No abstract

Claudins, the integral tight junction (TJ) proteins that regulate paracellular permeability and cell polarity, are frequently dysregulated in cancer; however, their role in neoplastic progression is unclear. Here, we demonstrated that knockout of Cldn18, a claudin family member highly expressed in lung alveolar epithelium, leads to lung enlargement, parenchymal expansion, increased abundance and proliferation of known distal lung progenitors, the alveolar epithelial type II (AT2) cells, activation of Yes-associated protein (YAP), increased organ size, and tumorigenesis in mice. Inhibition of YAP decreased proliferation and colony-forming efficiency (CFE) of Cldn18 -/-AT2 cells and prevented increased lung size, while CLDN18 overexpression decreased YAP nuclear localization, cell proliferation, CFE, and YAP transcriptional activity. CLDN18 and YAP interacted and colocalized at cell-cell contacts, while loss of CLDN18 decreased YAP interaction with Hippo kinases p-LATS1/2. Additionally, Cldn18 -/-mice had increased propensity to develop lung adenocarcinomas (LuAd) with age, and human LuAd showed stage-dependent reduction of CLDN18.1. These results establish CLDN18 as a regulator of YAP activity that serves to restrict organ size, progenitor cell proliferation, and tumorigenesis, and suggest a mechanism whereby TJ disruption may promote progenitor proliferation to enhance repair following injury.

Autophagy-related factors are implicated in metabolic adaptation and cancer metastasis. However, the role of autophagy factors in cancer progression and their effect in treatment response remain largely elusive. Recent studies have shown that UVRAG, a key autophagic tumor suppressor, is mutated in common human cancers. Here, we demonstrate that the cancer-related UVRAG frame-shift (FS), which does not result in a null mutation, is expressed as a truncated UVRAGFS in colorectal cancer (CRC) with microsatellite instability (MSI), and promotes tumorigenesis. UVRAGFS abrogates the normal functions of UVRAG, including autophagy, in a dominant-negative manner. Furthermore, expression of UVRAGFS can trigger CRC metastatic spread through Rac1 activation and epithelial-to-mesenchymal transition, independently of autophagy. Interestingly, UVRAGFS expression renders cells more sensitive to standard chemotherapy regimen due to a DNA repair defect. These results identify UVRAG as a new MSI target gene and provide a mechanism for UVRAG participation in CRC pathogenesis and treatment response.

Objective: To determine the intra-, inter-and test-retest variability of CT-based texture analysis (CTTA) metrics. Materials and methods: In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra-scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post-reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test-retest) and robustness (intra-scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter-scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust. Results: As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform-based texture metrics was overall most reliable across the two scanners and scanning conditions. Post-processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used. Conclusion: Following large-scale validation, identification of reliable CTTA metrics can aid in conducting large-scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists.