N-Glycan profiling of lung adenocarcinoma in patients at different stages of disease

Lung adenocarcinoma (LAC) is the most common form of lung cancer that increases in non-smokers at younger age. Altered protein glycosylation is one of the hallmarks of malignancy, its role in cancer progression is still poorly understood. In this study, we report mass spectrometric (MS) analysis of N-glycans released from fresh or defrosted tissue specimens from 24 patients with LAC. Comparison of cancerous versus adjacent healthy tissues revealed substantial differences in N-glycan profiles associated with disease. The significant increase in paucimannose and high-mannose glycans with 6-9 mannose residues and decline in the sialylated complex biantenary core fucosylated glycan with composition NeuAcGal(2)GlcNAc(2)Man(3)GlcNAc(2)Fuc were general features of tumors. In addition, 42 new N-glycan compositions were detected in cancerous tissues. The prominent changes in advanced disease stages were mostly observed in core fucosylated N-glycans with additional fucose (Fuc) residue/s and enhanced branching with non-galactosylated N-acetyl-glucosamine (GlcNAc) units. Both of these monosaccharide types were linked preferably on the 6-antenna. Importantly, as compared with noncancerous tissues, a number of these significant changes were clearly detectable early on in stage I. Application of N-glycan data obtained from tissues was next assessed and validated for evaluation of small sized biopsies obtained via bronchoscopy. In summary, observed alterations and data of newly detected N-glycans expand knowledge about the glycosylation in LAC and may contribute to research in more tailored therapies. Moreover, the results demonstrate effectiveness of the presented approach for utility in rapid discrimination of cancerous from healthy lung tissues

Brenner tumor of the ovary — ultrasound features and clinical management of a rare ovarian tumor mimicking ovarian cancer

  Objectives: To describe the ultrasound features of benign Brenner tumor in the background of complex clinical and histopathological pictures. Material and methods: We retrospectively identified patients with histologically confirmed benign Brenner tumor of the ovary who were treated in our institution in 2003–2016, and for whom complete imaging, clinical, perioperative and histopathological data were available in the database. Ultrasound findings were drawn from images and reports using terms and definitions of the International Ovarian Tumor Analysis group and pattern recognition description was applied. Results: Twenty-three patients were identified, most postmenopausal and asymptomatic. On ultrasound, 19/23 tumors were found unilaterally, 4/23 bilaterally, and 82% of tumors were detected in the left ovary. Most Brenner tumors (16/23) contained solid components and revealed no or minimal blood flow by subjective color score upon Doppler examination (19/23, 83%). Calcifications with shadowing were observed in 57% of all Brenner tumors and in 81% of tumors containing solid components. The complex appearance of the tumor misled the sonographers to describe the mass as malignant in 9 cases (39%), and frozen section was performed perioperatively. Surgery was performed via laparoscopy in 11 (48%) and via laparotomy in 12 (52%) cases. Conclusions: The complexity of the ultrasound picture, consisting of features like calcifications with acoustic shadowing, a poorly vascularized solid mass, and a left-sided localization could be signs of a benign Brenner tumor and could preop­eratively help to differentiate between benign and malignant tumor

Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study

Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist’s classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen’s kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen’s kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen’s kappa of 0.43 but was comparable for the binary classification with a substantial Cohen’s kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.publishedVersio

Does an Endometrial Cancer Diagnosis among Asymptomatic Patients Improve Prognosis?

Background: Endometrial cancer is the most common gynecological malignancy in developed countries with no screening available. There is still a tendency to provide invasive bioptic verification in asymptomatic women with abnormal ultrasound findings to diagnose carcinoma in a preclinical phase; even though, it is not supported by European guidelines. Our goal was to determine DFS (disease-free survival), OS (overall survival), and DSS (disease-specific survival) differences between symptom-free and symptomatic (bleeding, or spotting) endometrial cancer patients with similar stage and tumor/clinical characteristics. Methods: All of our patients with endometrial cancer following surgical treatment between 2006 and 2019 were assessed, evaluating risk factors for recurrence and death while focusing on bleeding using univariable and multivariable analysis. Results: 625 patients meeting the inclusion criteria were divided into asymptomatic (n = 144, 23%) and symptomatic (n = 481, 77%) groups. The median follow-up was 3.6 years. Using univariable analysis, symptomatic patients had a three times higher risk of recurrence (HR 3.1 (95% Cl 1.24–7.77), p = 0.016). OS (HR 1.35 (0.84–2.19), p = 0.219) and DSS (HR 1.66 (0.64–4.28), p = 0.3) were slightly worse without reaching statistical significance. In our multivariable analysis, symptomatology was deemed completely insignificant in all monitored parameters (DFS: HR 2.03 (0.79–5.24), p = 0.144; OS: HR 0.72 (0.43–1.21), p = 0.216). Conclusions: The symptomatic endometrial cancer patients risk factor of earlier recurrence and death is insignificantly higher when compared with the asymptomatic cohort. However, multivariable analysis verifies that prognosis worsens with other clinically relevant parameters, not by symptomatology itself. In terms of survival outcome in EC patients, we recognized symptomatology as a non-significant marker for the patient’s prognosis

Code and data for "RIP-seq reveals RNAs that interact with RNA polymerase and the primary sigma factor in bacteria"

<p>Data and code for "RIP-seq reveals RNAs that interact with RNA polymerase and the primary sigma factor in bacteria".</p> <p>See the README file for description of the contents.</p> <p>The access to data will be made public alongside publication of the manuscript.</p&gt

Endometrial pipelle biopsy computer-aided diagnosis : a feasibility study

Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses