Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis

The Function of the Immune System, Beyond Strategies Based on Cell-Autonomous Mechanisms, Determines Cancer Development: Immune Response and Cancer Development

Although cancer remains a challenging disease to treat, early detection and removal of primary tumors through surgery or chemotherapy/radiotherapy can offer hope for patients. The privilege paradigm in cancer biology suggests that cell-autonomous mechanisms play a central role in tumorigenesis. According to this paradigm, these cellular mechanisms are the primary focus for the prevention and treatment of cancers. However, this point of view does not present a comprehensive theory for the initiation of cancer and an effective therapeutic strategy. Having an incomplete understanding of the etiology of cancer, it is essential to re-examine previous assumptions about carcinogenesis and develop new, practical theories that can account for all available clinical and experimental evidence. This will not only help to gain a better understanding of the disease, but also offer new avenues for treatment. This review provides evidence suggesting a shift in focus from a cell-autonomous mechanism to systemic mechanisms, particularly the immune system, that are involved in cancer formation.

Chemotherapy-induced cachexia and model-informed dosing to preserve lean mass in cancer treatment

Although chemotherapy is a standard treatment for cancer, it comes with significant side effects. In particular, certain agents can induce severe muscle loss, known as cachexia, worsening patient quality of life and treatment outcomes. 5-fluorouracil, an anti-cancer agent used to treat several cancers, has been shown to cause muscle loss. Experimental data indicates a non-linear dose-dependence for muscle loss in mice treated with daily or week-day schedules. We present a mathematical model of chemotherapy-induced muscle wasting that captures this non-linear dose-dependence. Area-under-the-curve metrics are proposed to quantify the treatment’s effects on lean mass and tumour control. Model simulations are used to explore alternate dosing schedules, aging effects, and morphine use in chemotherapy treatment with the aim of better protecting lean mass while actively targeting the tumour, ultimately leading to improved personalization of treatment planning and improved patient quality of life.

In this paper we present a novel mathematical model for muscle loss due to cancer chemotherapy treatment. Loss of muscle mass relates to increased drug toxicity and side-effects, and to decreased patient quality of life and survival rates. With our model, we examine the therapeutic efficacy of various dosing schedules with the aim of controlling a growing tumour while also preserving lean mass. Preservation of body composition, in addition to consideration of inflammation and immune interactions, the gut microbiome, and other systemic health measures, may lead to improved patient-specific treatment plans that improve patient quality of life.

Adenosine 2A receptor and TIM3 suppress cytolytic killing of tumor cells via cytoskeletal polarization

Tumors generate an immune-suppressive environment that prevents effective killing of tumor cells by CD8⁺ cytotoxic T cells (CTL). It remains largely unclear upon which cell type and at which stage of the anti-tumor response mediators of suppression act. We have combined an in vivo tumor model with a matching in vitro reconstruction of the tumor microenvironment based on tumor spheroids to identify suppressors of anti-tumor immunity that directly act on interaction between CTL and tumor cells and to determine mechanisms of action. An adenosine 2A receptor antagonist, as enhanced by blockade of TIM3, slowed tumor growth in vivo. Engagement of the adenosine 2A receptor and TIM3 reduced tumor cell killing in spheroids, impaired CTL cytoskeletal polarization ex vivo and in vitro and inhibited CTL infiltration into tumors and spheroids. With this role in CTL killing, blocking A_2AR and TIM3 may complement therapies that enhance T cell priming, e.g. anti-PD-1 and anti-CTLA-4.

Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC.

RESULTS

In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines.

CONCLUSION

Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.

Using Free-Range Laboratory Mice to Explore Foraging, Lifestyle, and Diet Issues in Cancer

As cancer progresses, its impact should manifest in the foraging behavior of its host much like the effects of endo-parasites that hinder foraging aptitudes and risk management abilities. Furthermore, the lifestyle of the host can impact tumor growth and quality of life. To approach these questions, we conducted novel experiments by letting C57BL/6 laboratory mice, with or without oral squamous cell carcinoma, free range in a large outdoor vivarium. Our goals were to: (1) determine whether one could conduct experiments with a mouse model under free range conditions, (2) measure effects of cancer burden on foraging metrics, (3) compare tumor growth rates with laboratory housed mice, and (4) begin to sort out confounding factors such as diet. With or without cancer, the C57BL/6 laboratory mice dealt with natural climatic conditions and illumination, found shelter or dug burrows, sought out food from experimental food patches, and responded to risk factors associated with microhabitat by foraging more thoroughly in food patches under bush (safe) than in the open (risky). We quantified foraging using giving-up densities of food left behind in the food patches. The mice’s patch use changed over time, and was affected by disease status, sex, and microhabitat. Males, which were larger, consumed more food and had lower giving-up densities than females. Relative to cancer-free mice, mice with growing tumors lost weight, harvested more food, and increasingly relied on patches in the bush microhabitat. The tumors of free-ranging mice in the vivarium grew slower than those of their cohort that were housed in mouse cages in animal facilities. Numerous interesting factors could explain the difference in tumor growth rates: activity levels, stress, weather, food intake, diet, and more. To tease apart one of these intertwined factors, we found that tumors grew faster when mice in the laboratory were fed on millet rather than laboratory mouse chow. While just a start, these novel experiments and framework show how free-ranging mice provide a model that can test a broader range of hypotheses and use a broader range of metrics regarding cancer progression and its consequences for the host.

CyberKnife radiation therapy as a platform for translational mouse studies

PURPOSE

Radiation therapy (RT) is a common nonsurgical treatment in the management of patients with cancer. While genetically engineered mouse models (GEMM) recapitulate human disease, conventional linear particle accelerator systems are not suited for state-of-the-art, imageguided targeted RT (IGRT) of these murine tumors. We employed the CyberKnife (CK; Accuray) platform for IGRT of GEMM-derived non-small cell lung cancer (NSCLC) lesions.

MATERIAL AND METHODS

GEMM-derived KrasLSL-G12D/+/Trp53fl/fl -driven NSCLC flank tumors were irradiated using the CK RT platform. We applied IGRT of 2, 4, 6, and 8 Gy using field sizes of 5-12.5 mm to average gross tumor volumes (GTV) of 0.9 cm3 using Xsight Spine Tracking (Accuray).

RESULTS

We found that 0 mm planning target volume (PTV) margin is sufficient for IGRT of murine tumors using the CK. We observed that higher RT doses (6-8 Gy) decreased absolute cell numbers of tumor infiltrating leukocytes (TIL) by approximately half compared to low doses (2-4 Gy) within 1 h, but even with low dose RT (2 Gy) TIL were found to be reduced after 8-24 h.

CONCLUSION

We here demonstrate that the CK RT system allows for targeted IGRT of murine tumors with high precision and constitutes a novel promising platform for translational mouse RT studies.

Individualized growth prediction of mice skin tumors with maximum likelihood estimators

BACKGROUND & OBJECTIVE

In this work, we focus on estimating the parameters of the Gompertz model in order to predict tumor growth. The estimation is based on measurements from mice skin tumors of de novo carcinogenesis. The main objective is to compare the Maximum Likelihood estimator with the best performance from our previous work with the Non-linear Least Squares estimator which is commonly used in the literature to estimate the growth parameters of the Gompertz model.

METHODS

To describe tumor growth, we propose a stochastic model which is based on the Gompertz growth function. The principle of Maximum Likelihood is used to estimate both the growth rate and the carrying capacity of the Gompertz function, along with the characteristics of the additive Gaussian process and measurement noise. Moreover, we examine whether a Maximum A Posteriori estimator is able to utilize any available prior knowledge in order to improve the predictions.

RESULTS

Experimental data from a total of 24 tumors in 8 mice (3 tumors each) were used to study the performance of the proposed methods with respect to prediction accuracy. Our results show that the Maximum Likelihood estimator is able to provide, in most cases, more accurate predictions. Moreover, the Maximum A Posteriori estimator has the potential to correct potentially non-realistic estimates for the carrying capacity at early growth stages.

CONCLUSION

In most cases, the Maximum Likelihood estimator is able to provide more reliable predictions for the tumor's growth on individual test subjects. The Maximum A Posteriori estimator, it has the potential to improve the prediction when the available experimental data do not provide adequate information by utilizing prior knowledge about the unknown parameters.

Whole transcriptome sequence data of 5-FU sensitive and 5-FU resistant tumors generated in a mouse model of de novo carcinogenesis

We have performed whole transcriptome sequencing of 5-FU resistant and 5-FU sensitive tumors generated in a mouse model of de novo carcinogenesis that closely recapitulates tumor initiation, progression and maintenance in vivo. Tumors were generated using the DMBA/TPA model of chemically induced carcinogenesis [1], tumor-bearing mice were subsequently treated with 5-FU, and tumor growth as well as response to treatment was monitored by measuring tumor volume twice a week. Based on these measurements, we selected two 5-FU resistant and two 5-FU sensitive tumors and performed whole transcriptome sequencing and in order to identify differentially expressed transcripts between the two sets. Data obtained is deposited and available through NCBI SRA (reference number SRP155180 – https://www.ncbi.nlm.nih.gov/sra/?term=SRP155180).

Tumor growth modeling: Parameter estimation with Maximum Likelihood methods

BACKGROUND & OBJECTIVE

In this work, we focus on estimating the parameters of the widely used Gompertz tumor growth model, based on measurements of the tumor's volume. Being able to accurately describe the dynamics of tumor growth on an individual basis is very important both for growth prediction and designing personalized, optimal therapy schemes (e.g. when using model predictive control).

METHODS

Our analysis aims to compute both the growth rate and the carrying capacity of the Gompertz function, along with the characteristics of the additive Gaussian process and measurement noise of the system. Three methods based on Maximum Likelihood estimation are proposed. The first utilizes an assumption regarding the measurement noise that simplifies the problem, the second combines the Extended Kalman Filter and Maximum Likelihood estimation, and the third is a nonstandard exact form of Maximum Likelihood estimation, where numerical integration is used to approximate the likelihood of the measurements, along with a novel way to reduce the required computations.

RESULTS

Synthetic data were used in order to perform extensive simulations aiming to compare the methods' effectiveness, with respect to the accuracy of the estimation. The proposed methods are able to estimate the growth dynamics, even when the noise characteristics are not estimated accurately. Another very important finding is that the methods perform best in the case that corresponds to the problem needed to be solved when dealing with experimental data.

CONCLUSION

Using nonstandard, problem specific techniques can improve the estimation accuracy and best exploit the available data.

Chemotherapeutic Dose Scheduling Based on Tumor Growth Rates Provides a Case for Low-Dose Metronomic High-Entropy Therapies

We extended the classical tumor regression models such as Skipper's laws and the Norton-Simon hypothesis from instantaneous regression rates to the cumulative effect over repeated cycles of chemotherapy. To achieve this end, we used a stochastic Moran process model of tumor cell kinetics coupled with a prisoner's dilemma game-theoretic cell-cell interaction model to design chemotherapeutic strategies tailored to different tumor growth characteristics. Using the Shannon entropy as a novel tool to quantify the success of dosing strategies, we contrasted MTD strategies as compared with low-dose, high-density metronomic strategies (LDM) for tumors with different growth rates. Our results show that LDM strategies outperformed MTD strategies in total tumor cell reduction. This advantage was magnified for fast-growing tumors that thrive on long periods of unhindered growth without chemotherapy drugs present and was not evident after a single cycle of chemotherapy but grew after each subsequent cycle of repeated chemotherapy. The evolutionary growth/regression model introduced in this article agrees well with murine models. Overall, this model supports the concept of designing different chemotherapeutic schedules for tumors with different growth rates and develops quantitative tools to optimize these schedules for maintaining low-volume tumors. Cancer Res; 77(23); 6717-28. ©2017 AACR.

Non-cell-autonomous effects yield lower clonal diversity in expanding tumors

Recent cancer research has investigated the possibility that non-cell-autonomous (NCA) driving tumor growth can support clonal diversity (CD). Indeed, mutations can affect the phenotypes not only of their carriers (“cell-autonomous”, CA effects), but also sometimes of other cells (NCA effects). However, models that have investigated this phenomenon have only considered a restricted number of clones. Here, we designed an individual-based model of tumor evolution, where clones grow and mutate to yield new clones, among which a given frequency have NCA effects on other clones’ growth. Unlike previously observed for smaller assemblages, most of our simulations yield lower CD with high frequency of mutations with NCA effects. Owing to NCA effects increasing competition in the tumor, clones being already dominant are more likely to stay dominant, and emergent clones not to thrive. These results may help personalized medicine to predict intratumor heterogeneity across different cancer types for which frequency of NCA effects could be quantified.