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Weibull-like Model of Cancer Development in Aging

Mathematical modeling of cancer development is aimed at assessing the risk factors leading to cancer. Aging is a common risk factor for all adult cancers. The risk of getting cancer in aging is presented by a hazard function that can be estimated from the observed incidence rates collected in cancer...

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Autores principales: Mdzinarishvili, Tengiz, Sherman, Simon
Formato: Texto
Lenguaje:English
Publicado: Libertas Academica 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935819/
https://www.ncbi.nlm.nih.gov/pubmed/20838610
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author Mdzinarishvili, Tengiz
Sherman, Simon
author_facet Mdzinarishvili, Tengiz
Sherman, Simon
author_sort Mdzinarishvili, Tengiz
collection PubMed
description Mathematical modeling of cancer development is aimed at assessing the risk factors leading to cancer. Aging is a common risk factor for all adult cancers. The risk of getting cancer in aging is presented by a hazard function that can be estimated from the observed incidence rates collected in cancer registries. Recent analyses of the SEER database show that the cancer hazard function initially increases with the age, and then it turns over and falls at the end of the lifetime. Such behavior of the hazard function is poorly modeled by the exponential or compound exponential-linear functions mainly utilized for the modeling. In this work, for mathematical modeling of cancer hazards, we proposed to use the Weibull-like function, derived from the Armitage-Doll multistage concept of carcinogenesis and an assumption that number of clones at age t developed from mutated cells follows the Poisson distribution. This function is characterized by three parameters, two of which (r and λ) are the conventional parameters of the Weibull probability distribution function, and an additional parameter (C(0)) that adjusts the model to the observational data. Biological meanings of these parameters are: r—the number of stages in carcinogenesis, λ—an average number of clones developed from the mutated cells during the first year of carcinogenesis, and C(0)—a data adjustment parameter that characterizes a fraction of the age-specific population that will get this cancer in their lifetime. To test the validity of the proposed model, the nonlinear regression analysis was performed for the lung cancer (LC) data, collected in the SEER 9 database for white men and women during 1975–2004. Obtained results suggest that: (i) modeling can be improved by the use of another parameter A- the age at the beginning of carcinogenesis; and (ii) in white men and women, the processes of LC carcinogenesis vary by A and C(0), while the corresponding values of r and λ are nearly the same. Overall, the proposed Weibull-like model provides an excellent fit of the estimates of the LC hazard function in aging. It is expected that the Weibull-like model can be applicable to fit estimates of hazard functions of other adult cancers as well.
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spelling pubmed-29358192010-09-13 Weibull-like Model of Cancer Development in Aging Mdzinarishvili, Tengiz Sherman, Simon Cancer Inform Original Research Mathematical modeling of cancer development is aimed at assessing the risk factors leading to cancer. Aging is a common risk factor for all adult cancers. The risk of getting cancer in aging is presented by a hazard function that can be estimated from the observed incidence rates collected in cancer registries. Recent analyses of the SEER database show that the cancer hazard function initially increases with the age, and then it turns over and falls at the end of the lifetime. Such behavior of the hazard function is poorly modeled by the exponential or compound exponential-linear functions mainly utilized for the modeling. In this work, for mathematical modeling of cancer hazards, we proposed to use the Weibull-like function, derived from the Armitage-Doll multistage concept of carcinogenesis and an assumption that number of clones at age t developed from mutated cells follows the Poisson distribution. This function is characterized by three parameters, two of which (r and λ) are the conventional parameters of the Weibull probability distribution function, and an additional parameter (C(0)) that adjusts the model to the observational data. Biological meanings of these parameters are: r—the number of stages in carcinogenesis, λ—an average number of clones developed from the mutated cells during the first year of carcinogenesis, and C(0)—a data adjustment parameter that characterizes a fraction of the age-specific population that will get this cancer in their lifetime. To test the validity of the proposed model, the nonlinear regression analysis was performed for the lung cancer (LC) data, collected in the SEER 9 database for white men and women during 1975–2004. Obtained results suggest that: (i) modeling can be improved by the use of another parameter A- the age at the beginning of carcinogenesis; and (ii) in white men and women, the processes of LC carcinogenesis vary by A and C(0), while the corresponding values of r and λ are nearly the same. Overall, the proposed Weibull-like model provides an excellent fit of the estimates of the LC hazard function in aging. It is expected that the Weibull-like model can be applicable to fit estimates of hazard functions of other adult cancers as well. Libertas Academica 2010-08-24 /pmc/articles/PMC2935819/ /pubmed/20838610 Text en © 2010 the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.
spellingShingle Original Research
Mdzinarishvili, Tengiz
Sherman, Simon
Weibull-like Model of Cancer Development in Aging
title Weibull-like Model of Cancer Development in Aging
title_full Weibull-like Model of Cancer Development in Aging
title_fullStr Weibull-like Model of Cancer Development in Aging
title_full_unstemmed Weibull-like Model of Cancer Development in Aging
title_short Weibull-like Model of Cancer Development in Aging
title_sort weibull-like model of cancer development in aging
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935819/
https://www.ncbi.nlm.nih.gov/pubmed/20838610
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