Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patient's immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society Interfac

A mathematical model of combined CD8 T cell costimulation by 4-1BB (CD137) and OX40 (CD134) receptors.

Combined agonist stimulation of the TNFR costimulatory receptors 4-1BB (CD137) and OX40(CD134) has been shown to generate supereffector CD8 T cells that clonally expand to greater levels, survive longer, and produce a greater quantity of cytokines compared to T cells stimulated with an agonist of either costimulatory receptor individually. In order to understand the mechanisms for this effect, we have created a mathematical model for the activation of the CD8 T cell intracellular signaling network by mono- or dual-costimulation. We show that supereffector status is generated via downstream interacting pathways that are activated upon engagement of both receptors, and in silico simulations of the model are supported by published experimental results. The model can thus be used to identify critical molecular targets of T cell dual-costimulation in the context of cancer immunotherapy

A Systems Biology Approach to Understanding the Pathophysiology of High-Grade Serous Ovarian Cancer: Focus on Iron and Fatty Acid Metabolism.

Ovarian cancer (OVC) is the most lethal of the gynecological malignancies, with diagnosis often occurring during advanced stages of the disease. Moreover, a majority of cases become refractory to chemotherapeutic approaches. Therefore, it is important to improve our understanding of the molecular dependencies underlying the disease to identify novel diagnostic and precision therapeutics for OVC. Cancer cells are known to sequester iron, which can potentiate cancer progression through mechanisms that have not yet been completely elucidated. We developed an algorithm to identify novel links between iron and pathways implicated in high-grade serous ovarian cancer (HGSOC), the most common and deadliest subtype of OVC, using microarray gene expression data from both clinical sources and an experimental model. Using our approach, we identified several links between fatty acid (FA) and iron metabolism, and subsequently developed a network for iron involvement in FA metabolism in HGSOC. FA import and synthesis pathways are upregulated in HGSOC and other cancers, but a link between these processes and iron-related genes has not yet been identified. We used the network to derive hypotheses of specific mechanisms by which iron and iron-related genes impact and interact with FA metabolic pathways to promote tumorigenesis. These results suggest a novel mechanism by which iron sequestration by cancer cells can potentiate cancer progression, and may provide novel targets for use in diagnosis and/or treatment of HGSOC

Systems biology of ferroptosis: A modeling approach.

Ferroptosis is a recently discovered form of iron-dependent regulated cell death (RCD) that occurs via peroxidation of phospholipids containing polyunsaturated fatty acid (PUFA) moieties. Activating this form of cell death is an emerging strategy in cancer treatment. Because multiple pathways and molecular species contribute to the ferroptotic process, predicting which tumors will be sensitive to ferroptosis is a challenge. We thus develop a mathematical model of several critical pathways to ferroptosis in order to perform a systems-level analysis of the process. We show that sensitivity to ferroptosis depends on the activity of multiple upstream cascades, including PUFA incorporation into the phospholipid membrane, and the balance between levels of pro-oxidant factors (reactive oxygen species, lipoxogynases) and antioxidant factors (GPX4). We perform a systems-level analysis of ferroptosis sensitivity as an outcome of five input variables (ACSL4, SCD1, ferroportin, transferrin receptor, and p53) and organize the resulting simulations into \u27high\u27 and \u27low\u27 ferroptosis sensitivity groups. We make a novel prediction corresponding to the combinatorial requirements of ferroptosis sensitivity to SCD1 and ACSL4 activity. To validate our prediction, we model the ferroptotic response of an ovarian cancer stem cell line following single- and double-knockdown of SCD1 and ACSL4. We find that the experimental outcomes are consistent with our simulated predictions. This work suggests that a systems-level approach is beneficial for understanding the complex combined effects of ferroptotic input, and in predicting cancer susceptibility to ferroptosis

Mathematical modeling of tumor-microenvironment dynamics

In this thesis we explore tumor-microenvironment dynamics using three models of decreasing complexity. The first is a multispecies, spatiotemporal model of tumor development in tumor-derived growth factor responsive stroma that is activated to secrete the tumor growth and dispersal activator HGF. We show that HGF-induced invasive tumor morphology is promoted by increased heterogeneity at the tumor-host boundary. The second model is a system of ODEs that explores hypotheses based on experimental observations that tumor growth inhibition can occur at high levels of HGF. The model allows for the prediction of the molecular mechanism of HGF action via dose-response curve analysis. The final model is a system of two ODEs for stem cell and chemical activator of stem cell self-renewal concentrations, and allows for the approximation of the separatrix of the phase space that divides the space into basins of attraction for tumor eradication and tumor maintenance. The multiple models allow us to consider tumor-host interactions at various levels of abstraction and thus to infer both qualitative and quantitative results regarding tumor response to host and tumor-derived growth activators

Mathematical modeling of tumor-microenvironment dynamics

In this thesis we explore tumor-microenvironment dynamics using three models of decreasing complexity. The first is a multispecies, spatiotemporal model of tumor development in tumor-derived growth factor responsive stroma that is activated to secrete the tumor growth and dispersal activator HGF. We show that HGF-induced invasive tumor morphology is promoted by increased heterogeneity at the tumor-host boundary. The second model is a system of ODEs that explores hypotheses based on experimental observations that tumor growth inhibition can occur at high levels of HGF. The model allows for the prediction of the molecular mechanism of HGF action via dose-response curve analysis. The final model is a system of two ODEs for stem cell and chemical activator of stem cell self-renewal concentrations, and allows for the approximation of the separatrix of the phase space that divides the space into basins of attraction for tumor eradication and tumor maintenance. The multiple models allow us to consider tumor-host interactions at various levels of abstraction and thus to infer both qualitative and quantitative results regarding tumor response to host and tumor-derived growth activators

Predicting mechanism of biphasic growth factor action on tumor growth using a multi-species model with feedback control.

A large number of growth factors and drugs are known to act in a biphasic manner: at lower concentrations they cause increased division of target cells, whereas at higher concentrations the mitogenic effect is inhibited. Often, the molecular details of the mitogenic effect of the growth factor are known, whereas the inhibitory effect is not. Hepatoctyte Growth Factor, HGF, has recently been recognized as a strong mitogen that is present in the microenvironment of solid tumors. Recent evidence suggests that HGF acts in a biphasic manner on tumor growth. We build a multi-species model of HGF action on tumor cells using different hypotheses for high dose-HGF activation of a growth inhibitor and show that the shape of the dose-response curve is directly related to the mechanism of inhibitor activation. We thus hypothesize that the shape of a dose-response curve is informative of the molecular action of the growth factor on the growth inhibitor