Citations

Abstract Medulloblastoma and central nervous system primitive neuroectodermal tumors (CNS-PNET) are aggressive, poorly differentiated brain tumors with limited effective therapies. Using Sleeping Beauty (SB) transposon mutagenesis, we identified novel genetic drivers of medulloblastoma and CNS-PNET. Cross-species gene expression analyses classified SB-driven tumors into distinct medulloblastoma and CNS-PNET subgroups, indicating they resemble human Sonic hedgehog and group 3 and 4 medulloblastoma and CNS neuroblastoma with FOXR2 activation. This represents the first genetically induced mouse model of CNS-PNET and a rare model of group 3 and 4 medulloblastoma. We identified several putative proto-oncogenes including Arhgap36, Megf10, and Foxr2. Genetic manipulation of these genes demonstrated a robust impact on tumorigenesis in vitro and in vivo. We also determined that FOXR2 interacts with N-MYC, increases C-MYC protein stability, and activates FAK/SRC signaling. Altogether, our study identified several promising therapeutic targets in medulloblastoma and CNS-PNET. Significance: A transposon-induced mouse model identifies several novel genetic drivers and potential therapeutic targets in medulloblastoma and CNS-PNET.

Abstract Background Current American College of Gastroenterology (ACG) guidelines suggest that Helicobacter pylori serologic testing may be helpful to rule out H. pylori in areas with low disease prevalence. However, even in low prevalence regions, the positive predictive value of a positive serologic result remains low. Additionally, both the ACG and the American Gastroenterological Association recommend that this noninvasive testing be avoided entirely for patients 55 years and older. Methods The objective of this multicenter retrospective study was to assess H. pylori seroprevalence rates among symptomatic patients and serologic test utilization at the local, state, and national level. Submitted specimens were presumed to be collected from patients with upper gastrointestinal symptoms suspicious for an H. pylori etiology. Results for H. pylori IgG serologic tests performed between 2005 and 2014 were collected from 5 medical centers. Data were evaluated in the context of professional practice guidelines, with a focus on patients ages 55 years and older. Results Nationwide seropositivity among symptomatic individuals is approximately 25%, and 4 of 5 centers reported decreasing seropositivity rates over this 10-year period. State-specific seropositivity among symptomatic patients ranged from 12.4% in the state of Washington to 33.9% in Mississippi. Conclusions We demonstrate that for 48 states >25% of all H. pylori serologic testing was performed in individuals ≥55 years. Despite recommendations to avoid serologic evaluation for H. pylori infection, this study indicates that serology continues to be used at high rates across age-groups.

A key question asked when faced with an infection, an infestation, or environmental damage is whether it is a fungus. Identification of fungi as the cause of the problem can lead to remediation or treatment. Zygomycetes and ascomycetes account for the vast majority of fungal causes of human, animal, and plant disease, large-scale biodiversity loss, agricultural spoilage, and contamination of water-damaged buildings. These studies revealed the conservation of a common cell wall structural component of zygomycetes and ascomycetes to be a diagnostic target applicable to multiple pathogenic fungi and have leveraged that insight for practical use. Monoclonal antibodies reactive with this pan-fungal structure were produced and used to construct immunoassays (including ELISA and lateral flow assay) for detection of a broad range of pathogenic fungi.

Abstract TNF-related apoptosis-inducing ligand (TRAIL) was initially described to induce apoptosis of tumor cells and/or virally infected cells, although sparing normal cells, and has been implicated in the pathogenesis of HIV disease. We previously identified TRAILshort, a TRAIL splice variant, in HIV-infected patients and characterized it as being a dominant negative ligand to subvert TRAIL-mediated killing. Herein, using single-cell genomics we demonstrate that TRAILshort is produced by HIV-infected cells, as well as by uninfected bystander cells, and that the dominant stimulus which induces TRAILshort production are type I IFNs and TLR7, TLR8, and TLR9 agonists. TRAILshort has a short t1/2 by virtue of containing a PEST domain, which targets the protein toward the ubiquitin proteasome pathway for degradation. Further we show that TRAILshort binds preferentially to TRAIL receptors 1 and 2 with significantly reduced interaction with the decoy TRAIL receptors 3 and 4. Recombinant TRAILshort is sufficient to protect cells against TRAIL-induced killing, whereas immunodepletion of TRAILshort with a specific Ab restores TRAIL sensitivity. Importantly we show that TRAILshort is shed in microvesicles into the cellular microenvironment and therefore confers TRAIL resistance not only on the cell which produces it, but also upon neighboring bystander cells. These results establish a novel paradigm for understanding and overcoming TRAIL resistance, in particular how HIV-infected cells escape immune elimination by the TRAIL:TRAILshort receptor axis.

Abstract Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.