Neuropilin-2 promotes branching morphogenesis in the mouse mammary gland

Although the neuropilins were characterized as semaphorin receptors that regulate axon guidance, they also function as vascular endothelial growth factor (VEGF) receptors and contribute to the development of other tissues. Here, we assessed the role of NRP2 in mouse mammary gland development based on our observation that NRP2 is expressed preferentially in the terminal end buds of developing glands. A floxed NRP2 mouse was bred with an MMTV-Cre strain to generate a mammary gland-specific knockout of NRP2. MMTV-Cre;NRP2(loxP/loxP) mice exhibited significant defects in branching morphogenesis and ductal outgrowth compared with either littermate MMTV-Cre;NRP2(+/loxP) or MMTV-Cre mice. Mechanistic insight into this morphological defect was obtained from a mouse mammary cell line in which we observed that VEGF(165), an NRP2 ligand, induces branching morphogenesis in 3D cultures and that branching is dependent upon NRP2 as shown using shRNAs and a function-blocking antibody. Epithelial cells in the mouse mammary gland express VEGF, supporting the hypothesis that this NRP2 ligand contributes to mammary gland morphogenesis. Importantly, we demonstrate that VEGF and NRP2 activate focal adhesion kinase (FAK) and promote FAK-dependent branching morphogenesis in vitro. The significance of this mechanism is substantiated by our finding that FAK activation is diminished significantly in developing MMTV-Cre;NRP2(loxP/loxP) mammary glands compared with control glands. Together, our data reveal a VEGF/NRP2/FAK signaling axis that is important for branching morphogenesis and mammary gland development. In a broader context, our data support an emerging hypothesis that directional outgrowth and branching morphogenesis in a variety of tissues are influenced by signals that were identified initially for their role in axon guidance

Regulation of p53 by Mdm4 and ovarian hormones in mouse mammary glands

p53 protein is considered a major player in maintaining the genomic integrity. It regulates cell cycle, recognizes damaged DNA, and promotes apoptosis of cells that are defective or developmentally programmed for removal. Most cancers, including breast cancer, lose genomic integrity and have impaired p53. Li-Fraumeni patients and BALB/c mice bearing germline mutations in one allele of the p53 tumor suppressor gene develop mammary tumors. The p53 pathway is also required for the protective effect of pregnancy on breast cancer. Therefore, identifying the cellular pathways that regulate p53 will provide improvements in assessing breast cancer risk in individuals as well as therapeutic targets. The function of p53 could be regulated directly by Mdm2 and Mdm4. While Mdm2 is clearly a negative regulator of p53, the roles of Mdm4 on p53 are still not fully defined. Transgenic mice which over-express Mdm4 specifically in mammary gland were used to investigate the effects of Mdm4 on p53 function. Ovarian hormones also regulate p53 activity, but through indirect mechanisms. Oligonucleotide-based transcriptional profiling was conducted to identify mechanisms, by which estrogen and progesterone enhance p53 activity in mammary epithelial cells. The results from these studies showed that Mdm4 is unlikely to be a primary regulator of p53 functions in mammary epithelium. In contrast, expression profiling revealed groups of genes that are associated with the sensitization of p53 by estrogen and progesterone as well as tamoxifen and progesterone. These results implicate a common pathway used to sensitize p53 which involves proteins in the extracellular matrix

The Effects of Diet on the Immune Responses of the Oriental Armyworm <i>Mythimna separata</i>

Nutrients can greatly affect host immune defenses against infection. Possessing a simple immune system, insects have been widely used as models to address the relationships between nutrition and immunity. The effects of high versus low protein-to-carbohydrate ratio (P:C) diets on insect immune responses vary in different studies. To reveal the dietary manipulation of immune responses in the polyphagous agricultural pest oriental armyworm, we examined immune gene expression, phenoloxidase (PO) activity, and phagocytosis to investigate the immune traits of bacteria-challenged oriental armyworms, which were fed different P:C ratio diets. We found the oriental armyworms that were fed a 35:7 (P:C) diet showed higher phenoloxidase (PO) activity and stronger melanization, and those reared on a 28:14 (P:C) diet showed higher antimicrobial activity. However, different P:C diets had no apparent effect on the hemocyte number and phagocytosis. These results overall indicate that high P:C diets differently optimize humoral immune defense responses in oriental armyworms, i.e., PO-mediated melanization and antimicrobial peptide synthesis in response to bacteria challenge

Analysis of integrin beta4 expression in human breast cancer: association with basal-like tumors and prognostic significance

PURPOSE: The beta4 integrin has been implicated in functions associated with the genesis and progression of carcinomas based on data obtained from cell lines and mouse models. Data on its expression and relevance to human carcinomas, however, are relatively scant. The aim of this study was to assess its expression and prognostic significance in human breast carcinomas. EXPERIMENTAL DESIGN: We integrated data on beta4 expression from multiple gene profiling studies of breast tumors of known clinical outcome with immunohistochemical analysis of 105 breast carcinomas, and we identified genes whose expression correlates with that of beta4. RESULTS: The expression of both beta4 mRNA and protein is not homogeneous in breast cancer and it associates most significantly with the basal-like subtype of breast tumors (P = 0.008). No association between beta4 and HER2 expression was evident from either gene profiling or immunohistochemical analysis. To gain insight into the relevance of beta4 expression to human breast carcinomas, we generated a 65-gene beta4 signature based on integration of four published gene profiling studies that included the top 0.1% of genes that correlated with beta4, either positively or negatively. This beta4 signature predicted decreased time to tumor recurrence and survival of patients when applied to four data sets including two independent ones. CONCLUSIONS: These observations indicate that beta4 expression in human breast cancer is restricted and associated with basal-like cancers, and they support the hypothesis that beta4 may function in concert with a discrete set of proteins to facilitate the aggressive behavior of a subset of tumors

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non–Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study

BackgroundLymph node metastasis (LNM) is critical for treatment decision making of patients with resectable non–small cell lung cancer, but it is difficult to precisely diagnose preoperatively. Electronic medical records (EMRs) contain a large volume of valuable information about LNM, but some key information is recorded in free text, which hinders its secondary use. ObjectiveThis study aims to develop LNM prediction models based on EMRs using natural language processing (NLP) and machine learning algorithms. MethodsWe developed a multiturn question answering NLP model to extract features about the primary tumor and lymph nodes from computed tomography (CT) reports. We then combined these features with other structured clinical characteristics to develop LNM prediction models using machine learning algorithms. We conducted extensive experiments to explore the effectiveness of the predictive models and compared them with size criteria based on CT image findings (the maximum short axis diameter of lymph node >10 mm was regarded as a metastatic node) and clinician’s evaluation. Since the NLP model may extract features with mistakes, we also calculated the concordance correlation between the predicted probabilities of models using NLP-extracted features and gold standard features to explore the influence of NLP-driven automatic extraction. ResultsExperimental results show that the random forest models achieved the best performances with 0.792 area under the receiver operating characteristic curve (AUC) value and 0.456 average precision (AP) value for pN2 LNM prediction and 0.768 AUC value and 0.524 AP value for pN1&N2 LNM prediction. And all machine learning models outperformed the size criteria and clinician’s evaluation. The concordance correlation between the random forest models using NLP-extracted features and gold standard features is 0.950 and improved to 0.984 when the top 5 important NLP-extracted features were replaced with gold standard features. ConclusionsThe LNM models developed can achieve competitive performance using only limited EMR data such as CT reports and tumor markers in comparison with the clinician’s evaluation. The multiturn question answering NLP model can extract features effectively to support the development of LNM prediction models, which may facilitate the clinical application of predictive models