The management of advanced stage non-small cell lung cancer is increasingly based on diagnostic and predictive analyses performed mostly on limited amounts of tumor tissue. The evaluation of Epidermal Growth Factor Receptor (EGFR) mutations have emerged as the strongest predictor of response to EGFR-tyrosine kinase inhibitors mainly in patients with adenocarcinoma. Several EGFR mutation detection techniques are available, having both sensitivity and specificity issues, being the Sanger sequencing technique the reference standard, with the limitation of a relatively high amount of mutated cells needed for the analysis.

Mammalian target of rapamycin (mTOR) is a promising therapeutic target in advanced lung carcinoid patients. However, the mechanisms of mTOR modulation and of responsiveness to mTOR inhibitors are largely unclear. Our aim was to analyze the expression and functional role of specific miRNAs in lung carcinoids as an alternative mechanism targeting mTOR pathway.

Brugada syndrome is characterised by a typical ECG with ST segment elevation in the right precordial leads. Individuals with this condition are susceptible to ventricular arrhythmias and sudden cardiac death. The principal gene responsible for this syndrome is SCN5A, which encodes the α-subunit of the Nav1.5 voltage-gated sodium channel. Mutations involving other genes have been increasingly reported, but their contribution to Brugada syndrome has been poorly investigated. Here we focused on the SCN1B gene, which encodes the β1-subunit of the voltage-gated sodium channel and its soluble β1b isoform. SCN1B mutations have been associated with Brugada syndrome as well as with other cardiac arrhythmias and familial epilepsy. In this study, we have analysed SCN1B exons (including the alternatively-spliced exon 3A) and 3'UTR in 145 unrelated SCN5A-negative patients from a single centre. We took special care to report all identified variants (including polymorphisms), following the current nomenclature guidelines and considering both isoforms. We found two known and two novel (and likely deleterious) SCN1B variants. We also found two novel changes with low evidence of pathogenicity. Our findings contribute more evidence regarding the occurrence of SCN1B variants in Brugada syndrome, albeit with a low prevalence, which is in agreement with previous reports.

The American Society for Clinical Pathology (ASCP), College of American Pathologists (CAP), Association for Molecular Pathology (AMP), and the American Society of Clinical Oncology (ASCO) have been recently strongly recommended the evaluation of mismatch repair status (MMS) as molecular biomarkers in colorectal cancer for a better prognostic stratification of patients. This recommendation is emphasized by the recent evidence of Microsatellite Instability (MSI) as a predictive marker for chemotherapy and immunotherapy. In this scenario, the validation of molecular biomarker testing methods seems to be essential to design the most appropriate tailored therapy and the most suitable care strategy, respectively. In this study, we validated an alternative method based on capillary electrophoresis system label-free PCR (Qiaxcel system) to evaluate the MSI Bethesda Panel. We also parallel the results with a standard approach. Our data showed total concordance with the standard approach, with a highly time-efficient and easy procedure combined with high sensitivity for MSI detection. Alternative capillary electrophoresis based on label-free PCR such as the Qiaxel system is a very sensitive and specific method to detect MSI for the management of patients with colorectal cancer. This procedure is adequate and suitable in diagnostic routine for the evaluation of microsatellite repeats compared to standard procedures.

In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/ .