Modeling Bridging Biological Mechanisms to Population Level Scales
Population modeling has important links and collaborations with research efforts involving modeling of biological mechanisms. One key example is the initiative entitled, Bridging the Gap Between Cancer Mechanism and Population Science, which was jointly funded by NCI’s Division of Cancer Control and Population Sciences and Division of Cancer Biology under PAR-13-081. As the initiative’s title conveys, and as described in the funding announcement, the funded projects (now completed) bridged biological mechanism to population level scales. They were designed to cross-validate data gathered at different scales and explore links between basic biology, population science, and potential health applications in cancer treatment, prevention, diagnosis, and/or screening. The five funded projects are described below.
|Project Title and Abstract|
Baylor Research Institute
|Aspirin and Cancer Prevention in Lynch Syndrome: From Cell to Population Data
Lynch syndrome patients inherit a germline mutation in one of several DNA mismatch repair (MMR) genes, leading to a significantly earlier onset and a higher penetrance of colorectal cancer than in sporadic cases due to the occurrence of microsatellite instability (MSI). Long term administration of aspirin has been shown to reduce the incidence of colon cancer in Lynch syndrome patients, as documented by epidemiological data. The mechanisms underlying this effect are not fully understood. It is thought that protection is achieved through COX-dependent and independent activities. Basic kinetic parameters, such as the division rate, death rate, and mutation rate of cells have been shown to be affected. In order to better understand how protection is achieved, it is important to find out how changes in cellular parameters determine the degree of protection observed on the population level. The hypothesis is explored that the degree of protection observed on the population level can be predicted from data that quantify how aspirin changes parameters related to the evolutionary dynamics of cells. This hypothesis will be tested with an inter-disciplinary approach that combines experiments with mathematical models and that links data on cellular kinetics with those on incidence in the population. The firs two aims will quantify the extent to which aspirin changes a set of key parameters that are related to the growth and evolution of cells. These include the rate of cell division, the rate of ell death, the rate at which genetic changes are incurred, the probability of repair, the mean duration of repair, etc. This will be done with different cell lines exposed to varying levels of inflammation. The investigation starts first in an in vitro setting and is then extended to human tumor xenografts in mice, which represent a more complex growth environment for the cells. Subsequently, the data on cellular kinetics will inform a mathematical model of in vivo carcinogenesis in Lynch Syndrome patients. The model will be used to generate theoretical age-incidence curves for colon cancer in Lynch syndrome patients in the presence and absence of aspirin treatment. It will test whether the model can successfully predict the observed age-incidence curves through model application to epidemiological data. The model will allow us to determine which cellular parameter(s) contribute the most to the protection observed in the population data, and to explore possible avenues to enhance this level of protection. Implications of our results for understanding aspirin-mediated protection against sporadic colorectal cancer will be explored by analyzing appropriate epidemiological data.
Denis, Gerald V
Boston University Medical Campus
|Uncoupling Obesity from Breast Cancer in African American Women
The mechanistic relationship between immunometabolic complications of obesity and breast cancer is not understood, particularly in African American women, a group that is disproportionately affected. Insulin- resistant obesity features chronic systemic and local inflammation of fat, which has been linked to breast cancer outcomes. However, not all obesity conveys the same risk of cancer. About a quarter of obese African American adults are 'metabolically-healthy' despite their obesity and show reduced cardiovascular and diabetes risks. Recent analyses of Framingham Study population-based data show that risks for obesity- associated cancers, including breast cancer, are also reduced in these subjects. A key feature of these healthy obese adults is a reduced inflammatory profile, both locally in fat and systemically in blood. These data set up our long-term goal: to understand and use the relationships between obesity, inflammation and breast cancer outcomes to reduce the effects of obesity on cancer mortality. We do not know whether 'metabolically-healthy' obese African American women have less inflammation in breast tissue or systemically, or whether immunometabolic status associates with reduced breast cancer risk. Many 'metabolically-abnormal' obese African American women are given metformin to control blood glucose, but we do not know if metformin protects them against breast cancer; the critical studies simply have not been performed. It is urgent to resolve these questions, given the numbers of Americans affected and the high mortality arising from obesity and cancer. Our approach will investigate immunometabolic status and breast cancer in the Black Women's Health Study and use both basic laboratory and epidemiological population data to identify critical mechanisms and pharmacological solutions. Our overall objective is to define the critical immunometabolic mechanisms that stratify cancer risk in obese women, and test hypothesized relationships in cell culture models of breast cancer. Based on new preliminary data, we hypothesize that reduced inflammation in certain obese women protects against breast cancer; and that the standard of care for insulin-resistant obesity, metformin, has value for prevention of breast cancer in African American women. The hypothesis is formulated on the basis of preliminary and published studies of Framingham and BWHS subjects. We undertake three Aims: 1. Determine the immunometabolic factors that stratify obesity-related risk of breast cancer in BWHS subjects. 2. Determine whether inflammatory markers, including crown-like structures in breast adipose tissue and plasma cytokine levels, are associated with 'metabolically-abnormal' obesity as opposed to 'metabolically-healthy' obesity. 3. Determine whether novel inhibitors of inflammation and cancer diminish tumor cell aggressiveness in models of human breast cancer. The proposed research is innovative and important because we are the first to disentangle mechanisms that couple obesity to breast cancer risk. The investigation will have important public health impact because our results will help reduce cancer mortality in a disadvantaged population.
University of Michigan at Ann Arbor
|From Mechanism to Population: Modeling HPV-related Oropharyngeal Carcinogenesis
While cervical and other genital cancers are primarily caused by Human Papilloma Virus (HPV) infections, recent studies have demonstrated that HPV is also associated with head and neck (HN) cancers. The prevalence of oral HPV infection among men and women aged 14 to 69 years in the US is about 7%, however, 90% of University of Michigan (UM) oropharyngeal squamous cancer (OPSC) patients carry high-risk HPV. Indeed, the incidence of HPV- associated OPSCs is increasing and OPSC has become the most common HPV-related cancer in the US. HPV has been shown to disrupt several key cancer pathways in oropharyngeal squamous cell lines, including p53 and Rb, but many open questions remain regarding oral HPV transmission epidemiology, infection and persistence, the mechanisms of HPV HN carcinogenesis, and the connection between the ongoing oral HPV epidemic and the rising OPSC incidence. The overarching goal of this proposal is to understand the mechanistic effects of HPV infection on the regulatory pathways of oropharyngeal carcinogenesis, and how these effects in turn shape the observed age-specific incidence and mortality of OPSCs. This problem is inherently multi-scale, as population level HPV transmission drives dynamic, ongoing changes to intracellular cancer regulatory pathways, which in turn drives population-level trends in cancer incidence and mortality. Thus, understanding the rising incidence in OPSC necessitates tying together both the population level processes of infectious disease and the population-level cancer incidence through the mechanistic interactions between HPV and carcinogenesis. Toward this goal, we will develop systems biology models of the main proliferation regulatory networks affected by HPV, and assess the consequences of HPV infection, integration and alternate transcripts on the dynamics of HPV-positive tumor cell proliferation. We will integrate these mechanistic infection and cancer models into multistage models of carcinogenesis to gauge the impacts of HPV infection on the population-level age-specific incidence and mortality of OPSC. We will use these integrated multiscale cancer models in combination with population-level oral HPV transmission models to predict the effects of current HPV prevalence trends on future rates of OPSCs and the potential impact of vaccination and other prevention strategies. Our systems models will be based on multiscale inference using mechanistic infection and cancer data.
Luebeck, Georg E.
Fred Hutchinson Cancer Research Center
|Esophageal Cancer from Cells to Population: A Multiscale Approach
Esophageal Cancer from Cells to Population: A Multiscale Approach The goal of the proposed research is to reduce the burden of esophageal adenocarcinoma (EAC) by optimizing surveillance of patients with Barrett's esophagus (BE) using cutting-edge endoscopic imaging and advanced epigenetic profiling of neoplastic tissues in combination with standard endoscopic techniques. To accomplish this goal we will establish a multidisciplinary collaboration between cancer biologists, epidemiologists, clinicians and computational and mathematical modelers. This research team will develop a multiscale modeling framework that synthesizes and integrates data generated from diverse sources and at different scales to provide a coherent and informative portrayal of the natural history of EAC. Simulation models of EAC actively supported by the NCI's CISNET (U01 CA152926) and data from the Barrett's Esophagus Translational Research Network (BETRNet, U54 CA163060) will serve as the foundation for a new biologically-motivated Multiscale Esophageal Adenocarcinoma Model (MEMo). This new model will be informed by data that span numerous scales including: molecular level DNA methylation data, cellular level volumetric laser endomicroscopy (VLE) data, patient level endoscopic surveillance data, and population level cancer registry SEER data. We will use MEMo as an analytic tool to assess the clinical effectiveness of BE surveillance protocols for early esophageal neoplasia detection and prevention. By the end of the award period, we will have an improved and more comprehensive understanding of the biological and natural history of EAC that provides a platform to design better strategies to control its population burden.
Esserman, Laura J.
University of California, San Francisco
|Modeling the Impact of Targeted Therapy Based on Breast Cancer Subtypes
For decades our biological and clinical understanding of breast cancer has been based on three therapeutically predictive biomarkers: estrogen (ER), progesterone (PR) receptors and the human epidermal growth factor receptor-2 (HER2). Today, we recognize that breast cancer biology is more complex; as well, clinical oncologists routinely use additional biomarkers and gene expression signatures (e.g. Ki-67/IHC4, MammaPrint or Oncotype-Dx) to recommend breast cancer treatments. Despite this deeper understanding of breast cancer biology and increasing clinical use of biology-driven breast cancer therapeutics, we lack population-based estimates of the extent that these ever more costly breast cancer subtype-targeted diagnostics and therapeutics actually reduce breast cancer mortality (BCM), improve quality of life (QOL), or otherwise prove cost-effective. To address this need and overcome the challenging constraints imposed by cross-sectional US population modeling efforts now being advanced by the Cancer Intervention and Surveillance Modeling Network (CISNET), we will model an expanded repertoire of prognostic and predictive biomarkers linked to biology. We will employ a unique longitudinal population dataset not available in the US: the 40+ year old Stockholm Breast Cancer Registry, which currently tracks ~40,000 individual breast cancer patients over time and is annotated for screening, tumor biomarkers, treatments and outcomes, and which can be linked to the Stockholm Mammography Registry through unique identifiers, providing an unparalleled longitudinal population dataset for modeling. To model the population benefits of more modern predictive biomarkers and tailored adjuvant therapies, we will utilize our access to two other unique breast cancer randomized trials: the Stockholm-1 and I-SPY clinical trial datasets. Stockholm-1 consists of 729 women randomized to tamoxifen vs. no systemic therapy with 30-year follow-up; and the I-SPY trials are fully characterized biomarker-driven trials of pathway targeted agents that include response to therapy and event-free survival outcomes. Finally, we will update the CISNET model to estimate the population level benefits (BCM and cost effectiveness) of a more biologically targeted approach to treatment and screening. The specific aims for this study include: Aim 1. Develop and program a bridging model using longitudinal Swedish population data to determine the impact of assigning treatments on the basis of biological subtypes. This model will then be tailored to the US population using biased sampling to reflect SEER characteristics. Aim 2. Use the model in Aim 1 to evaluate the population effects on breast cancer mortality of tailored therapy employing highly characterized data sets with survival benefits and/or response rates from biomarker-driven outcomes and or/treatment. Aim 3. Estimate the population level cost effectiveness of biologically targeted therapy.