Systematic whole-genome sequencing reveals an unexpected diversity among actinomycetoma pathogens and provides insights into their antibacterial susceptibilities

Mycetoma is a neglected tropical chronic granulomatous inflammatory disease of the skin and subcutaneous tissues. More than 70 species with a broad taxonomic diversity have been implicated as agents of mycetoma. Understanding the full range of causative organisms and their antibiotic sensitivity profiles are essential for the appropriate treatment of infections. The present study focuses on the analysis of full genome sequences and antibiotic resistance profiles of actinomycetoma strains from patients seen at the Mycetoma Research Centre in Sudan with a view to developing rapid diagnostic tests. Seventeen pathogenic isolates obtained by surgical biopsies were sequenced using MinION and Illumina methods, and their antibiotic resistance profiles determined. The results highlight an unexpected diversity of actinomycetoma causing pathogens, including three Streptomyces isolates assigned to species not previously associated with human actinomycetoma and one new Streptomyces species. Thus, current approaches for clinical and histopathological classification of mycetoma may need to be updated. The standard treatment for actinomycetoma is a combination of sulfamethoxazole/trimethoprim and amoxicillin/clavulanic acid. Most tested isolates were not susceptible to sulfamethoxazole/trimethoprim or to amoxicillin alone. However, the addition of the β-lactamase inhibitor clavulanic acid to amoxicillin increased susceptibility, particularly for Streptomyces somaliensis and Streptomyces sudanensis . Actinomadura madurae isolates appear to be particularly resistant under laboratory conditions, suggesting that alternative agents, such as amikacin, should be considered for more effective treatment. The results obtained will inform future diagnostic methods for the identification of actinomycetoma and treatment.

Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma

BackgroundAn accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on the experience of a pathologist. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification.MethodsWe collected a whole-slide image of hematoxylin and eosin-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noise-specific deep learning model. The model was trained initially with 137 cases cropped into multiple-scaled datasets. Patch screening and dynamic label smoothing strategies are adopted to handle the histopathological liver image with noise annotation from the perspective of input and output. The model was then tested in an independent cohort of 455 cases with comparable tumor types and differentiations.ResultsExhaustive experiments demonstrated that our two-step method achieved 87.81% pixel-level accuracy and 98.77% slide-level accuracy in the test dataset. Furthermore, the generalization performance of our model was also verified using The Cancer Genome Atlas dataset, which contains 157 HCC pathological slides, and achieved an accuracy of 87.90%.ConclusionsThe noise-specific histopathological classification model of HCC based on deep learning is effective for the dataset with noisy annotation, and it significantly improved the pixel-level accuracy of the regular convolutional neural network (CNN) model. Moreover, the model also has an advantage in detecting well-differentiated HCC and microvascular invasion.

FLI1 and FRA1 transcription factors drive the transcriptional regulatory networks characterizing muscle invasive bladder cancer

Bladder cancer is mostly present in the form of urothelium carcinoma, causing over 150.000 deaths each year. Its histopathological classification as muscle invasive (MIBC) and non-muscle invasive (NMIBC) is the most prominent aspect, affecting the prognosis and progression of this disease. In this study, we defined the active regulatory landscape of MIBC and NMIBC cell lines using H3K27ac-seq and used an integrative data approach to combine our findings with existing data. Our analysis revealed FRA1 and FLI1 as the two critical transcription factors differentially regulating MIBC regulatory landscape. Importantly, we show that FRA1 and FLI1 regulate the genes involved in epithelial cell migration and cell junction organization. Knock-down of FRA1 and FLI1 in MIBC revealed the downregulation of several EMT-related genes such as MAP4K4 and FLOT1. Further, ChIP-SICAP performed for FRA1 and FLI1 enabled us to infer chromatin binding partners of these two transcription factors and link this information with their target genes, providing a comprehensive regulatory circuit for the genes implicated in invasive ability of the bladder cancer cells. Finally, for the first time we show that knock-down of FRA1 and FRA1-FLI1 double knock-down results in significant reduction of invasion capacity of MIBC cells towards muscle microenvironment using IC-CHIP assays. Our results collectively highlight the role of these two transcription factors in invasive characteristics of bladder cancer in selection and design of targeted options for treatment of MIBC.

Correlation of Clinical, Histopathological and Histomorphometric Features of Canine Soft Tissue Sarcomas

Soft-tissue sarcomas (STS) represent a heterogeneous group of tumours with similar histological characteristics and biological behaviour. This study aimed to describe the correlation between clinical, histopathological and histomorphometric features of STS in dogs. Medical records were reviewed to identify all dogs in which an STS was diagnosed between 2006-2017. Thirty cases were included, and tumour samples and medical records were recovered. Most of the dogs were mixed breed (40%) and 80% of the STS were located in the subcutaneous connective tissue. Histopathological classification showed that undifferentiated sarcoma (17%) and peripheral nerve sheath tumour (30%) were the most common STS. Grade I STS were obtained in 50% of cases (15/30), and grade II or III tumours compromised 43% (13/30) and 7% (2/30) respectively. The mitotic index ranged from zero to 26 (5.8 ± 7.5). Increased nucleus:cytoplasm ratio was moderately associated with higher tumour grade (p = 0.05; rS = 0.361) and mitotic index (p = 0.05; rS = 0.355), while the number of microvessels was positively correlated with degree of differentiation (p = 0.05; rS = 0.362) and nuclear pleomorphism (p = 0.036; rS = 0.384). Histomorphometry proved to be useful in the evaluation of STS, representing an additional tool correlated with well-established prognostic factors (histopathological grade, degree of differentiation, nuclear pleomorphism).

Methylation Drivers and Prognostic Implications in Sinonasal Poorly Differentiated Carcinomas

Background: Poorly differentiated sinonasal carcinomas (PDSNCs) are rare and aggressive malignancies, which include squamous cell carcinoma (SCC), sinonasal undifferentiated carcinoma (SNUC), and neuroendocrine carcinomas (NEC). Several epigenetic markers have been suggested to support the histopathological classification, predict prognosis, and guide therapeutic decision. Indeed, molecularly distinct subtypes of sinonasal carcinomas, including SMARCB1-INI1 or SMARCA4 deficient sinonasal carcinoma, isocitrate dehydrogenase (IDH)-mutant SNUC, ARID1A mutant PDSNCs, and NUT carcinomas, have recently been proposed as separate entities. Identification of aberrant DNA methylation levels associated with these specific epigenetic driver genes could be useful for prognostic and therapeutic purpose. Methods: Histopathological review and immunohistochemical study was performed on 53 PDSNCs. Molecular analysis included mutational profile by NGS, Sanger sequencing, and MLPA analyses, and global DNA methylation profile using LINE-1 bisulfite-PCR and pyrosequencing analysis. Results: Nine SWI/SNF complex defective cases and five IDH2 p.Arg172x cases were identified. A significant correlation between INI-1 or IDH2 defects and LINE-1 hypermethylation was observed (p = 0.002 and p = 0.032, respectively), which were associated with a worse prognosis (p = 0.007). Conclusions: Genetic and epigenetic characterization of PDSNCs should be performed to identify distinct prognostic entities, which deserved a tailored clinical treatment.

Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study

BackgroundHistopathological diagnosis of bone tumors is challenging for pathologists. We aim to classify bone tumors histopathologically in terms of aggressiveness using deep learning (DL) and compare performance with pathologists.MethodsA total of 427 pathological slides of bone tumors were produced and scanned as whole slide imaging (WSI). Tumor area of WSI was annotated by pathologists and cropped into 716,838 image patches of 256 × 256 pixels for training. After six DL models were trained and validated in patch level, performance was evaluated on testing dataset for binary classification (benign vs. non-benign) and ternary classification (benign vs. intermediate vs. malignant) in patch-level and slide-level prediction. The performance of four pathologists with different experiences was compared to the best-performing models. The gradient-weighted class activation mapping was used to visualize patch’s important area.ResultsVGG-16 and Inception V3 performed better than other models in patch-level binary and ternary classification. For slide-level prediction, VGG-16 and Inception V3 had area under curve of 0.962 and 0.971 for binary classification and Cohen’s kappa score (CKS) of 0.731 and 0.802 for ternary classification. The senior pathologist had CKS of 0.685 comparable to both models (p = 0.688 and p = 0.287) while attending and junior pathologists showed lower CKS than the best model (each p < 0.05). Visualization showed that the DL model depended on pathological features to make predictions.ConclusionDL can effectively classify bone tumors histopathologically in terms of aggressiveness with performance similar to senior pathologists. Our results are promising and would help expedite the future application of DL-assisted histopathological diagnosis for bone tumors.

Current Landscape of Non-Small Cell Lung Cancer: Epidemiology, Histological Classification, Targeted Therapies, and Immunotherapy

Non-small cell lung cancer (NSCLC) is a subtype of the most frequently diagnosed cancer in the world. Its epidemiology depends not only on tobacco exposition but also air quality. While the global trends in NSCLC incidence have started to decline, we can observe region-dependent differences related to the education and the economic level of the patients. Due to an increasing understanding of NSCLC biology, new diagnostic and therapeutic strategies have been developed, such as the reorganization of histopathological classification or tumor genotyping. Precision medicine is focused on the recognition of a genetic mutation in lung cancer cells called “driver mutation” to provide a variety of specific inhibitors of improperly functioning proteins. A rapidly growing group of approved drugs for targeted therapy in NSCLC currently allows the following mutated proteins to be treated: EGFR family (ERBB-1, ERBB-2), ALK, ROS1, MET, RET, NTRK, and RAF. Nevertheless, one of the most frequent NSCLC molecular sub-types remains without successful treatment: the K-Ras protein. In this review, we discuss the current NSCLC landscape treatment focusing on targeted therapy and immunotherapy, including first- and second-line monotherapies, immune checkpoint inhibitors with chemotherapy treatment, and approved predictive biomarkers.

Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors: a pilot study for future algorithmic approach

Background Histopathological classification of Wilms tumors determines treatment regimen. Machine learning has been shown to contribute to histopathological classification in various malignancies but requires large numbers of manually annotated images and thus specific pathological knowledge. This study aimed to assess whether trained, inexperienced observers could contribute to reliable annotation of Wilms tumor components for classification performed by machine learning. Methods Four inexperienced observers (medical students) were trained in histopathology of normal kidneys and Wilms tumors by an experienced observer (pediatric pathologist). Twenty randomly selected scanned Wilms tumor-slides (from n = 1472 slides) were annotated, and annotations were independently classified by both the inexperienced observers and two experienced pediatric pathologists. Agreement between the six observers and for each tissue element was measured using kappa statistics (κ). Results Pairwise interobserver agreement between all inexperienced and experienced observers was high (range: 0.845–0.950). The interobserver variability for the different histological elements, including all vital tumor components and therapy-related effects, showed high values for all κ-coefficients (> 0.827). Conclusions Inexperienced observers can be trained to recognize specific histopathological tumor and tissue elements with high interobserver agreement with experienced observers. Nevertheless, supervision by experienced pathologists remains necessary. Results of this study can be used to facilitate more rapid progress for supervised machine learning-based algorithm development in pediatric pathology and beyond.