ONCO Subgroup Contributed Talks

David Basanta

Moffitt Cancer Center

"Using game theory to model somatic evolution in cancer treated in the presence of environmentally mediated protection"

In recent years, game theory has become a valuable tool for studying the evolutionary dynamics of cancer and their response to treatment. In this study, we use an evolutionary game theory model to investigate the interplay between treatment and the environment in the development of drug resistance in tumor populations. The model consists of two tumor populations: a sensitive and a resistant phenotype, and a stromal population that interacts with the tumor cells. We use replicator equations to simulate the evolutionary dynamics of the populations and analyze the effects of different treatment strategies on the development of drug resistance. To validate the model, we integrate experimental data from previous studies that have investigated the role of stromal cells, specifically carcinoma-associated fibroblasts, in weakening the tumor's response to treatment in cancer. We use simulations and analyses to optimize adaptive therapies in this context by adjusting treatment frequency and dosage. Our results demonstrate that adaptive therapies using a combination of drugs can significantly reduce the emergence of drug resistance in the tumor population. Furthermore, our simulations suggest that adjusting the treatment frequency and dosage can further optimize the adaptive therapies, taking into account the interactions between the tumor and stromal cells. These results highlight the importance of considering the evolutionary dynamics of cancer cells when designing treatments. By understanding how selective pressures affect tumor populations and how they respond to treatment, we can develop more effective therapies that can prolong patient survival and improve their quality of life. Moreover, the evolutionary game theory model presented here can be adapted to other cancer types, potentially leading to the development of more personalized and effective treatments.
Additional authors: Grace Wolfe, University of South Florida; Andriy Marusyk, Moffitt Cancer Center

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Stefano Casarin

Houston Methodist Research Institute

"Improving the Efficacy of Radium223 for Prostate Cancer Bone Metastasis through Targeting β1 Integrin: In Silico Modeling and In Vivo Validation"

Bone metastasis is a lethal consequence for prostate cancer patients, mostly due to the emergence of resistance and therapy failure. Bone-targeting radiotherapy with Radium223 (Rad223), a radioisotope emitting genotoxic alpha-radiation with limited tissue penetrance (∼100 µm), prolongs the survival of patients with metastatic prostate cancer (PCa). The clinical response to Rad223 is often followed by detrimental relapse and progression. Whether Rad223 causes tumor-cell directed cytotoxicity in vivo remains unclear, and, additionally, effective strategies to improve long-term Rad223 efficacy have not been developed yet. Integrins are heterodimeric transmembrane receptors that, through their activity, support cell growth, decrease cell death and enable radio-resistance mechanisms on exposure to ionizing radiation. Anti-β1 integrin (β1I) targeting improves irradiation treatment outcomes in breast cancer cells and other subcutaneous xenografts. We hypothesize that: i) limited radiation penetrance in situ defines outcome, and ii) targeting β1I would improve 223Ra outcome. In vivo approaches have been limited by huge resource demanding, ethical concerns, and paucity of investigation time points during follow-up. The integration of central biological findings with mathematical modeling allows generating in silico pathophysiological profiles suitable for testing preclinically relevant hypotheses, including predictions on the impact of combinatorial treatments on disease progression. Such predictions can guide preclinical and clinical studies towards more successful outcomes and maximize efficacy. Accordingly, we developed an agent-based model of prostate cancer bone metastasis establishment, growth, and response to putative therapies to: i) predict Rad223 effectiveness in lesions of different sizes, ii) identify Rad223 resistance niches, and iii) optimize Rad223- β1I combinatorial regimen. Our model is regulated via Montecarlo stochastic simulations and seeded with bone metastasis cells endowed with mitosis/apoptosis probability densities which drive the model towards different outcome according to the therapeutic regimen tested. Our in silico model predictions were validated in vivo on humanized bone metastasis mouse model Rad223 delayed the growth of tumors (PC3 and C4-2B cell line) in bone. Cancer cell lethality in response to Rad223 was profound but zonally confined along the bone interface compared with the more distant tumor core, which remained unperturbed. In silico simulations predicted greater efficacy of Rad223 on single-cell lesions and minimal effects on larger, as further confirmed in vivo for PC3 and C4-2B tumors. Micro-tumors showed severe growth delay or eradication in response to Rad223, whereas macro-tumors persisted and expanded. The relative inefficacy in controlling large tumors points to application of Rad223 in secondary prevention of early bone-metastatic disease and regimens co-targeting the tumor core. Interference with β1I combined with Rad223 reduced PC3 cell growth in bone and significantly improved overall mouse survival, whereas no change was achieved in C4-2B tumors. Anti-β1I treatment decreased the PC3 tumor cell mitosis index and spatially expanded Rad223 lethal effects 2-fold, in vivo and in silico. Regression was paralleled by decreased expression of radio-resistance mediator. Targeting β1I significantly improves Rad223 outcome and points toward combinatorial application in PCa tumors with high β1I expression.
Additional authors: Eleonora Dondossola, David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.

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Tatiana Miti

H. Lee Moffitt Cancer Center

"Integrating Spatatial statistics and ABMs to Study Stromal Effects on the Remission-Relapse Dynamics of NSCLC and TNBC"

Tumor relapse during therapies is thought to reflect the ability of tumor cells to escape treatment via cell-intrinsic genetic and epigenetic changes, either preexistent or evolved de novo under the drug-imposed cytostatic and cytotoxic selective pressures. Multiple published and unpublished reports, show that at least some of the otherwise drug-sensitive tumor cells might be able to avoid elimination due to microenvironmental factors, such as pro-survival paracrine signals, capable of providing alternative means of survival despite the shutdown of oncogenic signaling. Concurrently, clinical studies indicated that higher stroma-to-tumor content has a poorer prognosis and increased risk of relapse in various cancers. However, despite our advances in deciphering the molecular mechanisms behind tumor progression, our understanding of how stroma contributes to the evolution of tumor resistance and relapse remains limited. Our objective is to build stochastic mathematical models to help understand the stroma-tumor eco-evolutionary interplay so that we can design new therapeutic strategies for tumor elimination or treatments that provide long-term control of tumor growth. The models integrate the spatiotemporal stroma-tumor cells interactions measured using a novel purposely-designed spatial analysis pipeline and combine it with in vivo and in vitro tumor growth dynamics data. We focus on two distinct stromal effects studied in our lab in vitro and in vivo, namely drug-sheltering effects against targeted therapies in Non-Small Cell Lung Cancer (NSCLC) and enhanced tumor cells’ proliferation, indiscriminately of treatment presence, in Triple Negative Breast Cancer (TNBC). The preliminary results show that enhanced proliferation in the vicinity of stroma in TNBC could be sufficient to drive tumor relapse after four cycles of chemotherapy and greatly accelerates relapse in tumors where the treatment has a low killing rate. In the context of NSCLC, the drug-sheltering effects of stroma could lead to tumor relapse in absence of hard-wired resistance, and disrupting the drug-sheltering stromal effects result in a higher rate of tumor eradication. Our results show that by using carefully designed and calibrated mathematical models, we can gain a deeper understanding of the ecological mechanisms that lead to tumor relapse as well as uncover new therapeutic strategies that account for stromal effects and are successful at eradicating tumors.
Additional authors: Daria Myroshnychenko, Bina Desai, Anna Miller, Philipp Altrock, Andriy Marusyk, David Basanta

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Zuping Wang

University of Maryland, College Park

"A mathematical model of TCR T cell therapy for cervical cancer"

Engineered T cell receptor (TCR) T cells are expected to drive strong anti-tumor responses upon recognition of the specific tumor antigen, with rapid expansion and cytotoxic functions, causing tumor cell death. However, although TCR T cell therapy against cancers is promising, it remains difficult to predict which patient will have better therapeutic outcome and why. We develop a mathematical model to re-assess some mechanisms of insufficient efficacy of TCR T cell therapy in HPV+ cervical cancer model of mice. We consider a dynamical system that follows the population of cancer cells, effector TCR T cells, regulatory T cells (Tregs), and 'non-tumor killing' TCR T cells. We demonstrate: 1) the majority of TCR T cell within the tumor is 'non-tumor killing' TCR T cells, such as exhausted cells, which are highly active but contribute little or have no direct cytotoxicity in the tumor microenvironment (TME); 2) there are two important conditions for tumor regression: the reversal of the immunosuppressive TME by depleting Tregs, and the increased proliferation of effector TCR T cells with antitumor activity. Using mathematical modeling, we show that certain treatment protocols have the potential to improve therapy responses.
Additional authors: Peter Choyke, National Cancer Institute, Molecular Imaging Branch; Doron Levy, University of Maryland, College Park; Noriko Sato, National Cancer Institute, Molecular Imaging Branch; Heyrim Cho, University of California, Riverside

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