Abstract B120: Capturing connection between aging and aging-associated diseases: The case of cancer

Abstract

Abstract B120 Traditional epidemiological studies of cancer often approximate age patterns of the incidence and mortality rates by appropriate parametric functions. Although such formal description is certainly convenient from the demographic point of view it does not allow for biological interpretation of the respective parameters, and, therefore, has limited use beyond demographic applications. The idea to describe cancer hazard rates in terms of biologically interpretable parameters resulted in the development of the research area called the “mathematical modeling of cancer” or “cancer modeling.” These models, however, do not allow for studying either connection between health history events, or links between cancer and aging, detected in a number of recent molecular biological and experimental studies. To take such connections into account, the new models of cancer are needed. In this paper, we suggest a new approach to modeling health history events associated with cancer and aging. The events include the onset of cancer, death from causes different from cancer, and death of individuals with and without cancer. Besides, this approach allows for taking into account unobservable precancerous changes in tissues, as well as deterioration in individuals’ health associated with the aging related processes. An attractive feature of this model is that all functional forms for the population characteristics can be derived analytically. The model also allows for studying a possible trade-off between two lines of organisms’ defense against a disease. The first is responsible for its vulnerability (robustness) and affects the age of disease onset. The second deals with organism’s resilience, i.e., its ability of coping with disease when it strikes. It affects chances of recovery, as well as duration of staying in an unhealthy state. The model allows for testing hypotheses about possibility of trade-off between these two defense strategies. The derived model was tested by fitting it to the combined data set consisting of the Surveillance, Epidemiology and End Results (SEER) dataset on cancer incidence and case-fatality data in the U.S. for different types of cancer and the U.S. overall mortality data taken from the Human Mortality Database (HMD). The results of the analyses show that the approach captures non-monotonic age patterns of cancer incidence and case fatality rates. The developed approach is capable of explaining links among health history data and provides useful insights on mechanisms of cancer occurrence, disease progression, other aging related changes, and mortality. The results also indicate that parameters, characterizing individual aging rate, may depend on type of cancer developing in body. Further developments of this approach are discussed. Citation Information: Cancer Prev Res 2008;1(7 Suppl):B120.