Dataset Information

Improving antitumor efficacy via combinatorial regimens of oncolytic virotherapy.

ABSTRACT: As a promising therapeutic strategy, oncolytic virotherapy has shown potent anticancer efficacy in numerous pre-clinical and clinical trials. Oncolytic viruses have the capacity for conditional-replication within carcinoma cells leading to cell death via multiple mechanisms, including direct lysis of neoplasms, induction of immunogenic cell death, and elicitation of innate and adaptive immunity. In addition, these viruses can be engineered to express cytokines or chemokines to alter tumor microenvironments. Combination of oncolytic virotherapy with other antitumor therapeutic modalities, such as chemotherapy and radiation therapy as well as cancer immunotherapy can be used to target a wider range of tumors and promote therapeutic efficacy. In this review, we outline the basic biological characteristics of oncolytic viruses and the underlying mechanisms that support their use as promising antitumor drugs. We also describe the enhanced efficacy attributed to virotherapy combined with other drugs for the treatment of cancer.

SUBMITTER: Zhang B

PROVIDER: S-EPMC7656670 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving antitumor efficacy via combinatorial regimens of oncolytic virotherapy.

Zhang Bin B Cheng Ping P

Molecular cancer 20201110 1

As a promising therapeutic strategy, oncolytic virotherapy has shown potent anticancer efficacy in numerous pre-clinical and clinical trials. Oncolytic viruses have the capacity for conditional-replication within carcinoma cells leading to cell death via multiple mechanisms, including direct lysis of neoplasms, induction of immunogenic cell death, and elicitation of innate and adaptive immunity. In addition, these viruses can be engineered to express cytokines or chemokines to alter tumor microe ...[more]

PMID: 33172438

Similar Datasets

Project description:Background:Attenuated measles virus (MV) strains are promising agents currently being tested against solid tumors or hematologic malignancies in ongoing phase I and II clinical trials; factors determining oncolytic virotherapy success remain poorly understood, however. Methods:We performed RNA sequencing and gene set enrichment analysis to identify pathways differentially activated in MV-resistant (n = 3) and -permissive (n = 2) tumors derived from resected human glioblastoma (GBM) specimens and propagated as xenografts (PDX). Using a unique gene signature we identified, we generated a diagonal linear discriminant analysis (DLDA) classification algorithm to predict MV responders and nonresponders, which was validated in additional randomly selected GBM and ovarian cancer PDX and 10 GBM patients treated with MV in a phase I trial. GBM PDX lines were also treated with the US Food and Drug Administration-approved JAK inhibitor, ruxolitinib, for 48 hours prior to MV infection and virus production, STAT1/3 signaling and interferon stimulated gene expression was assessed. All statistical tests were two-sided. Results:Constitutive interferon pathway activation, as reflected in the DLDA algorithm, was identified as the key determinant for MV replication, independent of virus receptor expression, in MV-permissive and -resistant GBM PDXs. Using these lines as the training data for the DLDA algorithm, we confirmed the accuracy of our algorithm in predicting MV response in randomly selected GBM PDX ovarian cancer PDXs. Using the DLDA prediction algorithm, we demonstrate that virus replication in patient tumors is inversely correlated with expression of this resistance gene signature (? = -0.717, P = .03). In vitro inhibition of the interferon response pathway with the JAK inhibitor ruxolitinib was able to overcome resistance and increase virus production (1000-fold, P = .03) in GBM PDX lines. Conclusions:These findings document a key mechanism of tumor resistance to oncolytic MV therapy and describe for the first time the development of a prediction algorithm to preselect for oncolytic treatment or combinatorial strategies.

Dataset Information

Improving antitumor efficacy via combinatorial regimens of oncolytic virotherapy.

Publications

Improving antitumor efficacy via combinatorial regimens of oncolytic virotherapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure