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Multistage Carcinogenesis: A Unified Framework for Cancer Data Analysis

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Statistical Modeling for Biological Systems

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Abstract

Traditional approaches to the analysis of epidemiologic data are focused on estimation of the relative risk and are based on the proportional hazards model. Proportionality of hazards in epidemiologic data is a strong assumption that is often violated but seldom checked. Risk often depends on detailed patterns of exposure to environmental agents, but detailed exposure histories are difficult to incorporate in the traditional approaches to analyses of epidemiologic data. For epidemiologic data on cancer, an alternative approach to analysis can be based on ideas of multistage carcinogenesis. The process of carcinogenesis is characterized by mutation accumulation and clonal expansion of partially altered cells on the pathway to cancer. Although this paradigm is now firmly established, most epidemiologic studies of cancer incorporate ideas of multistage carcinogenesis neither in their design nor in their analyses. In this paper we will briefly discuss stochastic multistage models of carcinogenesis and the construction of the appropriate likelihoods for analyses of epidemiologic data using these models. Statistical analyses based on multistage models can quite explicitly incorporate detailed exposure histories in the construction of the likelihood. We will give examples to show that using ideas of multistage carcinogenesis can help reconcile seemingly contradictory findings, and yield insights into epidemiologic studies of cancer that would be difficult or impossible to get from conventional methods. Finally, multistage cancer models provide a unified framework for analyses of data from diverse sources.

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Notes

  1. 1.

    Parameters would be expected to return to background levels with exposure to agents, such as benzene, that are rapidly cleared from the body. Other agents, such as amphibole asbestos, accumulate in tissues, and after exposure to such agents stops, the parameters of the model could remain altered for a long period of time and would be expected to return to background levels only slowly as the agent is excreted from tissues.

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Author information

Authors and Affiliations

  1. Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Suresh Moolgavkar

  2. Exponent, Inc., Bellevue, WA, USA

    Suresh Moolgavkar

  3. Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Georg Luebeck

Authors
  1. Suresh Moolgavkar
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  2. Georg Luebeck
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Editors and Affiliations

  1. Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA

    Anthony Almudevar

  2. Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA

    David Oakes

  3. Rochester, NY, USA

    Jack Hall

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Moolgavkar, S., Luebeck, G. (2020). Multistage Carcinogenesis: A Unified Framework for Cancer Data Analysis. In: Almudevar, A., Oakes, D., Hall, J. (eds) Statistical Modeling for Biological Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-34675-1_7

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