Triple Negative Breast Cancer Risk Genes Identified by Multigene Hereditary Cancer

In a constant battle to find more efficient and effective ways to test oncology preclinical candidates, 3D in vitro culture methods for PDX-derived tumor cells have shown promise of in vivo-like growth characteristics, invasion, and responses to therapeutics. With these methods, we can perform high-throughput in vitro PDX screening that identifies active and selective molecules, and select favorable PDX tumor models for subsequent validation of candidates in vivo.

This field spans through most scientific disciplines, studying cell imaging, stem cells, cell physiology and structure, cell signaling, cell culture, and translational biology, and encompassing other scientific fields such as cancer biology, neuroscience, genetics, bioengineering and so much more. The free, two-day conference will bring together professors, post-docs, biologists and biomedical professionals from all over the world.

Tumor heterogeneity is a major hurdle for cancer treatment success. To effectively enroll patients in clinical trials, the identification of predictive biomarkers at the preclinical stage is crucial. The use of patient-derived xenograft (PDX) tumor models facilitates the identification of biomarkers that predict the tumor’s response to therapy. In addition, the assessment of PDX models on broad screening platforms such as 3D culture and in vivo mouse clinical trials (humanized and immunodeficient) accelerates compound evaluation. This webinar discusses the use of PDX models in preclinical drug development and the benefits of using this approach to clinical trial outcomes. Click below to view the slides of the presentation.

How nuclear factor-erythroid factor 2-related factor 2 (Nrf2) affects the redox status of cells, The effects of cinnamic aldehyde treatment on Nrf2, Evidence for a Nrf2-dependent mechanism of neointimal hyperplasia inhibition by cinnamic aldehyde treatment and Benefits of 3D analysis of arterial injury over traditional 2D histology analysis