Frontiers in Molecular Biosciences (Jun 2022)
Inconsistencies in Modeling the Efficacy of the Oncolytic Virus HSV1716 Reveal Potential Predictive Biomarkers for Tolerability
Abstract
Treatment with HSV1716 via intralesional administration has proven successful for melanoma patients with the hope that oncolytic virotherapy would become another weapon in the systemic anticancer therapy (SACT) arsenal. In addition to challenges surrounding the systemic delivery of oncolytic viruses (OVs), problems associated with its in vivo modeling have resulted in low predictive power, contributing to the observed disappointing clinical efficacy. As OV’s efficacy is elicited through interaction with the immune system, syngeneic orthotopic mouse models offer the opportunity to study these with high reproducibility and at a lower cost; however, inbred animals display specific immune characteristics which may confound results. The systemic delivery of HSV1716 was, therefore, assessed in multiple murine models of breast cancer. Tolerability to the virus was strain-dependent with C57/Bl6, the most tolerant and Balb/c experiencing lethal side effects, when delivered intravenously. Maximum tolerated doses were not enough to demonstrate efficacy against tumor growth rates or survival of Balb/c and FVB mouse models; therefore; the most susceptible strain (Balb/c mice) was treated with immunomodulators prior to virus administration in an attempt to reduce side effects. These studies demonstrate the number of variables to consider when modeling the efficacy of OVs and the complexities involved in their interpretation for translational purposes. By reporting these observations, we have potentially revealed a role for T-cell helper polarization in viral tolerability. Importantly, these findings were translated to human studies, whereby a Th1 cytokine profile was expressed in pleural effusions of patients that responded to HSV1716 treatment for malignant pleural mesothelioma with minimal side effects, warranting further investigation as a biomarker for predictive response.
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