Hypoxia Negatively Regulates Antimetastatic PEDF in Melanoma Cells by a Hypoxia Inducible Factor-Independent, Autophagy Dependent Mechanism

Pigment epithelium-derived factor (PEDF), a member of the serine protease inhibitor (SERPIN) superfamily, displays a potent antiangiogenic and antimetastatic activity in a broad range of tumor types. Melanocytes and low aggressive melanoma cells secrete high levels of PEDF, while its expression is lost in highly aggressive melanomas. PEDF efficiently abrogates a number of functional properties critical for the acquisition of metastatic ability by melanoma cells, such as neovascularization, proliferation, migration, invasiveness and extravasation. In this study, we identify hypoxia as a relevant negative regulator of PEDF in melanocytes and low aggressive melanoma cells. PEDF was regulated at the protein level. Importantly, although downregulation of PEDF was induced by inhibition of 2-oxoglutarate-dependent dioxygenases, it was independent of the hypoxia inducible factor (HIF), a key mediator of the adaptation to hypoxia. Decreased PEDF protein was not mediated by inhibition of translation through untranslated regions (UTRs) in melanoma cells. Degradation by metalloproteinases, implicated on PEDF degradation in retinal pigment epithelial cells, or by the proteasome, was also excluded as regulatory mechanism in melanoma cells. Instead, we found that degradation by autophagy was critical for PEDF downregulation under hypoxia in human melanoma cells. Our findings show that hypoxic conditions encountered during primary melanoma growth downregulate antiangiogenic and antimetastasic PEDF by a posttranslational mechanism involving degradation by autophagy and could therefore contribute to the acquisition of highly metastatic potential characteristic of aggressive melanoma cells

A Bayesian assessment of an approximate model for unconfined water flow in sloping layered porous media

The prediction of water table height in unconfined layered porous media is a difficult modelling problem that typically requires numerical simulation. This paper proposes an analytical model to approximate the exact solution based on a steady-state Dupuit–Forchheimer analysis. The key contribution in relation to a similar model in the literature relies in the ability of the proposed model to consider more than two layers with different thicknesses and slopes, so that the existing model becomes a special case of the proposed model herein. In addition, a model assessment methodology based on the Bayesian inverse problem is proposed to efficiently identify the values of the physical parameters for which the proposed model is accurate when compared against a reference model given by MODFLOW-NWT, the open-source finite-difference code by the U.S. Geological Survey. Based on numerical results for a representative case study, the ratio of vertical recharge rate to hydraulic conductivity emerges as a key parameter in terms of model accuracy so that, when appropriately bounded, both the proposed model and MODFLOW-NWT provide almost identical results

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10−4) alone remained predictive after adjusting for clinical predictors

CD73 controls Myosin II-driven invasion, metastasis, and immunosuppression in amoeboid pancreatic cancer cells.

Pancreatic ductal adenocarcinoma (PDAC) has a very poor prognosis because of its high propensity to metastasize and its immunosuppressive microenvironment. Using a panel of pancreatic cancer cell lines, three-dimensional (3D) invasion systems, microarray gene signatures, microfluidic devices, mouse models, and intravital imaging, we demonstrate that ROCK-Myosin II activity in PDAC cells supports a transcriptional program conferring amoeboid invasive and immunosuppressive traits and in vivo metastatic abilities. Moreover, we find that immune checkpoint CD73 is highly expressed in amoeboid PDAC cells and drives their invasive, metastatic, and immunomodulatory traits. Mechanistically, CD73 activates RhoA-ROCK-Myosin II downstream of PI3K. Tissue microarrays of human PDAC biopsies combined with bioinformatic analysis reveal that rounded-amoeboid invasive cells with high CD73-ROCK-Myosin II activity and their immunosuppressive microenvironment confer poor prognosis to patients. We propose targeting amoeboid PDAC cells as a therapeutic strategy

Regional Activation of Myosin II in Cancer Cells Drives Tumor Progression via a Secretory Cross-Talk with the Immune Microenvironment

ROCK-Myosin II drives fast rounded-amoeboid migration in cancer cells during metastatic dissemination. Analysis of human melanoma biopsies revealed that amoeboid melanoma cells with high Myosin II activity are predominant in the invasive fronts of primary tumors in proximity to CD206+CD163+ tumor-associated macrophages and vessels. Proteomic analysis shows that ROCK-Myosin II activity in amoeboid cancer cells controls an immunomodulatory secretome, enabling the recruitment of monocytes and their differentiation into tumor-promoting macrophages. Both amoeboid cancer cells and their associated macrophages support an abnormal vasculature, which ultimately facilitates tumor progression. Mechanistically, amoeboid cancer cells perpetuate their behavior via ROCK-Myosin II-driven IL-1α secretion and NF-κB activation. Using an array of tumor models, we show that high Myosin II activity in tumor cells reprograms the innate immune microenvironment to support tumor growth. We describe an unexpected role for Myosin II dynamics in cancer cells controlling myeloid function via secreted factors