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Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo
Published on: January 7, 2019
Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico
Tyler Cassidy1, Morgan Craig2,3
1Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada.
Abstract:
Oncolytic virotherapies, including the modified herpes simplex virus talimogene laherparepvec (T-VEC), have shown great promise as potent instigators of anti-tumour immune effects. The OPTiM trial, in particular, demonstrated the superior anti-cancer effects of T-VEC as compared to systemic immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging exogenous cytokine immunotherapy and oncolytic virotherapy would elicit an even greater immune response and improve patient outcomes. However, regimen scheduling of combination immunostimulation and T-VEC therapy has yet to be established. Here, we calibrate a computational biology model of sensitive and resistant tumour cells and immune interactions for implementation into an in silico clinical trial to test and individualize combination immuno- and virotherapy. By personalizing and optimizing combination oncolytic virotherapy and immunostimulatory therapy, we show improved simulated patient outcomes for individuals with late-stage melanoma. More crucially, through evaluation of individualized regimens, we identified determinants of combination GM-CSF and T-VEC therapy that can be translated into clinically-actionable dosing strategies without further personalization. Our results serve as a proof-of-concept for interdisciplinary approaches to determining combination therapy, and suggest promising avenues of investigation towards tailored combination immunotherapy/oncolytic virotherapy.
Insights
Combining oncolytic virus talimogene laherparepvec (T-VEC) with granulocyte-macrophage colony-stimulating factor (GM-CSF) shows promise for melanoma. Computational modeling optimizes treatment schedules for improved patient outcomes in late-stage melanoma.
Area of Science:
- Oncology
- Immunotherapy
- Computational Biology
Background:
- Oncolytic virotherapies like talimogene laherparepvec (T-VEC) show potential for anti-tumor immunity.
- T-VEC demonstrated superior anti-cancer effects compared to granulocyte-macrophage colony-stimulating factor (GM-CSF) in the OPTiM trial.
- Combining oncolytic virotherapy with cytokine immunotherapy may enhance immune response and patient outcomes.
Purpose of the Study:
- To establish optimal regimen scheduling for combination immunostimulation and T-VEC therapy.
- To develop and implement a computational biology model for an in silico clinical trial.
- To test and individualize combination immunotherapy and virotherapy for late-stage melanoma.
Main Methods:
- Calibration of a computational biology model simulating tumor cells and immune interactions.
- Implementation of the model into an in silico clinical trial.
- Personalization and optimization of combination therapy regimens.
Main Results:
- Improved simulated patient outcomes for late-stage melanoma using personalized combination therapy.
- Identification of key determinants for combination GM-CSF and T-VEC therapy.
- Development of clinically-actionable dosing strategies without further personalization.
Conclusions:
- Combination immunotherapy and oncolytic virotherapy can improve simulated patient outcomes.
- Individualized regimens offer insights into effective dosing strategies.
- This study provides a proof-of-concept for interdisciplinary approaches in determining combination therapy.
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