Proliferation and Survival Mechanisms in Soft Tissue Sarcoma and Glioblastoma Tumors

Soft tissue sarcomas and glioblastomas are two deadly tumors that are characterized by aggressive overproliferation, and regions of severe intratumoral nutrient and oxygen deprivation. The mechanisms by which tumors evade proliferation control signals and survive in a hostile microenvironment are active areas of investigation. This work describes two projects investigating loss of proliferation control in soft tissue sarcoma, as a result of Hippo pathway deregulation, and mechanisms of survival under stress in glioblastoma, as a result of decreased microRNA-124 (miR-124) levels. First, we demonstrate that the Hippo pathway is deregulated in soft tissue sarcoma patient samples, leading to overexpression of the Hippo effector YAP. YAP, a transcriptional coactivator, binds to TEAD proteins in the nucleus and controls the transcription of multiple pro-proliferation and anti-apoptosis targets, including the transcription factor FOXM1. Interestingly, we show that FOXM1 physically interacts with the TEAD/YAP complex, creating a powerful pro-proliferation complex. FOXM1 genetic deletion and pharmacologic inhibition resulted in decreased sarcoma tumor size, suggesting that FOXM1 inhibition is a viable potential sarcoma treatment. Second, we show that ectopically expressing miR-124 in glioblastoma cells leads to increased cell death. We identify three factors (SERP1, TEAD1, and MAPK14) as direct miR-124 targets and partial effectors of cell survival under stress. Inhibition of these targets recapitulates the miR-124 cell death phenotype under stress, and decreased glioma growth in vivo. Importantly, miR-124 ectopic expression results in increased survival in an in vivo orthotopic intracranial glioma model, suggesting that expression of miR-124, or inhibition of its downstream targets, is an attractive way of targeting glioblastoma cells residing in hypoxic/ischemic regions, and ultimately a method of investigating novel glioblastoma treatments

Ten rules for conducting retrospective pharmacoepidemiological analyses: example COVID-19 study

Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized controlled trial generally provides the most credible evaluation of a treatment, but the efficiency and effectiveness of the trial depend on the existing evidence supporting the treatment. The researcher must therefore compile a body of evidence justifying the use of time and resources to further investigate a treatment hypothesis in a trial. An observational study can provide this evidence, but the lack of randomized exposure and the researcher’s inability to control treatment administration and data collection introduce significant challenges. A proper analysis of observational health care data thus requires contributions from experts in a diverse set of topics ranging from epidemiology and causal analysis to relevant medical specialties and data sources. Here we summarize these contributions as 10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study. A detailed supplement presents a practical how-to guide for following each rule. When carefully designed and properly executed, a retrospective pharmacoepidemiological analysis framed around these rules will inform the decisions of whether and how to investigate a treatment hypothesis in a randomized controlled trial. This work has important implications for any future pandemic by prescribing what we can and should do while the world waits for global vaccine distribution