Ligand-dependent EGFR activation induces the co-expression of IL-6 and PAI-1 via the NFkB pathway in advanced-stage epithelial ovarian cancer.

The epidermal growth factor receptor (EGFR), a member of the ErbB family of receptor tyrosine kinases, is expressed in up to 70% of epithelial ovarian cancers (EOCs), where it correlates with poor prognosis. The majority of EOCs are diagnosed at an advanced stage, and at least 50% present malignant ascites. High levels of IL-6 have been found in the ascites of EOC patients and correlate with shorter survival. Herein, we investigated the signaling cascade led by EGFR activation in EOC and assessed whether EGFR activation could induce an EOC microenvironment characterized by pro-inflammatory molecules. In vitro analysis of EOC cell lines revealed that ligand-stimulated EGFR activated NFkB-dependent transcription and induced secretion of IL-6 and plasminogen activator inhibitor (PAI-1). IL-6/PAI-1 expression and secretion were strongly inhibited by the tyrosine kinase inhibitor AG1478 and EGFR silencing. A significant reduction of EGF-stimulated IL-6/PAI-1 secretion was also obtained with the NFkB inhibitor dehydroxymethylepoxyquinomicin. Of 23 primary EOC tumors from advanced-stage patients with malignant ascites at surgery, 12 co-expressed membrane EGFR, IL-6 and PAI-1 by immunohistochemistry; both IL-6 and PAI-1 were present in 83% of the corresponding ascites. Analysis of a publicly available gene-expression data set from 204 EOCs confirmed a significant correlation between IL-6 and PAI-1 expression, and patients with the highest IL-6 and PAI-1 co-expression showed a significantly shorter progression-free survival time (P=0.028). This suggests that EGFR/NFkB/IL-6-PAI-1 may have a significant impact on the therapy of a particular subset of EOC, and that IL-6/PAI-1 co-expression may be a novel prognostic marker

Molecular cloning and functional characterization of brefeldin A-ADP-ribosylated substrate. A novel protein involved in the maintenance of the Golgi structure.

Brefeldin A (BFA) is a fungal metabolite that disassembles the Golgi apparatus into tubular networks and causes the dissociation of coatomer proteins from Golgi membranes. We have previously shown that an additional effect of BFA is to stimulate the ADP-ribosylation of two cytosolic proteins of 38 and 50 kDa (brefeldin A-ADP-riboslyated substrate (BARS)) and that this effect greatly facilitates the Golgi-disassembling activity of the toxin. In this study, BARS has been purified from rat brain cytosol and microsequenced, and the BARS cDNA has been cloned. BARS shares high homology with two known proteins, C-terminal-binding protein 1 (CtBP1) and CtBP2. It is therefore a third member of the CtBP family. The role of BARS in Golgi disassembly by BFA was verified in permeabilized cells. In the presence of dialyzed cytosol that had been previously depleted of BARS or treated with an anti-BARS antibody, BFA potently disassembled the Golgi. However, in cytosol complemented with purified BARS, or even in control cytosols containing physiological levels of BARS, the action of BFA on Golgi disassembly was strongly inhibited. These results suggest that BARS exerts a negative control on Golgi tubulation, with important consequences for the structure and function of the Golgi complex

Two-step MAPbI3 deposition by low-vacuum proximity-space-effusion for high-efficiency inverted semitransparent perovskite solar cells

The innovative two-step Low Vacuum-Proximity Space Effusion (LV-PSE) method exploits the conversion of a textured PbI2 layer into MAPbI3 by adsorption–incorporation–migration of energetic MAI molecules, thus enabling a best efficiency of 17.5% in 150 nm thick layers

Combination of peripheral neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio is predictive of pathological complete response after neoadjuvant chemotherapy in breast cancer patients

The immune system seems to play a fundamental role in breast cancer responsiveness to chemotherapy. We investigated two peripheral indicators of immunity/inflammation, i.e. neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR), in order to reveal a possible relationship with pathological complete response (pCR) in patients with early or locally advanced breast cancer treated with neoadjuvant chemotherapy (NACT). We retrospectively analyzed 373 consecutive patients affected by breast cancer and candidates to NACT. The complete blood cell count before starting NACT was evaluated to calculate NLR and PLR. ROC curve analysis determined threshold values of 2.42 and 104.47 as best cut-off values for NLR and PLR, respectively. The relationships between NLR/PLR and pCR, along with other clinical-pathological characteristics, were evaluated by Pearson's χ 2 or Fisher's exact test as appropriate. Univariate and multivariate analyses were performed using a logistic regression model. NLR and PLR were not significantly associated with pCR if analyzed separately. However, when combining NLR and PLR, patients with a NLRlow/PLRlow profile achieved a significantly higher rate of pCR compared to those with NLRhigh and/or PLRhigh (OR 2.29, 95% CI 1.22-4.27, p 0.009). Importantly, the predictive value of NLRlow/PLRlow was independent from common prognostic factors such as grading, Ki67, and molecular subtypes. The combination of NLR and PLR may reflect patients' immunogenic phenotype. Low levels of both NLR and PLR may thus indicate a status of immune system activation that may predict pCR in breast cancer patients treated with NACT

Phylogenetic conservation of Trop-2 across species—rodent and primate genomics model anti-Trop-2 therapy for pre-clinical benchmarks

A phylogenetic conservation analysis of Trop-2 across vertebrate species showed a high degree of sequence conservation, permitting to explore multiple models as pre-clinical benchmarks. Sequence divergence and incomplete conservation of expression patterns were observed in mouse and rat. Primate Trop-2 sequences were found to be 95%–100% identical to the human sequence. Comparative three-dimension primate Trop-2 structures were obtained with AlphaFold and homology modeling. This revealed high structure conservation of Trop-2 (0.66 ProMod3 GMQE, 0.80–0.86 ± 0.05 QMEANDisCo scores), with conservative amino acid changes at variant sites. Primate TACSTD2/TROP2 cDNAs were cloned and transfectants for individual ORF were shown to be efficiently recognized by humanized anti-Trop-2 monoclonal antibodies (Hu2G10, Hu2EF). Immunohistochemistry analysis of Macaca mulatta (rhesus monkey) tissues showed Trop-2 expression patterns that closely followed those in human tissues. This led us to test Trop-2 targeting in vivo in Macaca fascicularis (cynomolgus monkey). Intravenously injected Hu2G10 and Hu2EF were well tolerated from 5 to 10 mg/kg. Neither neurological, respiratory, digestive, urinary symptoms, nor biochemical or hematological toxicities were detected during 28-day observation. Blood serum pharmacokinetic (PK) studies were conducted utilizing anti-idiotypic antibodies in capture-ELISA assays. Hu2G10 (t1/2 = 6.5 days) and Hu2EF (t1/2 = 5.5 days) were stable in plasma, and were detectable in the circulation up to 3 weeks after the infusion. These findings validate primates as reliable models for Hu2G10 and Hu2EF toxicity and PK, and support the use of these antibodies as next-generation anti-Trop-2 immunotherapy tools

A retrospective multicentric observational study of trastuzumab emtansine in HER2 positive metastatic breast cancer: A real-world experience

We addressed trastuzumab emtansine (T-DM1) efficacy in HER2+ metastatic breast cancer patients treated in real-world practice, and its activity in pertuzumab-pretreated patients. We conducted a retrospective, observational study involving 23 cancer centres, and 250 patients. Survival data were analyzed by Kaplan Meier curves and log rank test. Factors testing significant in univariate analysis were tested in multivariate models. Median follow-up was 15 months and median T-DM1 treatment-length 4 months. Response rate was 41.6%, clinical benefit 60.9%. Median progression-free and median overall survival were 6 and 20 months, respectively. Overall, no differences emerged by pertuzumab pretreatment, with median progression-free and median overall survival of 4 and 17 months in pertuzumab-pretreated (p=0.13), and 6 and 22 months in pertuzumab-na\uc3\uafve patients (p=0.27). Patients who received second-line T-DM1 had median progression-free and median overall survival of 3 and 12 months (p=0.0001) if pertuzumab-pretreated, and 8 and 26 months if pertuzumab-na\uc3\uafve (p=0.06). In contrast, in third-line and beyond, median progression-free and median overall survival were 16 and 18 months in pertuzumab-pretreated (p=0.05) and 6 and 17 months in pertuzumab-na\uc3\uafve patients (p=0.30). In multivariate analysis, lower ECOG performance status was associated with progression-free survival benefit (p < 0.0001), while overall survival was positively affected by lower ECOG PS (p < 0.0001), absence of brain metastases (p 0.05), and clinical benefit (p < 0.0001). Our results are comparable with those from randomized trials. Further studies are warranted to confirm and interpret our data on apparently lower T-DM1 efficacy when given as second-line treatment after pertuzumab, and on the optimal sequence order

RLVNA: a Platform for Experimenting with Virtual Networks Adaptations over Public Testbeds

Network emulators and simulation environments traditionally support computer networking and distributed system research. The continued use of multiple approaches highlights both the value and inadequacy of each approach. To this end, several large-scale virtual networks testbeds, such as GENI and CloudLab, have emerged, allowing testing of a networked system in controlled yet realistic environments, focusing in particular on facilitating the test of network management schema in Software-Defined Network (SDN) scenarios. Nevertheless, setting up those experiments first and integrating machine learning models later in these deployments is challenging. In this paper, we propose designing and implementing a web-based platform that integrates Reinforcement Learning (RL)-based models with a virtual network experiment using resources acquired within a real-world testbed, e.g., GENI. Users are able to reserve the network resources (links, switches, and hosts) and configure them through our intuitive interface with little effort. The RL algorithm is then launched to learn how to steer traffic dynamically and according to diverse traffic network conditions. Such a model can be easily customized by the user, while our architecture enables fast reprogramming of the Open Virtual Switches via the SDN controller instantiated. We experimented with trace-based traffic to validate this user-friendly platform and evaluated how centralized and decentralized RL algorithms can effectively lead to self-driving networks. While in this paper, the system focuses on the deployment of experiments for virtual network adaptation, the platform can be easily extended to other network management mechanisms and machine learning algorithms