Tumor-Immune Dynamics and Oncolytic Virotherapy

Tuesday, June 15 at 11:30am (PDT)
Tuesday, June 15 at 07:30pm (BST)
Wednesday, June 16 03:30am (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS08" time block.
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Lisette dePillis (Department of Mathematics, Harvey Mudd College, United States), Amina Eladdadi (Department of Mathematics, The College of St. Rose, United States)


Oncolytic viruses (OVs) have emerged as a novel and promising immunotherapeutic strategy against advanced cancer that may be further combined with existing therapeutic modalities to enhance effectiveness. Predicting the outcome of tumor virotherapy is a challenge since the system responses to OV treatment are complex. One of the major challenges involves understanding the immune system’s response to the virus, which diminishes the effects of oncolytic virotherapy by facilitating viral clearance. While incredible efforts have been made over the past decades to decipher the complexity of tumour–immune interactions, the dynamics of oncolytic viral tumor infection and the consequences of OV induced immune response are still poorly understood. In the quest to better understand the complex dynamics involved with OV therapy, mathematical models can be used to address specific questions regarding disease progression, immune activation, and pathologies. In this session, we will bring together applied mathematicians working in the areas of tumor-immune dynamics and virotherapy treatments to present current discoveries.

Raluca Eftime

(University of Franche-Comté, France)
"Modelling oncolytic virotherapies for cancer: the complex roles of innate immune responses"
Oncolytic viruses are emerging as important approaches in cancer treatment. However, the effectiveness of these therapies depends significantly on the interactions between the oncolytic viruses and the host immune response. Macrophages are one of the most important cell types in the anti-viral immune responses, as well as in the anti-cancer immune responses. Nevertheless, the heterogeneity of macrophage population (with the two extreme phenotypes represented by the M1 and M2 cells) makes it difficult to understand the anti-cancer as well as anti-viral roles of these cells. We start by focusing on a single-scale model for oncolytic virus--cancer cell interactions in the presence of immune responses represented by macrophages. We show that cell polarization towards either an M1 or M2 phenotype can enhance oncolytic virus therapy through either (i) anti-tumour immune activation, or (ii) enhanced oncolysis. Then, we discuss the impact of the spatial spread of macrophages inside solid tumours on the heterogeneous spatial distributions of oncolytic viruses.

Justin Le Sauteur

(University of Montreal, Canada)
"Optimizing combined oncolytic vaccinia and PAC-1 treatment of ovarian cancer using in silico clinical trials"
Ovarian cancer poses a unique challenge due to its late diagnosis and high rate of relapse. In response, oncolytic vaccinia virus (VACV), which selectively kills tumour cells through infection and viral replication, and procaspase-activating compound 1 (PAC-1), a small tumour cell apoptosis-inducing molecule, have been recently proposed as a combination therapy that may better control ovarian cancer growth. The combination of VACV and PAC-1 has already been shown to be a promising treatment, however a delicate therapeutic balance must be stuck, as PAC-1 induces apoptosis in cells that VACV needs for continued replication. To provide a quantitative basis behind the use of VACV with PAC-1 in ovarian cancer, we developed a mathematical and computational biology model that accounts for tumour growth and treatment-induced death. Our model was calibrated to experimental measurements of the individual and combined effects of each molecule. To determine the optimal dose size and therapeutic schedule for combined VACV and PAC-1, we expanded an in silico clinical trial of 200 patients to bolster the preclinical translation of this investigational therapy. Our results contribute to the evaluation of the validity of this proposed treatment, and establish maximal PAC-1 concentrations that maintain VACV efficacy. Overall, this work demonstrates the ability to use simple mathematical modelling techniques to inform treatment design in real time.

Pantea Pooladvand

(The University of Sydney, Australia)
"The dynamics of oncolytic virotherapy in dense tumours"
The growth of a tumour can be characterised by a complex network of cells, fibers and molecules. Images of tumour histology show that cell-stroma landscapes can vastly differ from one tumour to another. These variations in structure, density and cell placement inevitably change the outcome of treatment. Mathematical studies often focus on modelling the degradation and reconstruction of extracellular matrix (ECM) by tumour cells to capture tumour progression. However, the extracellular matrix can also significantly hinder anti-cancer therapy. In this project we explore the role of ECM differently. Here, we focus on how changes in stroma affect oncolytic virotherapy. We want to understand how different configurations of tumour-ECM landscape change the spread and efficacy of viral treatment. By building a system of partial differential equations that includes a novel diffusion term for virus spread in ECM, we look for patterns in tumour-cell ratios, collagen density and collagen configurations to predict treatment outcome. We find that collagen density, cell-collagen ratio and gaps in the collagen surface can significantly affect tumour treatment. Therefore, to accurately describe treatment outcome in oncolytic virotherapy, models need to consider the influence of cell-collagen interactions on therapy.

Khaphetsi J. Mahasa

(National University of Lesotho, Lesotho)
"Natural killer cells recruitment in oncolytic virotherapy: a mathematical model"
In this talk, we investigate how natural killer (NK) cell recruitment to the tumor microenvironment (TME) affects oncolytic virotherapy. NK cells play a major role against viral infections. They are, however, known to induce early viral clearance of oncolytic viruses, which hinders the overall efficacy of oncolytic virotherapy. Here, we formulate and analyze a simple mathematical model of the dynamics of the tumor, OV and NK cells using currently available preclinical information. The aim of this study is to characterize conditions under which the synergistic balance between OV-induced NK responses and required viral cytopathicity may or may not result in a successful treatment. In this study, we found that NK cell recruitment to the TME must take place neither too early nor too late in the course of OV infection so that treatment will be successful. NK cell responses are most influential at either early (partly because of rapid response of NK cells to viral infections or antigens) or later (partly because of antitumoral ability of NK cells) stages of oncolytic virotherapy. The model also predicts that: (a) an NK cell response augments oncolytic virotherapy only if viral cytopathicity is weak; (b) the recruitment of NK cells modulates tumor growth; and (c) the depletion of activated NK cells within the TME enhances the probability of tumor escape in oncolytic virotherapy. Taken together, our model results demonstrate that OV infection is crucial, not just to cytoreduce tumor burden, but also to induce the stronger NK cell response necessary to achieve complete or at least partial tumor remission. Furthermore, our modeling framework supports combination therapies involving NK cells and OV which are currently used in oncolytic immunovirotherapy to treat several cancer types.

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Virtual conference of the Society for Mathematical Biology, 2021.