The two-stage clonal expansion model in occupational cancer epidemiology: Results from three cohort studies

Copyright © 2010 by the BMJ Publishing Group Ltd. All rights reserved.Objectives: The objective of this work was to apply the two-stage clonal expansion model, with the intention to expand the literature on epidemiological applications of the model and demonstrate the feasibility of incorporating biologically based modelling methods into the widely used retrospective cohort study. Methods: The authors fitted the two-stage clonal expansion model model to three occupational cohort studies: (1) a cohort of textile workers exposed to asbestos and followed for lung cancer mortality; (2) a cohort of diatomaceous earth workers exposed to silica and also followed for lung cancer mortality; and (3) a cohort of automotive manufacturing workers exposed to straight metalworking fluid (MWF) and followed for larynx cancer incidence. The model allowed the authors to estimate exposure effects in three stages: cancer initiation (early effects), promotion or malignant transformation (late effects). Results: In the first cohort, the authors found strong evidence for an early effect of asbestos on lung cancer risk. Findings from analyses of the second cohort suggested early and less evidently late effects of silica on lung cancer risk. In the MWF (third) cohort, there was only weak evidence of straight MWF exposure effects on both early and late stages. The authors also observed a late birth cohort effect on larynx cancer risk. Conclusions: The findings for asbestos and silica were essentially confirmatory, supporting evidence for their early effects on lung cancer from a large body of literature. The effect of straight MWF on larynx cancer was less clear.This work was supported by a grant from the US National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention,R01-OH03575, and a grant from the Centers for Disease Control and Prevention/ Association of Teachers of Preventive Medicine number TS 0699

The real war on cancer: the evolutionary dynamics of cancer suppression.

Cancer is a disease of multicellular animals caused by unregulated cell division. The prevailing model of cancer (multistage carcinogenesis) is based on the view that cancer results after a series of (generally somatic) mutations that knock out the genetic mechanisms suppressing unregulated cell growth. The chance of these mutations occurring increases with size and longevity, leading to Peto's paradox: why don't large animals have a higher lifetime incidence of cancer than small animals? The solution to this paradox is evolution. From an evolutionary perspective, an increasing frequency of prereproductive cancer deaths results in natural selection for enhanced cancer suppression. The expected result is a prereproductive risk of cancer across species that is independent of life history. However, within species, we still expect cancer risk to increase with size and longevity. Here, I review the evolutionary model of cancer suppression and some recent empirical evidence supporting it. Data from humans and domestic dogs confirm the expected intraspecific association between size and cancer risk, while results from interspecific comparisons between rodents provide the best evidence to date of the predicted recruitment of additional cancer suppression mechanisms as species become larger or longer lived

Stochastic and State Space Models of Carcinogenesis Under Complex Situation

With more and more biological mechanisms of cancer development being discovered, in order to improve cancer control and prevention, it becomes necessary to develop effective and efficient mathematical and statistical models and methods to incorporate the biological information, and to identify critical events in the process of carcinogenesis. In this dissertation, the complex nature of carcinogenesis has been represented by stochastic system model; combining this model with information from observations and prior knowledge, we have developed state space models to evaluate cancer gene mutations and cell proliferation at different cancer development stages. Also, we have proposed a generalized Bayesian method via multi-level Gibbs sampling procedure to predict state (stage) variables of the models. In this dissertation, stochastic models have been proposed for initiation, promotion and complete carcinomas experiments; these experiments are most commonly performed in cancer risk assessment of environmental agents. These stochastic models are simple multi-pathway models which are constructed based on biological mechanisms. The estimates we obtained from the models have provided quantitative evaluation of dose related mutation rates of major genes and cells proliferation rates; these results could be used to assess the risk of developing malignant tumor in the environment we live. More complicated stochastic and state space models have been developed for sporadic human colon cancer and for hereditary and non-hereditary human liver cancer. We have utilized the proposed models to fit to Surveillance Epidemiology and End Results (SEER) data. The results imply that our models have effectively incorporated biological information and observations; these models fitted the data very well and the inferences based on estimate were very consistent with biological findings. Furthermore, the models reflected the complex nature of carcinogenesis. We notice that many cancers are developed through multiple-stage multiple-pathway. Our analyses of colon cancer and liver cancer have showed that some pathways are more devastated than others. This suggests thus it would be more efficient to intervene or treat the critical events in the more devastated pathways

A new stochastic and state space model of human colon cancer incorporating multiple pathways

<p>Abstract</p> <p>Background and Purpose</p> <p>Studies by molecular biologists and geneticists have shown that tumors of human colon cancer are developed from colon stem cells through two mechanisms: The chromosomal instability and the micro-satellite instability. The purpose of this paper is therefore to develop a new stochastic and state space model for carcinogenesis of human colon cancer incorporating these biological mechanisms.</p> <p>Results</p> <p>Based on recent biological studies, in this paper we have developed a state space model for human colon cancer. In this state space model, the stochastic system is represented by a stochastic model, involving 2 different pathways-the chromosomal instability pathway and the micro-satellite instability pathway; the observation, cancer incidence data, is represented by a statistical model. Based on this model we have developed a generalized Bayesian approach to estimate the parameters through the posterior modes of the parameters via Gibbs sampling procedures. We have applied this model to fit and analyze the SEER data of human colon cancers from NCI/NIH.</p> <p>Conclusions</p> <p>Our results indicate that the model not only provides a logical avenue to incorporate biological information but also fits the data much better than other models including the 4-stage single pathway model. This model not only would provide more insights into human colon cancer but also would provide useful guidance for its prevention and control and for prediction of future cancer cases.</p> <p>Reviewers</p> <p>This article was reviewed by M.P. Little and M. Kimmel</p

Number and Size Distribution of Colorectal Adenomas under the Multistage Clonal Expansion Model of Cancer

Colorectal cancer (CRC) is believed to arise from mutant stem cells in colonic crypts that undergo a well-characterized progression involving benign adenoma, the precursor to invasive carcinoma. Although a number of (epi)genetic events have been identified as drivers of this process, little is known about the dynamics involved in the stage-wise progression from the first appearance of an adenoma to its ultimate conversion to malignant cancer. By the time adenomas become endoscopically detectable (i.e., are in the range of 1–2 mm in diameter), adenomas are already comprised of hundreds of thousands of cells and may have been in existence for several years if not decades. Thus, a large fraction of adenomas may actually remain undetected during endoscopic screening and, at least in principle, could give rise to cancer before they are detected. It is therefore of importance to establish what fraction of adenomas is detectable, both as a function of when the colon is screened for neoplasia and as a function of the achievable detection limit. To this end, we have derived mathematical expressions for the detectable adenoma number and size distributions based on a recently developed stochastic model of CRC. Our results and illustrations using these expressions suggest (1) that screening efficacy is critically dependent on the detection threshold and implicit knowledge of the relevant stem cell fraction in adenomas, (2) that a large fraction of non-extinct adenomas remains likely undetected assuming plausible detection thresholds and cell division rates, and (3), under a realistic description of adenoma initiation, growth and progression to CRC, the empirical prevalence of adenomas is likely inflated with lesions that are not on the pathway to cancer

Models of HPV as an Infectious Disease and as an Etiological Agent of Cancer.

The human papillomavirus (HPV) infects multiple sites in the human epithelium and is the etiological agent for over 90% of anogenital cancers and an increasing percentage of oropharyngeal cancers. HPV presents an inherently multiscale problem: disease prevalence is known at the population level, infection and disease progression occur within an individual, and cancer incidence is given again for the population. This dissertation uses several mathematical models (of prevalence, transmission, and the progression to oral cancer) to address HPV at different levels. Using data from the National Health and Nutrition Examination Survey, I assess trends in prevalence of cervical HPV and seroprevalence of HPV antibodies using age-period-cohort (APC) epidemiological models that seek to differentiate between the temporal effects of age, period, and birth cohort. Additionally, I consider demographic (age, race) variation in concurrence and type-concordance of genital and oral infections and serum antibodies and the impact of vaccination on seroprevalence and genital prevalence among women. To study the dynamics of HPV transmission and infection, I develop a multisite transmission model that includes consideration of autoinoculation. Assuming homogeneous contacts, I analyze the basic reproductive number R0, as well as type and target reproduction numbers, for a two-site model. I find R0 takes the maximum of certain next generation matrix terms or takes their geometric average in certain limiting cases, and heterogeneity in the same-site terms increases R0 while heterogeneity in the cross-site terms decreases it. I extend this analysis to a heterosexual population, which yields dynamics analogous to those of vector-host models. Finally, I leverage multistage clonal expansion (MSCE) models of cancer biology coupled with APC models to analyze oral squamous cell carcinoma data in the Surveillance, Epidemiology, and End Results cancer registry. MSCE models are based on the initiation-promotion-malignant conversion paradigm in carcinogenesis. I find that HPV-related, HPV-unrelated, and oral tongue sites are best described by placing period and cohort effects on the initiation rate. Racial differences in estimated biological parameters as well as period and cohort trends are considered. To connect HPV prevalence to incidence of oral cancer, I develop MSCE models that use initiation rates dependent on HPV prevalence.PhDApplied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111523/1/brouweaf_1.pd

Mathematical modeling of Lynch syndrome carcinogenesis

Cancer is one of the leading causes of disease-related death worldwide. In recent years, large amounts of data on cancer genetics and molecular characteristics have become available and accumulated with increasing speed. However, the current understanding of cancer as a disease is still limited by the lack of suitable models that allow interpreting these data in proper ways. Thus, the highly interdisciplinary research field of mathematical oncology has evolved to use mathematics, modeling, and simulations to study cancer with the overall goal to improve clinical patient care. This dissertation aims at developing mathematical models and tools for different spatial scales of cancer development at the example of colorectal cancer in Lynch syndrome, the most common inherited colorectal cancer predisposition syndrome. We derive model-driven approaches for carcinogenesis at the DNA, cell, and crypt level, as well as data-driven methods for cancer-immune interactions at the DNA level and for the evaluation of diagnostic procedures at the Lynch syndrome population level. The developed models present an important step toward an improved understanding of hereditary cancer as a disease aiming at rapid implementation into clinical management guidelines and into the development of novel, innovative approaches for prevention and treatment