Magic™ Anti-PI 3 Kinase p85 beta (Phospho Y464) polyclonal antibody

Acquired resistance to the anti-estrogen tamoxifen remains a significant challenge in breast cancer management. In this study, we used an integrative approach to characterize global protein expression and tyrosine phosphorylation events in tamoxifen-resistant MCF7 breast cancer cells (TamR) compared with parental controls. Quantitative mass spectrometry and computational approaches were combined to identify perturbed signalling networks, and candidate regulatory proteins were functionally interrogated by siRN-Amediated knockdown. Network analysis revealed that cellular metabolism was perturbed in TamR cells, together with pathways enriched for proteins associated with growth factor, cell-cell and cell matrix-initiated signalling. Consistent with known roles for Ras/MAPK and PI3-kinase signalling in tamoxifen resistance, tyrosine-phosphorylated MAPK1, SHC1 and PIK3R2 were elevated in TamR cells. Phosphorylation of the tyrosine kinase Yes and expression of the actin-binding protein myristoylated alanine-rich C-kinase substrate (MARCKS) were increased two-and eightfold in TamR cells respectively, and these proteins were selected for further analysis. Knockdown of either protein in TamR cells had no effect on anti-estrogen sensitivity, but significantly decreased cell motility. MARCKS expression was significantly higher in breast cancer cell lines than normal mammary epithelial cells and in ER-negative versus ER-positive breast cancer cell lines. In primary breast cancers, cytoplasmic MARCKS staining was significantly higher in basal-like and HER2 cancers than in luminal cancers, and was independently predictive of poor survival in multivariate analyses of the whole cohort (P < 0.0001) and in ER-positive patients (P = 0.0005). These findings provide network-level insights into the molecular alterations associated with the tamoxifen-resistant phenotype, and identify MARCKS as a potential biomarker of therapeutic responsiveness that may assist in stratification of patients for optimal therapy.

Background: New emergence of immunotherapy has significantly improved clinical outcome of melanoma patients with advanced and metastatic diseases. We aimed to develop a gene signature based on the expression of PD-1/PD-L1 signaling pathway genes to predict prognosis and immunotherapy response in melanoma patients. Methods: Melanoma samples from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) were used as the training set and external validation sets respectively. Prognostic genes for overall survival (OS) were identified by univariate Cox regression analysis. Then a multi-gene risk signature was established with the Least Absolute Shrinkage and Selector Operation (LASSO) regression and multivariate Cox regression. The predictive and prognostic value of gene signature was evaluated by Kaplan Meier curve, Timedependent receiver operating characteristic (ROC) curve, and area under curve (AUC). Gene set enrichment analysis (GSEA) was performed to investigate the discrepantly enriched biological processes between low-risk and high-risk group of melanoma patients. Results: A seven-gene risk signature (BATF2, CTLA4, EGFR, HLA-DQB1, IKBKG, PIK3R2, PPP3CA) was constructed. The signature was an independent risk factor for OS (hazard ratio = 1.544, p < 0.001) and it could robustly predict OS in both training and validation sets. Besides, high risk scores indicated advanced clinical stage and no response to immunotherapy for melanoma patients. GSEA demonstrated that high risk score was intimately associated with immune response and immune regulation. In conclusion, the novel seven-gene signature could serve as a robust biomarker for prognosis and a potential indicator of immunotherapy response in melanoma.