Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Jenner, Adrianne L.; Smalley, Munisha; Goldman, David; Goins, William F.; Cobbs, Charles S.; Puchalski, Ralph B.; Chiocca, E. Antonio; Lawler, Sean E.; Macklin, Paul; Goldman, Aaron; Craig, Morgan

doi:10.1016/j.isci.2022.104395

Cited by 29 publications

(32 citation statements)

References 174 publications

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“…Hereby they can contribute to therapy failure when viral infection and viral replication are specific to cancer cells only. Our observations from the simulations are in line with various experimental studies that observe an improvement in therapeutic outcomes due to either low stromal density [29], or high density of target cancer cells [30], or upon sensitizing normal cells, or upon using viruses that are not only specific for cancer cells [31][32][33]. We observe that sensitizing stromal cells to viral infection increases the likelihood of the virus to spread from an infected cell to non-infected (sensitive) cancer cells in the neighbourhood, which results in an improvement in the efficacy of tumour eradication.…”

Section: Discussionsupporting

confidence: 90%

Modelling the spatial dynamics of oncolytic virotherapy in the presence of virus-resistant tumour cells

et al. 2022

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Oncolytic virotherapy is a promising form of cancer treatment that uses native or genetically engineered viruses to target, infect and kill cancer cells. Unfortunately, this form of therapy is not effective in a substantial proportion of cancer patients, partly due to the occurrence of infection-resistant tumour cells. To shed new light on the mechanisms underlying therapeutic failure and to discover strategies that improve therapeutic efficacy we designed a cell-based model of viral infection. The model allows us to investigate the dynamics of infection-sensitive and infection-resistant cells in tumour tissue in presence of the virus. To reflect the importance of the spatial configuration of the tumour on the efficacy of virotherapy, we compare three variants of the model: two 2D models of a monolayer of tumour cells and a 3D model. In all model variants, we systematically investigate how the therapeutic outcome is affected by the properties of the virus (e.g. the rate of viral spread), the tumour (e.g. production rate of resistant cells, cost of resistance), the healthy stromal cells (e.g. degree of resistance to the virus) and the timing of treatment. We find that various therapeutic outcomes are possible when resistant cancer cells arise at low frequency in the tumour. These outcomes depend in an intricate but predictable way on the death rate of infected cells, where faster death leads to rapid virus clearance and cancer persistence. Our simulations reveal three different causes of therapy failure: rapid clearance of the virus, rapid selection of resistant cancer cells, and a low rate of viral spread due to the presence of infection-resistant healthy cells. Our models suggest that improved therapeutic efficacy can be achieved by sensitizing healthy stromal cells to infection, although this remedy has to be weighed against the toxicity induced in the healthy tissue.

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Section: Discussionsupporting

confidence: 90%

Modelling the spatial dynamics of oncolytic virotherapy in the presence of virus-resistant tumour cells

et al. 2022

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“…57 Spatial heterogeneity is regarded a hallmark of cancer, and taking it into account would further enhance Stochastic discrete agent-based models and deterministic continuum partial differential equations-based models are the most common framework used to understand the heterogeneity in tumors. [73][74][75][76] We have developed a spatial platform, spQSP, that merges whole-patient QSP models with agent-based models of tumor with stochastic celllevel representation of the tumor microenvironment that is capable of incorporating results of digital pathology and spatial transcriptomic. [77][78][79] In summary, QSP is a quickly developing field that has been shown to play a crucial role in drug development and QSP models are increasingly becoming a standard part of regulatory submissions.…”

Section: Discussionmentioning

confidence: 99%

Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model

Sové

Verma

Wang

et al. 2022

J Immunother Cancer

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BackgroundHepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is the third-leading cause of cancer-related death worldwide. Most patients with HCC are diagnosed at an advanced stage, and the median survival for patients with advanced HCC treated with modern systemic therapy is less than 2 years. This leaves the advanced stage patients with limited treatment options. Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or its ligand, are widely used in the treatment of HCC and are associated with durable responses in a subset of patients. ICIs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) also have clinical activity in HCC. Combination therapy of nivolumab (anti-PD-1) and ipilimumab (anti-CTLA-4) is the first treatment option for HCC to be approved by Food and Drug Administration that targets more than one immune checkpoints.MethodsIn this study, we used the framework of quantitative systems pharmacology (QSP) to perform a virtual clinical trial for nivolumab and ipilimumab in HCC patients. Our model incorporates detailed biological mechanisms of interactions of immune cells and cancer cells leading to antitumor response. To conduct virtual clinical trial, we generate virtual patient from a cohort of 5,000 proposed patients by extending recent algorithms from literature. The model was calibrated using the data of the clinical trial CheckMate 040 (ClinicalTrials.gov number,NCT01658878).ResultsRetrospective analyses were performed for different immune checkpoint therapies as performed in CheckMate 040. Using machine learning approach, we predict the importance of potential biomarkers for immune blockade therapies.ConclusionsThis is the first QSP model for HCC with ICIs and the predictions are consistent with clinically observed outcomes. This study demonstrates that using a mechanistic understanding of the underlying pathophysiology, QSP models can facilitate patient selection and design clinical trials with improved success.

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“…It should be noted however, the HCA model is parameterized with a variety of in vitro, in vivo, and human data, making translation directly to the clinic challenging in its current form. This can be resolved with further rigorous model calibration and validation using clinical data, and multiple efforts at moving agent-based models into the clinical setting are underway [22][23][24]66,67 .…”

Section: Discussionmentioning

confidence: 99%

The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous proteasome inhibitor resistant disease

Miller

Bishop

et al. 2022

Preprint

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Multiple myeloma (MM) is an osteolytic plasma cell malignancy that, despite being responsive to therapies such as proteasome inhibitors, frequently relapses. Understanding the mechanism and the niches where resistant disease evolves remains of major clinical importance. Cancer cell intrinsic mechanisms and bone ecosystem factors are known contributors to the evolution of resistant MM but the exact contribution of each is difficult to define with current in vitro and in vivo models. However, mathematical modeling can help address this gap in knowledge. Here, we describe a novel biology- driven hybrid agent-based model that incorporates key cellular species of the bone ecosystem that control normal bone remodeling and, in MM, yields a protective environment under therapy. Critically, the spatiotemporal nature of the model captures two key features: normal bone homeostasis and how MM interacts with the bone ecosystem to induce bone destruction. We next used the model to examine how the bone ecosystem contributes to the evolutionary dynamics of resistant MM under control and proteasome inhibitor treatment. Our data demonstrates that resistant disease cannot develop without MM intrinsic mechanisms. However, protection from the bone microenvironment dramatically increases the likelihood of developing intrinsic resistance and subsequent relapse. The spatial nature of the model also reveals how the bone ecosystem provides a protective niche for drug sensitive MM cells under treatment, consequently leading to the emergence of a heterogenous and drug resistant disease. In conclusion, our data demonstrates a significant role for the bone ecosystem in MM survival and resistance, and suggests that early intervention with bone ecosystem targeting therapies may prevent the emergence of heterogeneous drug resistant MM.

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Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Cited by 29 publications

References 174 publications

Modelling the spatial dynamics of oncolytic virotherapy in the presence of virus-resistant tumour cells

Modelling the spatial dynamics of oncolytic virotherapy in the presence of virus-resistant tumour cells

Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model

The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous proteasome inhibitor resistant disease

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