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A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.

ABSTRACT: When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint-related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.

SUBMITTER: Gong C

PROVIDER: S-EPMC5636269 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.

Gong Chang C Milberg Oleg O Wang Bing B Vicini Paolo P Narwal Rajesh R Roskos Lorin L Popel Aleksander S AS

Journal of the Royal Society, Interface 20170901 134

When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint-related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions betwee ...[more]

PMID: 28931635

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Project description:Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.

Project description:Tumour growth, angiogenesis and oxygenation vary substantially among tumours and significantly impact their treatment outcome. Imaging provides a unique means of investigating these tumour-specific characteristics. Here we propose a computational model to simulate tumour-specific oxygenation changes based on the molecular imaging data. Tumour oxygenation in the model is reflected by the perfused vessel density. Tumour growth depends on its doubling time (T d) and the imaged proliferation. Perfused vessel density recruitment rate depends on the perfused vessel density around the tumour (sMVDtissue) and the maximum VEGF concentration for complete vessel dysfunctionality (VEGFmax). The model parameters were benchmarked to reproduce the dynamics of tumour oxygenation over its entire lifecycle, which is the most challenging test. Tumour oxygenation dynamics were quantified using the peak pO2 (pO2peak) and the time to peak pO2 (t peak). Sensitivity of tumour oxygenation to model parameters was assessed by changing each parameter by 20%. t peak was found to be more sensitive to tumour cell line related doubling time (~30%) as compared to tissue vasculature density (~10%). On the other hand, pO2peak was found to be similarly influenced by the above tumour- and vasculature-associated parameters (~30-40%). Interestingly, both pO2peak and t peak were only marginally affected by VEGFmax (~5%). The development of a poorly oxygenated (hypoxic) core with tumour growth increased VEGF accumulation, thus disrupting the vessel perfusion as well as further increasing hypoxia with time. The model with its benchmarked parameters, is applied to hypoxia imaging data obtained using a [(64)Cu]Cu-ATSM PET scan of a mouse tumour and the temporal development of the vasculature and hypoxia maps are shown. The work underscores the importance of using tumour-specific input for analysing tumour evolution. An extended model incorporating therapeutic effects can serve as a powerful tool for analysing tumour response to anti-angiogenic therapies.

Project description:Introduction:Until recently, systemic chemotherapy was the only option for treating bladder cancer and outcomes remained dismal. After a long gap of no progress for 40 years, immuno-therapy with checkpoint inhibitors (PDL1 and PD1) has revolutionized the treatment paradigm of bladder cancer, with five approved agents to treat platinum-refractory bladder cancer since the first approval of atezolizumab in May 2016. Methods:This review summarizes the most recent data on approved checkpoint inhibitors currently used in management of advanced bladder cancer. Early- and late-phase trials of the five checkpoint inhibitors (pembrolizumab, nivolumab, atezolizumab, durvalumab, and avelumab) in advanced bladder cancer are reviewed in detail. This review also describes the potential application of PD1/PDL1 inhibitors in adjuvant and neoadjuvant settings and non-muscle-invasive bladder cancer, as well as with radiation in muscle-invasive bladder cancer treatment. The role of PDL1 and tumor-mutation burden and clinical considerations in choosing a particular immunotherapy are also discussed. Results:The approved checkpoint inhibitors (PD1 and PDL1 inhibitors) have similar efficacy and safety profiles in metastatic platinum-refractory bladder cancer, but they vary in dose and frequency and cost burden. However, only pembrolizumab has shown superiority over standard chemotherapy in a randomized Phase III setting so far. In addition, in the first-line setting for cisplatin-ineligible patients, both pembrolizumab and atezolizumab are US Food and Drug Administration-approved and well tolerated. There is a lack of consensus on the utility of testing for PDL1 as a predictive biomarker, as patients with no PDL1 expression also derive some clinical benefit. Tumor-mutation burden is another predictive biomarker, but needs further validation. Conclusion:Immunotherapy has offered a glimmer of hope to patients with bladder cancer. The current landscape is rapidly evolving, with novel immunotherapy-combination trials to improve outcomes further and evaluate predictive biomarkers to help identify patients most likely to benefit from such therapies.

Dataset Information

A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.

Publications

A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.

Similar Datasets

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