A machine learning approach for single cell interphase cell cycle staging.

The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited. It is therefore urgent to establish accurate strategies combining bioimaging with high-content image analysis for a bona fide classification. In this study we developed a supervised machine learning method for interphase cell cycle staging of individual adherent cells using in situ fluorescence images of nuclei stained with DAPI. A Support Vector Machine (SVM) classifier operated over normalized nuclear features using more than 3500 DAPI stained nuclei. Molecular ground truth labels were obtained by automatic image processing using fluorescent ubiquitination-based cell cycle indicator (Fucci) technology. An average F1-Score of 87.7% was achieved with this framework. Furthermore, the method was validated on distinct cell types reaching recall values higher than 89%. Our method is a robust approach to identify cells in G1 or S/G2 at the individual level, with implications in research and clinical applications.

The Extracellular Matrix: An Accomplice in Gastric Cancer Development and Progression.

The extracellular matrix (ECM) is a dynamic and highly organized tissue structure, providing support and maintaining normal epithelial architecture. In the last decade, increasing evidence has emerged demonstrating that alterations in ECM composition and assembly strongly affect cellular function and behavior. Even though the detailed mechanisms underlying cell-ECM crosstalk are yet to unravel, it is well established that ECM deregulation accompanies the development of many pathological conditions, such as gastric cancer. Notably, gastric cancer remains a worldwide concern, representing the third most frequent cause of cancer-associated deaths. Despite increased surveillance protocols, patients are usually diagnosed at advanced disease stages, urging the identification of novel diagnostic biomarkers and efficient therapeutic strategies. In this review, we provide a comprehensive overview regarding expression patterns of ECM components and cognate receptors described in normal gastric epithelium, pre-malignant lesions, and gastric carcinomas. Important insights are also discussed for the use of ECM-associated molecules as predictive biomarkers of the disease or as potential targets in gastric cancer.

S100P is a molecular determinant of E-cadherin function in gastric cancer.

BACKGROUND: E-cadherin has been awarded a key role in the aetiology of both sporadic and hereditary forms of gastric cancer. In this study, we aimed to identify molecular interactors that influence the expression and function of E-cadherin associated to cancer. METHODS: A data mining approach was used to predict stomach-specific candidate genes, uncovering S100P as a key candidate. The role of S100P was evaluated through in vitro functional assays and its expression was studied in a gastric cancer tissue microarray (TMA). RESULTS: S100P was found to contribute to a cancer pathway dependent on the context of E-cadherin function. In particular, we demonstrated that S100P acts as an E-cadherin positive regulator in a wild-type E-cadherin context, and its inhibition results in decreased E-cadherin expression and function. In contrast, S100P is likely to be a pro-survival factor in gastric cancer cells with loss of functional E-cadherin, contributing to an oncogenic molecular program. Moreover, expression analysis in a gastric cancer TMA revealed that S100P expression impacts negatively among patients bearing Ecad- tumours, despite not being significantly associated with overall survival on its own. CONCLUSIONS: We propose that S100P has a dual role in gastric cancer, acting as an oncogenic factor in the context of E-cadherin loss and as a tumour suppressor in a functional E-cadherin setting. The discovery of antagonist effects of S100P in different E-cadherin contexts will aid in the stratification of gastric cancer patients who may benefit from S100P-targeted therapies.

Clinical spectrum and pleiotropic nature of CDH1 germline mutations.

CDH1 encodes E-cadherin, a key protein in adherens junctions. Given that E-cadherin is involved in major cellular processes such as embryogenesis and maintenance of tissue architecture, it is no surprise that deleterious effects arise from its loss of function. E-cadherin is recognised as a tumour suppressor gene, and it is well established that CDH1 genetic alterations cause diffuse gastric cancer and lobular breast cancer-the foremost manifestations of the hereditary diffuse gastric cancer syndrome. However, in the last decade, evidence has emerged demonstrating that CDH1 mutations can be associated with lobular breast cancer and/or several congenital abnormalities, without any personal or family history of diffuse gastric cancer. To date, no genotype-phenotype correlations have been observed. Remarkably, there are reports of mutations affecting the same nucleotide but inducing distinct clinical outcomes. In this review, we bring together a comprehensive analysis of CDH1-associated disorders and germline alterations found in each trait, providing important insights into the biological mechanisms underlying E-cadherin's pleiotropic effects. Ultimately, this knowledge will impact genetic counselling and will be relevant to the assessment of risk of cancer development or congenital malformations in CDH1 mutation carriers.