, long cold ischemia time (CIT) and/or poor hypothermic perfusion parameters. They were subsequently placed on EVNP using oxygenated packed red blood cells and supplemental nutrition for a period of 3 hours. Continuous hemodynamic and functional parameters were assessed.ResultsAfter a mean CIT of 43.7 hours, all 7 kidneys appeared viable on EVNP with progressively increasing renal blood flow over the 3-hour period of perfusion. Five of the 7 kidneys had excellent macroscopic appearance, rapid increase in blood flow to 200 to 250 mL/min, urine output of 40 to 260 mL/h and increasing creatinine clearance.ConclusionsFavorable perfusion characteristics and immediate function after a 3-hour course of EVNP suggests that high-risk kidneys subjected to long CIT may have been considered for transplantation. The combined use of ex vivo hypothermic and normothermic perfusion may be a useful strategy to more adequately assess and preserve high-risk kidneys deemed unsuitable for transplantation. A clinical trial will be necessary to validate the usefulness of this approach.

Research into metabolic reprogramming in cancer has become commonplace, yet this area of research has only recently come of age in nephrology. In light of the parallels between cancer and autosomal dominant polycystic kidney disease (ADPKD), the latter is currently being studied as a metabolic disease. In clear cell renal cell carcinoma (RCC), which is now considered a metabolic disease, we and others have shown derangements in the enzyme arginosuccinate synthase 1 (ASS1), resulting in RCC cells becoming auxotrophic for arginine and leading to a new therapeutic paradigm involving reducing extracellular arginine. Based on our earlier finding that glutamine pathways are reprogrammed in ARPKD, and given the connection between arginine and glutamine synthetic pathways via citrulline, we investigated the possibility of arginine reprogramming in ADPKD. We now show that, in a remarkable parallel to RCC, ASS1 expression is reduced in murine and human ADPKD, and arginine depletion results in a dose-dependent compensatory increase in ASS1 levels as well as decreased cystogenesis in vitro and ex vivo with minimal toxicity to normal cells. Nontargeted metabolomics analysis of mouse kidney cell lines grown in arginine-deficient versus arginine-replete media suggests arginine-dependent alterations in the glutamine and proline pathways. Thus, depletion of this conditionally essential amino acid by dietary or pharmacological means, such as with arginine-degrading enzymes, may be a novel treatment for this disease.

Exposure to thirdhand smoke (THS) is a recently described health concern that arises in many indoor environments. However, the carcinogenic potential of THS, a critical consideration in risk assessment, remains untested. Here we investigated the effects of short-term early exposure to THS on lung carcinogenesis in A/J mice. Forty weeks after THS exposure from 4 to 7 weeks of age, the mice had increased incidence of lung adenocarcinoma, tumor size and, multiplicity, compared with controls. In vitro studies using cultured human lung cancer cells showed that THS exposure induced DNA double-strand breaks and increased cell proliferation and colony formation. RNA sequencing analysis revealed that THS exposure induced endoplasmic reticulum stress and activated p53 signaling. Activation of the p53 pathway was confirmed by an increase in its targets p21 and BAX. These data indicate that early exposure to THS is associated with increased lung cancer risk.

Targeting somatically activated oncogenes has revolutionized the treatment of non-small cell lung cancer (NSCLC). Mutations in the gene mesenchymal-epithelial transition (MET) near the exon 14 splice sites are recurrent in lung adenocarcinoma and cause exon skipping (METΔ14). Here, we analyzed 4,422 samples from 12 different malignancies to estimate the rate of said exon skipping. METΔ14 mutation and transcript were most common in lung adenocarcinoma. Endogenously expressed levels of METΔ14 transformed human epithelial lung cells in a hepatocyte growth factor-dependent manner. In addition, overexpression of the orthologous mouse allele induced lung adenocarcinoma in a novel, immunocompetent mouse model. Met inhibition showed clinical benefit in this model. In addition, we observed a clinical response to crizotinib in a patient with METΔ14-driven NSCLC, only to observe new missense mutations in the MET activation loop, critical for binding to crizotinib, upon clinical progression. These findings support genomically selected clinical trials directed toward METΔ14 in a fraction of NSCLC patients, confirm second-site mutations for further therapeutic targeting prior to and beyond acquired resistance, and provide an in vivo system for the study of METΔ14 in an immunocompetent host. Cancer Res; 77(16); 4498-505. ©2017 AACR.

Hepatic steatosis develops after liver transplantation (LT) in 30% of adults, and nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in nontransplanted children. However, posttransplant steatosis has been minimally studied in pediatric LT recipients. We explored the prevalence, persistence, and association with chronic liver damage of hepatic steatosis in these children. In this single-center study of pediatric patients transplanted 1988-2015 (n = 318), 31% of those with any posttransplant biopsy (n = 271) had ≥ 1 biopsy with steatosis. Median time from transplant to first biopsy with steatosis was 0.8 months (interquartile range [IQR], 0.3-6.5 months) and to last biopsy with steatosis was 5.5 months (IQR, 1.0-24.5 months); 85% of patients with steatosis also had for-cause biopsies without steatosis. All available for-cause biopsies were re-evaluated (n = 104). Of 9 biopsies that could be interpreted as nonalcoholic steatohepatitis (NASH)/borderline NASH, with steatosis plus inflammation or ballooning, 8 also had features of cholestasis or rejection. Among 70 patients with surveillance biopsies 3.6-20.0 years after transplant, only 1 overweight adolescent had a biopsy with NAFLD (grade 1 steatosis, mild inflammation, no ballooning or fibrosis)-despite a 30% prevalence of overweight/obesity in the cohort and 27% with steatosis on previous for-cause biopsy. Steatosis on preceding for-cause biopsy was not associated with portal (P = 0.49) or perivenular fibrosis (P = 0.85) on surveillance biopsy. Hepatic steatosis commonly develops early after transplant in children and adolescents, but it rarely persists. Biopsies that did have steatosis with NASH characteristics were all for-cause, mostly in patients with NAFLD risk factors and/or confounding causes of liver damage. Prospective studies that follow children into adulthood will be needed to evaluate if and when hepatic steatosis presents a longterm risk for pediatric LT recipients. Liver Transplantation 23 957-967 2017 AASLD.

NUCKS1 is a 27 kD vertebrate-specific protein, with a role in the DNA damage response. Here, we show that after 4 Gy total-body X-irradiation, Trp53+/- Nucks1+/- mice more rapidly developed tumors, particularly thymic lymphoma (TL), than Trp53+/- mice. TLs in both cohorts showed loss of heterozygosity (LOH) of the Trp53+ allele in essentially all cases. In contrast, LOH of the Nucks1+ allele was rare. Nucks1 expression correlated well with Nucks1 gene dosage in normal thymi, but was increased in the majority of TLs from Trp53+/- Nucks1+/- mice, suggesting that elevated Nucks1 message may be associated with progression towards malignancy in vivo. Trp53+/- Nucks1+/- mice frequently succumbed to CD4- CD8- TLs harboring translocations involving Igh but not Tcra/d, indicating TLs in Trp53+/- Nucks1+/- mice mostly originated prior to the double positive stage and at earlier lineage than TLs in Trp53+/- mice. Monoclonal rearrangements at Tcrb were more prevalent in TLs from Trp53+/- Nucks1+/- mice, as was infiltration of primary TL cells to distant organs (liver, kidney and spleen). We propose that, in the context of Trp53 deficiency, wild type levels of Nucks1 are required to suppress radiation-induced TL, likely through the role of the NUCKS1 protein in the DNA damage response.

The tumor suppressor TP53 can initiate a plethora of anti-proliferative effects to maintain genomic integrity under conditions of genotoxic stress. The N-terminal proline-rich domain (PRD) of TP53 is important in the regulation of TP53 activity and stability. A common polymorphism at codon 72 in this region has been associated with altered cancer risk in humans. The Trp53ΔP mouse, which carries a germline homozygous deletion of a region of the PRD, does not develop spontaneous tumors in a mixed 129/Sv and C57BL/6 genetic background, but is highly susceptible to a broad range of tumor types following total body exposure to 4 Gy gamma (γ) radiation. This contrasts with the tumor spectrum in Trp53 null (-/-) mice, which mainly develop thymic lymphomas and osteosarcomas. Analysis of genomic instability in tissues and cells from Trp53ΔP mice demonstrated elevated basal levels of aneuploidy, but this is not sufficient to drive spontaneous tumorigenesis, which requires an additional DNA damage stimulus. Levels of genomic instability did not increase significantly in Trp53ΔP mice following irradiation exposure, suggesting that other radiation effects including tissue inflammation, altered metabolism or autophagy, may play an important role. The Trp53ΔP mouse is a novel model to dissect the mechanisms of tumor development induced by radiation exposure. © 2015 Wiley Periodicals, Inc.

BackgroundAA amyloidosis due to subcutaneous injection of drugs of abuse has been described in the USA, but all the existing literature is from more than 20 years ago. There is more recent literature from Europe. We have observed a high incidence of AA amyloidosis in the county hospital in San Francisco.DesignHere, we describe 24 patients who had kidney biopsy-proven AA amyloidosis from our hospital from 1998 to 2013. All the patients were thought to have AA amyloidosis from skin popping of illicit drugs after having exhausted the intravenous route. These patients with biopsy-proven AA amyloidosis were analysed further.ResultsAll patients were found to have hepatitis C infection, hypertension was not common, most had advanced kidney failure, and acidosis was common as was tubulointerstitial involvement on the kidney biopsy. Other organ involvement included hepatomegaly and splenomegaly in a number of patients; direct myocardial involvement was not seen, but pulmonary hypertension, history of deep vein thrombosis and pulmonary embolism were common. The prognosis of these patients was poor. The mortality rate approached 50% 1 year after biopsy, and most of the patient needed dialysis shortly after diagnosis. Cessation of drug use seemed beneficial but rarely achievable.ConclusionAA amyloidosis from skin popping is common in San Francisco. Most patients with renal involvement end up on dialysis, and mortality rates are exceedingly high.

Histopathology interpretation remains an essential, foundational method for disease diagnosis in medical practice. (1) Standard histopathology assessment is initiated through interpretation of hematoxylin and eosin--stained glass microscope slides prepared from [...], * Context.--The adoption of digital capture of pathology slides as whole slide images (WSI) for educational and research applications has proven utility. Objective.--To compare pathologists' primary diagnoses derived from WSI versus the standard microscope. Because WSIs differ in format and method of observation compared with the current standard glass slide microscopy, this study is critical to potential clinical adoption of digital pathology. Design.--The study enrolled a total of 2045 cases enriched for more difficult diagnostic categories and represented as 5849 slides were curated and provided for diagnosis by a team of 19 reading pathologists separately as WSI or as glass slides viewed by light microscope. Cases were reviewed by each pathologist in both modalities in randomized order with a minimum 31-day washout between modality reads for each case. Each diagnosis was compared with the original clinical reference diagnosis by an independent central adjudication review. Results.--The overall major discrepancy rates were 3.64% for WSI review and 3.20% for manual slide review diagnosis methods, a difference of 0.44% (95% CI, -0.15 to 1.03). The time to review a case averaged 5.20 minutes for WSI and 4.95 minutes for glass slides. There was no specific subset of diagnostic category that showed higher rates of modality-specific discrepancy, though some categories showed greater discrepancy than others in both modalities. Conclusions.--WSIs are noninferior to traditional glass slides for primary diagnosis in anatomic pathology.

Malignant pleural mesothelioma (MPM), a rare malignancy with a poor prognosis, is mainly caused by exposure to asbestos or other organic fibers, but the underlying genetic mechanism is not fully understood. Genetic alterations and causes for multiple primary cancer development including MPM are unknown. We used whole exome sequencing to identify somatic mutations in a patient with MPM and two additional primary cancers who had no evidence of venous, arterial, lymphovascular, or perineural invasion indicating dissemination of a primary lung cancer to the pleura. We found that the MPM had R282W, a key TP53 mutation, and genome-wide allelic loss or loss of heterozygosity, a distinct genomic alteration not previously described in MPM. We identified frequent inactivating SETDB1 mutations in this patient and in 68 additional MPM patients (mutation frequency: 10%, 7/69) by targeted deep sequencing. Our observations suggest the possibility of a new genetic mechanism in the development of either MPM or multiple primary cancers. The frequent SETDB1 inactivating mutations suggest there could be new diagnostic or therapeutic options for MPM.

Human tumors show a high level of genetic heterogeneity, but the processes that influence the timing and route of metastatic dissemination of the subclones are unknown. Here we have used whole-exome sequencing of 103 matched benign, malignant and metastatic skin tumors from genetically heterogeneous mice to demonstrate that most metastases disseminate synchronously from the primary tumor, supporting parallel rather than linear evolution as the predominant model of metastasis. Shared mutations between primary carcinomas and their matched metastases have the distinct A-to-T signature of the initiating carcinogen dimethylbenzanthracene, but non-shared mutations are primarily G-to-T, a signature associated with oxidative stress. The existence of carcinomas that either did or did not metastasize in the same host animal suggests that there are tumor-intrinsic factors that influence metastatic seeding. We also demonstrate the importance of germline polymorphisms in determining allele-specific mutations, and we identify somatic genetic alterations that are specifically related to initiation of carcinogenesis by Hras or Kras mutations. Mouse tumors that mimic the genetic heterogeneity of human cancers can aid our understanding of the clonal evolution of metastasis and provide a realistic model for the testing of novel therapies.

Rheumatic manifestations of cocaine have been well described, but more recently, a dramatic increase in the levamisole-adulterated cocaine supply in the United States has disclosed unique pathologic consequences that are distinct from pure cocaine use. Most notably, patients show skin lesions and renal dysfunction in the setting of extremely high perinuclear anti-neutrophil cytoplasmic antibodies (p-ANCA). Unexpectedly, antibodies to myeloperoxidase, the typical target of p-ANCA, are relatively low if at all present. This discrepancy is due to the fact that p-ANCA seen in association with levamisole-adulterated cocaine exposure is often directed against atypical p-ANCA-associated antigens within the neutrophil granules such as human neutrophil elastase, lactoferrin, and cathepsin G. Biopsies of the skin lesions reveal leukocytoclastic vasculitis often involving both superficial and deep dermal vessels. Renal injury most typically manifests as crescentic and necrotizing pauci-immune glomerulonephritis. In this review, the manifestations of levamisole-adulterated cocaine-induced vasculitis are discussed with an emphasis on the typical histomorphologic findings seen on biopsy.Virtual slidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1764738711370019 .

Oxidative stress has been proposed to be a unifying cause for diabetic nephropathy and a target for novel therapies. Here we apply a new endogenous reduction-oxidation (redox) sensor, hyperpolarized (HP) (13)C dehydroascorbate (DHA), in conjunction with MRI to noninvasively interrogate the renal redox capacity in a mouse diabetes model. The diabetic mice demonstrate an early decrease in renal redox capacity, as shown by the lower in vivo HP (13)C DHA reduction to the antioxidant vitamin C (VitC), prior to histological evidence of nephropathy. This correlates with lower tissue reduced glutathione (GSH) concentration and higher NADPH oxidase 4 (Nox4) expression, consistent with increased superoxide generation and oxidative stress. ACE inhibition restores the HP (13)C DHA reduction to VitC with concomitant normalization of GSH concentration and Nox4 expression in diabetic mice. HP (13)C DHA enables rapid in vivo assessment of altered redox capacity in diabetic renal injury and after successful treatment.

Next-generation sequencing of human tumours has refined our understanding of the mutational processes operative in cancer initiation and progression, yet major questions remain regarding the factors that induce driver mutations and the processes that shape mutation selection during tumorigenesis. Here we performed whole-exome sequencing on adenomas from three mouse models of non-small-cell lung cancer, which were induced either by exposure to carcinogens (methyl-nitrosourea (MNU) and urethane) or by genetic activation of Kras (Kras(LA2)). Although the MNU-induced tumours carried exactly the same initiating mutation in Kras as seen in the Kras(LA2) model (G12D), MNU tumours had an average of 192 non-synonymous, somatic single-nucleotide variants, compared with only six in tumours from the Kras(LA2) model. By contrast, the Kras(LA2) tumours exhibited a significantly higher level of aneuploidy and copy number alterations compared with the carcinogen-induced tumours, suggesting that carcinogen-induced and genetically engineered models lead to tumour development through different routes. The wild-type allele of Kras has been shown to act as a tumour suppressor in mouse models of non-small-cell lung cancer. We demonstrate that urethane-induced tumours from wild-type mice carry mostly (94%) Kras Q61R mutations, whereas those from Kras heterozygous animals carry mostly (92%) Kras Q61L mutations, indicating a major role for germline Kras status in mutation selection during initiation. The exome-wide mutation spectra in carcinogen-induced tumours overwhelmingly display signatures of the initiating carcinogen, while adenocarcinomas acquire additional C > T mutations at CpG sites. These data provide a basis for understanding results from human tumour genome sequencing, which has identified two broad categories of tumours based on the relative frequency of single-nucleotide variations and copy number alterations, and underline the importance of carcinogen models for understanding the complex mutation spectra seen in human cancers.

Exposure to levamisole-adulterated cocaine can induce a distinct clinical syndrome characterized by retiform purpura and/or agranulocytosis accompanied by an unusual constellation of serologic abnormalities including antiphospholipid antibodies, lupus anticoagulants, and very high titers of antineutrophil cytoplasmic antibodies. Two recent case reports suggest that levamisole-adulterated cocaine may also lead to renal disease in the form of pauci-immune glomerulonephritis. To explore this possibility, we reviewed cases of pauci-immune glomerulonephritis between 2010 and 2012 at an inner city safety net hospital where the prevalence of levamisole in the cocaine supply is known to be high. We identified 3 female patients and 1 male patient who had biopsy-proven pauci-immune glomerulonephritis, used cocaine, and had serologic abnormalities characteristic of levamisole-induced autoimmunity. Each also had some other form of clinical disease known to be associated with levamisole, either neutropenia or cutaneous manifestations. One patient had diffuse alveolar hemorrhage. Three of the 4 patients were treated with short courses of prednisone and cyclophosphamide, 2 of whom experienced stable long-term improvement in their renal function despite ongoing cocaine use. The remaining 2 patients developed end-stage renal disease and became dialysis-dependent. This report supports emerging concern of more wide spread organ toxicity associated with the use of levamisole-adulterated cocaine.