Nitric oxide suppression by secreted frizzled-related protein 2 drives retinoblastoma.

Retinoblastoma is a cancer of the infant retina primarily driven by loss of the Rb tumor suppressor gene, which is undruggable. Here, we report an autocrine signaling, mediated by secreted frizzled-related protein 2 (SFRP2), which suppresses nitric oxide and enables retinoblastoma growth. We show that coxsackievirus and adenovirus receptor (CXADR) is the cell-surface receptor for SFRP2 in retinoblastoma cells; that CXADR functions as a "dependence receptor," transmitting a growth-inhibitory signal in the absence of SFRP2; and that the balance between SFRP2 and CXADR determines nitric oxide production. Accordingly, high SFRP2 RNA expression correlates with high-risk histopathologic features in retinoblastoma. Targeting SFRP2 signaling by SFRP2-binding peptides or by a pharmacological inhibitor rapidly induces nitric oxide and profoundly inhibits retinoblastoma growth in orthotopic xenograft models. These results reveal a cytokine signaling pathway that regulates nitric oxide production and retinoblastoma cell proliferation and is amenable to therapeutic intervention.

NELL2-cdc42 signaling regulates BAF complexes and Ewing sarcoma cell growth.

BAF chromatin remodeling complexes play important roles in chromatin regulation and cancer. Here, we report that Ewing sarcoma cells are dependent on the autocrine signaling mediated by NELL2, a secreted glycoprotein that has been characterized as an axon guidance molecule. NELL2 uses Robo3 as the receptor to transmit critical growth signaling. NELL2 signaling inhibits cdc42 and upregulates BAF complexes and EWS-FLI1 transcriptional output. We demonstrate that cdc42 is a negative regulator of BAF complexes, inducing actin polymerization and complex disassembly. Furthermore, we identify NELL2highCD133highEWS-FLI1high and NELL2lowCD133lowEWS-FLI1low populations in Ewing sarcoma, which display phenotypes consistent with high and low NELL2 signaling, respectively. We show that NELL2, CD133, and EWS-FLI1 positively regulate each other and upregulate BAF complexes and cell proliferation in Ewing sarcoma. These results reveal a signaling pathway regulating critical chromatin remodeling complexes and cancer cell proliferation.

GDF6-CD99 Signaling Regulates Src and Ewing Sarcoma Growth.

We report here that the autocrine signaling mediated by growth and differentiation factor 6 (GDF6), a member of the bone morphogenetic protein (BMP) family of cytokines, maintains Ewing sarcoma growth by preventing Src hyperactivation. Surprisingly, Ewing sarcoma depends on the prodomain, not the BMP domain, of GDF6. We demonstrate that the GDF6 prodomain is a ligand for CD99, a transmembrane protein that has been widely used as a marker of Ewing sarcoma. The binding of the GDF6 prodomain to the CD99 extracellular domain results in recruitment of CSK (C-terminal Src kinase) to the YQKKK motif in the intracellular domain of CD99, inhibiting Src activity. GDF6 silencing causes hyperactivation of Src and p21-dependent growth arrest. We demonstrate that two GDF6 prodomain mutants linked to Klippel-Feil syndrome are hyperactive in CD99-Src signaling. These results reveal a cytokine signaling pathway that regulates the CSK-Src axis and cancer cell proliferation and suggest the gain-of-function activity for disease-causing GDF6 mutants.

FGF19 functions as autocrine growth factor for hepatoblastoma.

Hepatoblastoma is the most common liver cancer in children, accounting for over 65% of all childhood liver malignancies. Hepatoblastoma is distinct from adult liver cancer in that it is not associated with hepatitis virus infection, cirrhosis, or other underlying liver pathology. The paucity of appropriate cell and animal models has been hampering the mechanistic understanding of hepatoblastoma pathogenesis. Consequently, there is no molecularly targeted therapy for hepatoblastoma. To gain insight into cytokine signaling in hepatoblastoma, we employed mass spectrometry to analyze the proteins secreted from Hep293TT hepatoblastoma cell line we established and identified the specific secretion of fibroblast growth factor 19 (FGF19), a growth factor for liver cells. We determined that silencing FGF19 by shRNAs or neutralizing secreted FGF19 by anti-FGF19 antibody inhibits the proliferation of hepatoblastoma cells. Furthermore, blocking FGF19 signaling by an FGF receptor kinase inhibitor suppressed hepatoblastoma growth. RNA expression analysis in hepatoblastoma tumors revealed that the high expression of FGF19 signaling pathway components as well as the low expression of FGF19 signaling repression targets correlates with the aggressiveness of the tumors. These results suggest the role of FGF19 as autocrine growth factor for hepatoblastoma.

MicroCT-based virtual histology evaluation of preclinical medulloblastoma.

PURPOSE: The purpose of this paper is to validate a rapid and cost-effective ex vivo technique, microCT-based virtual histology, as an alternative to MRI imaging for assessing the therapeutic response in genetically engineered mouse models of cancer. PROCEDURES: All animal procedures were conducted in accordance with the Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Texas Health Science Center at San Antonio. MRI imaging was performed on 6-week-old, bortezomib-treated genetically engineered Patched1, p53 mice that recapitulate the characteristics of human medulloblastoma. After MRI scans, the same mice were euthanized to collect brain or spine samples for virtual histology staining followed by microCT scanning. RESULTS: Nine-micrometer resolution ex vivo micro X-ray computed tomography (microCT)-based virtual histology images were qualitatively reflective of high-field live animal images obtained with magnetic resonance imaging (MRI) and histopathology. Cerebellar volumes on microCT-based virtual histology correlated closely with MRI cerebellar volumes (R = 0.998). MRI and microCT-based virtual histology both indicated a significant difference between cerebellar volumes of untreated and treated mice (p = 0.02 and p = 0.04, respectively). The ex vivo microCT method also allowed a 7,430-fold improvement in voxel resolution (voxel volume of 729 µm³ for 9-µm isometric resolution microCT vs. 5,416,800 µm³ for 400 × 111 × 122 µm resolution MRI) at a 28% cost savings ($400 vs. $555 per animal). CONCLUSION: The ex vivo, en bloc technique of microCT-based virtual histology matched MRI in reflecting histopathology. MicroCT-based virtual histology proved to be a more cost-effective technique and less labor-intensive. On the other hand, MRI provides ability to perform in vivo imaging, faster scanning and lower radiation dose by sacrificing the spatial resolution. Thus, both in vivo MRI and ex vivo microCT-based virtual histology are effective means of quantitatively evaluating therapeutic response in preclinical models of cerebellar tumors including the childhood cancer, medulloblastoma.

Building and analyzing protein interactome networks by cross-species comparisons.

BACKGROUND: A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species. RESULTS: The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen) and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced. CONCLUSIONS: Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website http://www.interologfinder.org provides research biologists intuitive access to this data.