Machine-learning-based integrative -'omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction.

Environmental enteric dysfunction (EED) is a subclinical enteropathy challenging to diagnose due to an overlap of tissue features with other inflammatory enteropathies. EED subjects (n = 52) from Pakistan, controls (n = 25), and a validation EED cohort (n = 30) from Zambia were used to develop a machine-learning-based image analysis classification model. We extracted histologic feature representations from the Pakistan EED model and correlated them to transcriptomics and clinical biomarkers. In-silico metabolic network modeling was used to characterize alterations in metabolic flux between EED and controls and validated using untargeted lipidomics. Genes encoding beta-ureidopropionase, CYP4F3, and epoxide hydrolase 1 correlated to numerous tissue feature representations. Fatty acid and glycerophospholipid metabolism-related reactions showed altered flux. Increased phosphatidylcholine, lysophosphatidylcholine (LPC), and ether-linked LPCs, and decreased ester-linked LPCs were observed in the duodenal lipidome of Pakistan EED subjects, while plasma levels of glycine-conjugated bile acids were significantly increased. Together, these findings elucidate a multi-omic signature of EED.

Duodenal quantitative mucosal morphometry in children with environmental enteric dysfunction: a cross-sectional multi-country analysis.

BACKGROUND: Environmental enteric dysfunction (EED), a chronic inflammatory condition of the small intestine, is an important driver of childhood malnutrition globally. Quantifying intestinal morphology in EED allows for exploration of its association with functional and disease outcomes. OBJECTIVE: We sought to define morphometric characteristics of childhood EED and determine whether morphology features were associated with disease pathophysiology. METHODS: Morphometric measurements and histology were assessed on duodenal biopsy slides for this cross-sectional study from children with EED in Bangladesh, Pakistan, and Zambia (n=69), and those with no pathologic abnormality (NPA; n=8) or celiac disease (n=18) in North America. Immunohistochemistry was also conducted on 46, 8, and 18 biopsy slides, respectively. Linear mixed-effects regression models were used to reveal morphometric differences between EED compared to NPA or celiac disease, and identify associations between morphometry and histology or immunohistochemistry amongst children with EED. RESULTS: In duodenal biopsies, median EED villus height (248 µm), crypt depth (299 µm), and villus:crypt (V:C) ratio (0.9) values ranged between those of NPA (396 µm villus height; 246 µm crypt depth; 1.6 V:C ratio) and celiac disease (208 µm villus height; 365 µm crypt depth; 0.5 V:C ratio). Among EED biopsy slides, morphometric assessments were not associated with histologic parameters or immunohistochemical markers, other than pathologist determined subjective semi-quantitative villus architecture. CONCLUSIONS: Morphometric analysis of duodenal biopsy slides across geographies identified morphologic features of EED, specifically short villi, elongated crypts, and a smaller V:C ratio relative to NPA slides; although not as severe as in celiac slides. Morphometry did not explain other EED features, suggesting that EED histopathologic processes may be operating independently of morphology. While acknowledging the challenges with obtaining relevant tissue, these data form the basis for further assessments of the role of morphometry in EED.

Comprehensive molecular characterization of adenoid cystic carcinoma reveals tumor suppressors as novel drivers and prognostic biomarkers.

Adenoid cystic carcinoma (ACC) is a MYB-driven head and neck malignancy with high rates of local recurrence and distant metastasis and poor long-term survival. New effective targeted therapies and clinically useful biomarkers for patient stratification are needed to improve ACC patient survival. Here, we present an integrated copy number and transcriptomic analysis of ACC to identify novel driver genes and prognostic biomarkers. A total of 598 ACCs were studied. Clinical follow-up was available from 366 patients, the largest cohort analyzed to date. Copy number losses of 1p36 (70/492; 14%) and of the tumor suppressor gene PARK2 (6q26) (85/343; 25%) were prognostic biomarkers; patients with concurrent losses (n = 20) had significantly shorter overall survival (OS) than those with one or no deletions (p < 0.0001). Deletion of 1p36 independently predicted short OS in multivariate analysis (p = 0.02). Two pro-apoptotic genes, TP73 and KIF1B, were identified as putative 1p36 tumor suppressor genes whose reduced expression was associated with poor survival and increased resistance to apoptosis. PARK2 expression was markedly reduced in tumors with 6q deletions, and PARK2 knockdown increased spherogenesis and decreased apoptosis, indicating that PARK2 is a tumor suppressor in ACC. Moreover, analysis of the global gene expression pattern in 30 ACCs revealed a transcriptomic signature associated with short OS, multiple copy number alterations including 1p36 deletions, and reduced expression of TP73. Taken together, the results indicate that TP73 and PARK2 are novel putative tumor suppressor genes and potential prognostic biomarkers in ACC. Our studies provide new important insights into the pathogenesis of ACC. The results have important implications for biomarker-driven stratification of patients in clinical trials. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The Cooperative Human Tissue Network of the National Cancer Institute: Supporting Cancer Research for 35 Years.

The Cooperative Human Tissue Network was created by the NCI in 1987 to support a coordinated national effort to collect and distribute high quality, pathologist-validated human tissues for cancer research. Since then, the network has expanded to provide different types of tissue samples, blood and body fluid samples, immunohistologic and molecular sample preparations, tissue microarrays, and clinical datasets inclusive of biomarkers and molecular testing. From inception through the end of 2021, the network has distributed 1,375,041 biospecimens. It served 889 active investigators in 2021. The network has also taken steps to begin to optimize the representation of diverse communities among the distributed biospecimens. In this article, the authors review the 35-year history of this network, describe changes to the program over the last 15 years, and provide operational and scientific highlights from each of the divisions. Readers will learn how to engage with the network and about the continued evolution of the program for the future.

Somatic Sequencing and Microsatellite Instability Results From Mismatch Repair-deficient Endometrial Carcinoma Patients Without Lynch Syndrome ("Lynch-like" tumors): Implications for Heritable Cancer Screening, Molecular Prognostication, and Immunotherapeutic Vulnerability.

Immunostaining of endometrial carcinomas for mismatch repair (MMR) protein loss is standard-of-care for Lynch syndrome screening, but also identifies MMR-deficient tumors without germline pathogenic variants. While the majority show MLH1 hypermethylation ( MLH1hm ), somatic MMR pathogenic variants are increasingly recognized drivers of immunohistochemistry-germline discordance. Because MMR abnormalities with both germline and somatic origins have prognostic significance and impart susceptibility to immune checkpoint inhibitors, it is important to understand how frequently tumors with MMR immunohistochemical loss and normal germline testing ("Lynch-like" tumors) have underlying somatic MMR pathogenic variants. Somatic tumor sequencing±microsatellite instability (MSI) testing was performed on 18 endometrial cancers with MMR immunohistochemical loss but negative MMR germline results and negative MLH1hm where relevant. Tumor sequencing and MSI testing were successful in 94%. Where successful, 80% were MSI-high and 94% had a molecular correlate for the initial immunohistochemical interpretation. The single case without an identified somatic pathogenic variant was MSI-low and initially showed loss of MSH6 by immunohistochemistry but with extremely limited internal control staining. On review, MSH6 immunohistochemistry was reclassified as equivocal, and repeat staining revealed improved control expression with intact MSH6. Following reclassification of this case, 100% tumors with MMR deficiency by immunohistochemistry had at least 1 confirmed somatic MMR pathogenic variant, and 86% were MSI-high. These results demonstrate that when correctly interpreted immunohistochemistry is a strong surrogate for somatic MMR pathogenic variants and support its use as the frontline MMR biomarker in endometrial cancer for heritable screening, molecular prognostic classification, and immunotherapeutic biomarker testing purposes.

Inverse agonists of retinoic acid receptor/retinoid X receptor signaling as lineage-specific antitumor agents against human adenoid cystic carcinoma.

BACKGROUND: Adenoid cystic carcinoma (ACC) is a lethal malignancy of exocrine glands, characterized by the coexistence within tumor tissues of 2 distinct populations of cancer cells, phenotypically similar to the myoepithelial and ductal lineages of normal salivary epithelia. The developmental relationship linking these 2 cell types, and their differential vulnerability to antitumor treatments, remains unknown. METHODS: Using single-cell RNA sequencing, we identified cell-surface markers (CD49f, KIT) that enabled the differential purification of myoepithelial-like (CD49fhigh/KITneg) and ductal-like (CD49flow/KIT+) cells from patient-derived xenografts (PDXs) of human ACCs. Using prospective xenotransplantation experiments, we compared the tumor-initiating capacity of the 2 cell types and tested whether one could differentiate into the other. Finally, we searched for signaling pathways with differential activation between the 2 cell types and tested their role as lineage-specific therapeutic targets. RESULTS: Myoepithelial-like cells displayed higher tumorigenicity than ductal-like cells and acted as their progenitors. Myoepithelial-like and ductal-like cells displayed differential expression of genes encoding for suppressors and activators of retinoic acid signaling, respectively. Agonists of retinoic acid receptor (RAR) or retinoid X receptor (RXR) signaling (all-trans retinoic acid, bexarotene) promoted myoepithelial-to-ductal differentiation, whereas suppression of RAR/RXR signaling with a dominant-negative RAR construct abrogated it. Inverse agonists of RAR/RXR signaling (BMS493, AGN193109) displayed selective toxicity against ductal-like cells and in vivo antitumor activity against PDX models of human ACC. CONCLUSIONS: In human ACCs, myoepithelial-like cells act as progenitors of ductal-like cells, and myoepithelial-to-ductal differentiation is promoted by RAR/RXR signaling. Suppression of RAR/RXR signaling is lethal to ductal-like cells and represents a new therapeutic approach against human ACCs.

Mapping the Immune Cell Microenvironment with Spatial Profiling in Muscle Tissue Injected with the Venom of Daboia russelii.

Pathological and inflammatory events in muscle after the injection of snake venoms vary in different regions of the affected tissue and at different time intervals. In order to study such heterogeneity in the immune cell microenvironment, a murine model of muscle necrosis based on the injection of the venom of Daboia russelii was used. Histological and immunohistochemical methods were utilized to identify areas in muscle tissue with a different extent of muscle cell damage, based on the presence of hypercontracted muscle cells, a landmark of necrosis, and on the immunostaining for desmin. A gradient of inflammatory cells (neutrophils and macrophages) was observed from heavily necrotic areas to less damaged and non-necrotic areas. GeoMx® Digital Spatial Profiler (NanoString, Seattle, WA, USA) was used for assessing the presence of markers of various immune cells by comparing high-desmin (nondamaged) and low-desmin (damaged) regions of muscle. Markers of monocytes, macrophages, M2 macrophages, dendritic cells, neutrophils, leukocyte adhesion and migration markers, and hematopoietic precursor cells showed higher levels in low-desmin regions, especially in samples collected 24 hr after venom injection, whereas several markers of lymphocytes did not. Moreover, apoptosis (BAD) and extracellular matrix (fibronectin) markers were also increased in low-desmin regions. Our findings reveal a hitherto-unknown picture of immune cell microheterogeneity in venom-injected muscle which greatly depends on the extent of muscle cell damage and the time lapse after venom injection.

Quantitative Morphometry and Machine Learning Model to Explore Duodenal and Rectal Mucosal Tissue of Children with Environmental Enteric Dysfunction.

Environmental enteric dysfunction (EED) is a subclinical enteropathy prevalent in resource-limited settings, hypothesized to be a consequence of chronic exposure to environmental enteropathogens, resulting in malnutrition, growth failure, neurocognitive delays, and oral vaccine failure. This study explored the duodenal and colonic tissues of children with EED, celiac disease, and other enteropathies using quantitative mucosal morphometry, histopathologic scoring indices, and machine learning-based image analysis from archival and prospective cohorts of children from Pakistan and the United States. We observed villus blunting as being more prominent in celiac disease than in EED, as shorter lengths of villi were observed in patients with celiac disease from Pakistan than in those from the United States, with median (interquartile range) lengths of 81 (73, 127) µm and 209 (188, 266) µm, respectively. Additionally, per the Marsh scoring method, celiac disease histologic severity was increased in the cohorts from Pakistan. Goblet cell depletion and increased intraepithelial lymphocytes were features of EED and celiac disease. Interestingly, the rectal tissue from cases with EED showed increased mononuclear inflammatory cells and intraepithelial lymphocytes in the crypts compared with controls. Increased neutrophils in the rectal crypt epithelium were also significantly associated with increased EED histologic severity scores in duodenal tissue. We observed an overlap between diseased and healthy duodenal tissue upon leveraging machine learning image analysis. We conclude that EED comprises a spectrum of inflammation in the duodenum, as previously described, and the rectal mucosa, warranting the examination of both anatomic regions in our efforts to understand and manage EED.

Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways.

We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.

Rearrangements, Expression, and Clinical Significance of MYB and MYBL1 in Adenoid Cystic Carcinoma: A Multi-Institutional Study.

Adenoid cystic carcinoma (ACC) is an aggressive head and neck malignancy characterized by a t (6;9) translocation resulting in an MYB-NFIB gene fusion or, more rarely, an MYBL1 fusion. The true frequency and clinical significance of these alterations are still unclear. Here, we have used tissue microarrays and analyzed 391 ACCs and 647 non-ACC salivary neoplasms to study the prevalence, expression, and clinical significance of MYB/MYBL1 alterations by FISH and immunohistochemistry. Alterations of MYB or MYBL1 were found in 78% of the cases, of which 62% had MYB alterations and 16% had MYBL1 rearrangements. Overexpression of MYB/MYBL1 oncoproteins was detected in 93% of the cases. MYB split signal, seen in 39% of the cases, was specific for ACC and not encountered in non-ACC salivary tumors. Loss of the 3'-part of MYB was enriched in grade 3 tumors and was a significant independent prognostic biomarker for overall survival in multivariate analyses. We hypothesize that loss of the 3'-part of MYB results from an unbalanced t(6;9) leading to an MYB-NFIB fusion with concomitant loss of the segment distal to the MYB breakpoint in 6q23.3. Our study provides new knowledge about the prevalence and clinical significance of MYB/MYBL1 alterations and indicates the presence of genes with tumor suppressive functions in 6q23.3-qter that contribute to poor prognosis and short overall survival in ACC.

MLH1/PMS2-deficient Endometrial Carcinomas in a Universally Screened Population: MLH1 Hypermethylation and Germline Mutation Status.

MLH1/PMS2 loss due to epigenetic hypermethylation of the MLH1 promoter is the most common cause of mismatch repair deficiency in endometrial carcinoma, and typically provides reassurance against an associated germline mutation. To further characterize the genetic features of MLH1/PMS2-deficient endometrial cancers, the departmental database was searched for cases with dual MLH1/PMS2 loss and retained MSH2/6 expression which underwent MLH1 hypermethylation testing. Genetic testing results were obtained when available. One hundred seventeen endometrial cancers met inclusion criteria: 100 (85%) were MLH1-hypermethylated, 3 (3%) were low-level/borderline, 7 (6%) were nonmethylated, and 7 (6%) were insufficient for testing. Sixteen cases (12 MLH1-hypermethylated, 3 nonmethylated, and 1 insufficient for testing) underwent germline testing, 6 of which (37.5%) demonstrated germline variants of unknown significance (VUS) (MSH6, PMS2, POLD1, BRIP1, RAD51D, CHEK2) but no known deleterious mutations. Notably, however, the patients harboring the MSH6 and PMS2 germline VUS had clinical features concerning for Lynch syndrome. One nonmethylated, germline-normal case underwent somatic tumor testing, and demonstrated a somatic MLH1 mutation. In summary, MLH1-hypermethylation accounts for the vast majority of MLH1/PMS2-deficient cancers in a universally screened population, although MLH1 somatic and germline mutations can occur. Occasionally, patients with MLH1-hypermethlated tumors also bear germline VUS in other mismatch repair genes as well as genes implicated in other hereditary cancer syndromes, but their clinical relevance is unclear. Family and personal cancer histories must always be evaluated to determine the need for germline testing in women with loss of MLH1/PMS2, even in the setting of hypermethylation.

Genetic Mutations Associated with Hormone-Positive Breast Cancer in a Small Cohort of Ethiopian Women.

In Ethiopia, a breast cancer diagnosis is associated with a prognosis significantly worse than that of Europe and the US. Further, patients presenting with breast cancer in Ethiopia are far younger, on average, and patients are typically diagnosed at very late stages, relative to breast cancer patients of European descent. Emerging data suggest that a large proportion of Ethiopian patients have hormone-positive (ER+) breast cancer. This is surprising given (1) that patients have late-stage breast cancer at the time of diagnosis, (2) that African Americans with breast cancer frequently have triple negative breast cancer (TNBC), and (3) these patients typically receive chemotherapy, not hormone-targeting drugs. To further examine the similarity of Ethiopian breast tumors to those of African Americans or of those of European descent, we sequenced matched tumor and normal adjacent tissue from Ethiopian patients from a small pilot collection. We identified mutations in 615 genes across all three patients, unique to the tumor tissue. Across this analysis, we found far more mutations shared between Ethiopian patient tissue and that from white patients (103) than we did comparing to African Americans (3). Several mutations were found in extracellular matrix encoding genes with known roles in tumor cell growth and metastasis. We suggest future mechanistic studies on this disease focus on these genes first, toward finding new treatment strategies for breast cancer patients in Ethiopia.

Artificial Intelligence-based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection.

OBJECTIVES: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; environmental enteropathy (EE) and celiac disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. METHODS: Data for the secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using CNNs including one with multizoom architecture. Gradient-weighted class activation mappings (Grad-CAMs) were used to visualize the models' decision-making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively. RESULTS: Four hundred and sixty-one high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0-121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated models' ability to learn different microscopic morphological features for EE, CD, and controls. CONCLUSIONS: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision-making process. Grad-CAMs illuminated the otherwise "black box" of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.

A novel histological index for evaluation of environmental enteric dysfunction identifies geographic-specific features of enteropathy among children with suboptimal growth.

BACKGROUND: A major limitation to understanding the etiopathogenesis of environmental enteric dysfunction (EED) is the lack of a comprehensive, reproducible histologic framework for characterizing the small bowel lesions. We hypothesized that the development of such a system will identify unique histology features for EED, and that some features might correlate with clinical severity. METHODS: Duodenal endoscopic biopsies from two cohorts where EED is prevalent (Pakistan, Zambia) and North American children with and without gluten sensitive enteropathy (GSE) were processed for routine hematoxylin & eosin (H&E) staining, and scanned to produce whole slide images (WSIs) which we shared among study pathologists via a secure web browser-based platform. A semi-quantitative scoring index composed of 11 parameters encompassing tissue injury and response patterns commonly observed in routine clinical practice was constructed by three gastrointestinal pathologists, with input from EED experts. The pathologists then read the WSIs using the EED histology index, and inter-observer reliability was assessed. The histology index was further used to identify within- and between-child variations as well as features common across and unique to each cohort, and those that correlated with host phenotype. RESULTS: Eight of the 11 histologic scoring parameters showed useful degrees of variation. The overall concordance across all parameters was 96% weighted agreement, kappa 0.70, and Gwet's AC 0.93. Zambian and Pakistani tissues shared some histologic features with GSE, but most features were distinct, particularly abundance of intraepithelial lymphocytes in the Pakistani cohort, and marked villous destruction and loss of secretory cell lineages in the Zambian cohort. CONCLUSIONS: We propose the first EED histology index for interpreting duodenal biopsies. This index should be useful in future clinical and translational studies of this widespread, poorly understood, and highly consequential disorder, which might be caused by multiple contributing processes, in different regions of the world.

Toward Improving Practices for Submission of Diagnostic Tissue Blocks for National Cancer Institute Clinical Trials.

OBJECTIVES: The National Cancer Institute (NCI) National Clinical Trials Network performs phase II and III clinical trials, which increasingly rely on the submission of diagnostic formalin-fixed, paraffin-embedded tissue blocks for biomarker assessment. Simultaneously, advances in precision oncology require that clinical centers maintain diagnostic specimens for ancillary, standard-of-care diagnostics. This has caused tissue blocks to become a limited resource for advancing the NCI clinical trial enterprise and the practice of modern molecular pathology. METHODS: The NCI convened a 1-day workshop of multidisciplined experts to discuss barriers and strategic solutions to facilitate diagnostic block submission for clinical trial science, from the perspective of patient advocates, legal experts, pathologists, and clinical oncologists. RESULTS: The expert views and opinions were carefully noted and reported. CONCLUSIONS: Recommendations were proposed to reduce institutional barriers and to assist organizations in developing clear policies regarding diagnostic block submission for clinical trials.

Study of Environmental Enteropathy and Malnutrition (SEEM) in Pakistan: protocols for biopsy based biomarker discovery and validation.

BACKGROUND: Environmental Enteropathy (EE), characterized by alterations in intestinal structure, function, and immune activation, is believed to be an important contributor to childhood undernutrition and its associated morbidities, including stunting. Half of all global deaths in children < 5 years are attributable to under-nutrition, making the study of EE an area of critical priority. METHODS: Community based intervention study, divided into two sub-studies, 1) Longitudinal analyses and 2) Biopsy studies for identification of EE features via omics analyses. Birth cohorts in Matiari, Pakistan established: moderately or severely malnourished (weight for height Z score (WHZ) < - 2) children, and well-nourished (WHZ > 0) children. Blood, urine, and fecal samples, for evaluation of potential biomarkers, will be collected at various time points from all participants (longitudinal analyses). Participants will receive appropriate educational and nutritional interventions; non-responders will undergo further evaluation to determine eligibility for further workup, including upper gastrointestinal endoscopy. Histopathological changes in duodenal biopsies will be compared with duodenal biopsies obtained from USA controls who have celiac disease, Crohn's disease, or who were found to have normal histopathology. RNA-Seq will be employed to characterize mucosal gene expression across groups. Duodenal biopsies, luminal aspirates from the duodenum, and fecal samples will be analyzed to define microbial community composition (omic analyses). The relationship between histopathology, mucosal gene expression, and community configuration will be assessed using a variety of bioinformatic tools to gain better understanding of disease pathogenesis and to identify mechanism-based biomarkers. Ethical review committees at all collaborating institutions have approved this study. All results will be made available to the scientific community. DISCUSSION: Operational and ethical constraints for safely obtaining intestinal biopsies from children in resource-poor settings have led to a paucity of human tissue-based investigations to understand and reverse EE in vulnerable populations. Furthermore, EE biomarkers have rarely been correlated with gold standard histopathological confirmation. The Study of Environmental Enteropathy and Malnutrition (SEEM) is designed to better understand the pathophysiology, predictors, biomarkers, and potential management strategies of EE to inform strategies to eradicate this debilitating pathology and accelerate progress towards the 2030 Sustainable Development Goals. TRIAL REGISTRATION: Retrospectively registered; clinicaltrials.gov ID NCT03588013 .

Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children.

Importance: Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap. Objective: To develop a convolutional neural network (CNN) to enhance the detection of pathologic morphological features in diseased vs healthy duodenal tissue. Design, Setting, and Participants: In this prospective diagnostic study, a CNN consisting of 4 convolutions, 1 fully connected layer, and 1 softmax layer was trained on duodenal biopsy images. Data were provided by 3 sites: Aga Khan University Hospital, Karachi, Pakistan; University Teaching Hospital, Lusaka, Zambia; and University of Virginia, Charlottesville. Duodenal biopsy slides from 102 children (10 with EE from Aga Khan University Hospital, 16 with EE from University Teaching Hospital, 34 with CD from University of Virginia, and 42 with no disease from University of Virginia) were converted into 3118 images. The CNN was designed and analyzed at the University of Virginia. The data were collected, prepared, and analyzed between November 2017 and February 2018. Main Outcomes and Measures: Classification accuracy of the CNN per image and per case and incorrect classification rate identified by aggregated 10-fold cross-validation confusion/error matrices of CNN models. Results: Overall, 102 children participated in this study, with a median (interquartile range) age of 31.0 (20.3-75.5) months and a roughly equal sex distribution, with 53 boys (51.9%). The model demonstrated 93.4% case-detection accuracy and had a false-negative rate of 2.4%. Confusion metrics indicated most incorrect classifications were between patients with CD and healthy patients. Feature map activations were visualized and learned distinctive patterns, including microlevel features in duodenal tissues, such as alterations in secretory cell populations. Conclusions and Relevance: A machine learning-based histopathological analysis model demonstrating 93.4% classification accuracy was developed for identifying and differentiating between duodenal biopsies from children with EE and CD. The combination of the CNN with a deconvolutional network enabled feature recognition and highlighted secretory cells' role in the model's ability to differentiate between these histologically similar diseases.

Sub-terahertz vibrational spectroscopy of ovarian cancer and normal control tissue for molecular diagnostic technology.

We introduce here recently developed highly resolved Sub-Terahertz resonance spectroscopy of biological molecules and cells combined with molecular dynamics (MD) computational analysis as a new approach for optical visualization and quantification of the presence of microRNAs, particularly the mir-200 family, as potential biomarkers in samples from tissue of epithelial ovarian cancers for disease early detection, analysis, prognosis and treatment.METHOD: A set of samples for this study was prepared from anonymized archival formalin-fixed, paraffin-embedded ovarian epithelial tissue containing regions of invasive neoplastic cells from cases of high-histologic grade serous papillary ovarian carcinoma. Control samples were normal mucosa from fallopian tubes of patients with no known malignancy. Spectroscopic characterization of tissue samples in this study was performed using a continuous wave, frequency domain automated spectrometer operating at room temperature in the spectral region of 310-500 GHz. The spectral results were compared with molecular dynamics simulations and absorption coefficient calculations utilized to predict the absorption spectra.RESULTS: The characteristic spectroscopic features in absorption spectra, particularly the presence of absorption peaks near 13 cm-1 have been identified as cancer indicators. Tissue samples heterogeneity, reflected by diverse spectral signatures, provides additional, very specific information that may be used for identification of cancer subtypes, clinical behavior or sensitivity to specific therapies. Further work is warranted to determine if this signature can be detected in bio-fluids from ovarian cancer patients. If strongly correlated with cancer burden, it may then be investigated as a potential new biomarker for disease monitoring, and also perhaps as a biomarker for cancer screening.

Serial patient-derived orthotopic xenografting of adenoid cystic carcinomas recapitulates stable expression of phenotypic alterations and innervation.

BACKGROUND: Patient-derived xenograft (PDX) models have significantly enhanced cancer research, and often serve as a robust model. However, enhanced growth rate and altered pathological phenotype with serial passages have repeatedly been shown in adenoid cystic carcinoma (ACC) PDX tumors, which is a major concern. METHODS: We evaluated the fidelity of ACCs in their natural habitat by performing ACC orthotopic xenotransplantation (PDOX) in salivary glands. FINDINGS: Our PDOX model enabled solid tumors to integrate within the local epithelial, stromal and neuronal environment. Over serial passages, PDOX tumors maintained their stereotypic MYB-NFIB translocation, and FGFR2 and ATM point mutations. Tumor growth rate and histopathology were retained, including ACCs hallmark presentations of cribriform, tubular, solid areas and innervation. We also demonstrate that the PDOX model retains its capacity as a tool for drug testing. INTERPRETATION: Unlike the precedent PDX model, our data shows that the PDOX is a superior model for future cancer biology and therapy research. FUND: This work was supported by the National Institutes of Health (NIH)/National Institute of Dental and Craniofacial Research (NIDCR) grants DE022557, DE027034, and DE027551.

CeliacNet: Celiac Disease Severity Diagnosis on Duodenal Histopathological Images Using Deep Residual Networks.

Celiac Disease (CD) is a chronic autoimmune disease that affects the small intestine in genetically predisposed children and adults. Gluten exposure triggers an inflammatory cascade which leads to compromised intestinal barrier function. If this enteropathy is unrecognized, this can lead to anemia, decreased bone density, and, in longstanding cases, intestinal cancer. The prevalence of the disorder is 1% in the United States. An intestinal (duodenal) biopsy is considered the "gold standard" for diagnosis. The mild CD might go unnoticed due to non-specific clinical symptoms or mild histologic features. In our current work, we trained a model based on deep residual networks to diagnose CD severity using a histological scoring system called the modified Marsh score. The proposed model was evaluated using an independent set of 120 whole slide images from 15 CD patients and achieved an AUC greater than 0.96 in all classes. These results demonstrate the diagnostic power of the proposed model for CD severity classification using histological images.