Multi-modal efficacy of a chimeric vesiculovirus expressing the Morreton glycoprotein in sarcoma.

Vesiculoviruses are attractive oncolytic virus platforms due to their rapid replication, appreciable transgene capacity, broad tropism, limited preexisting immunity, and tumor selectivity through type I interferon response defects in malignant cells. We developed a synthetic chimeric virus (VMG) expressing the glycoprotein (G) from Morreton virus (MorV) and utilizing the remaining structural genes from vesicular stomatitis virus (VSV). VMG exhibited in vitro efficacy by inducing oncolysis in a broad range of sarcoma subtypes across multiple species. Notably, all cell lines tested showed the ability of VMG to yield productive infection with rapid replication kinetics and induction of apoptosis. Furthermore, pilot safety evaluations of VMG in immunocompetent, non-tumor-bearing mice showed an absence of toxicity with intranasal doses as high as 1e10 50% tissue culture infectious dose (TCID50)/kg. Locoregional administration of VMG in vivo resulted in tumor reduction in an immunodeficient Ewing sarcoma xenograft at doses as low as 2e5 TCID50. In a murine syngeneic fibrosarcoma model, while no tumor inhibition was achieved with VMG, there was a robust induction of CD8+ T cells within the tumor. The studies described herein establish the promising potential for VMG to be used as a novel oncolytic virotherapy platform with anticancer effects in sarcoma.

Characterization of Morreton virus as an oncolytic virotherapy platform for liver cancers.

BACKGROUND: Morreton virus (MORV) is an oncolytic Vesiculovirus , genetically distinct from vesicular stomatitis virus (VSV). AIM: To report that MORV induced potent cytopathic effects (CPEs) in cholangiocarcinoma (CCA) and hepatocellular carcinoma (HCC) in vitro models. APPROACH AND RESULTS: In preliminary safety analyses, high intranasal doses (up to 10 10 50% tissue culture infectious dose [TCID 50 ]) of MORV were not associated with significant adverse effects in immune competent, non-tumor-bearing mice. MORV was shown to be efficacious in a Hep3B hepatocellular cancer xenograft model but not in a CCA xenograft HuCCT1 model. In an immune competent, syngeneic murine CCA model, single intratumoral treatments with MORV (1 × 10 7 TCID 50 ) triggered a robust antitumor immune response leading to substantial tumor regression and disease control at a dose 10-fold lower than VSV (1 × 10 8 TCID 50 ). MORV led to increased CD8 + cytotoxic T cells without compensatory increases in tumor-associated macrophages and granulocytic or monocytic myeloid-derived suppressor cells. CONCLUSIONS: Our findings indicate that wild-type MORV is safe and can induce potent tumor regression via immune-mediated and immune-independent mechanisms in HCC and CCA animal models without dose limiting adverse events. These data warrant further development and clinical translation of MORV as an oncolytic virotherapy platform.

Repurposing live attenuated trivalent MMR vaccine as cost-effective cancer immunotherapy.

It has long been known that oncolytic viruses wield their therapeutic capability by priming an inflammatory state within the tumor and activating the tumor immune microenvironment, resulting in a multifaceted antitumor immune response. Vaccine-derived viruses, such as measles and mumps, have demonstrated promising potential for treating human cancer in animal models and clinical trials. However, the extensive cost of manufacturing current oncolytic viral products makes them far out of reach for most patients. Here by analyzing the impact of intratumoral (IT) administrations of the trivalent live attenuated measles, mumps, and rubella viruses (MMR) vaccine, we unveil the cellular and molecular basis of MMR-induced anti-cancer activity. Strikingly, we found that IT delivery of low doses of MMR correlates with tumor control and improved survival in murine hepatocellular cancer and colorectal cancer models via increased tumor infiltration of CD8+ granzyme B+ T-cells and decreased macrophages. Moreover, our data indicate that MMR activates key cellular effectors of the host's innate and adaptive antitumor immunity, culminating in an immunologically coordinated cancer cell death. These findings warrant further work on the potential for MMR to be repurposed as safe and cost-effective cancer immunotherapy to impact cancer patients globally.

Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer.

PURPOSE: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC). The selection of important features is a crucial step before training a classifier. METHODS: In this study, we built a model that uses support vector machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy-maximum-relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid). RESULTS: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF), Bayes net (BN), multilayer perceptron (MLP), naïve Bayes (NB), reduced error pruning tree (REPT), and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP). Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1, MMP7, and TGFB1 were predicted to be CRC biomarkers. CONCLUSION: This model could be used to further develop a diagnostic tool for predicting CRC based on gene expression data from patient samples.