Mathematical bridge between epidemiological and molecular data on cancer and beyond

Abstract

BackgroundAt least six different mathematical models of cancer and their count-less variations and combinations have been published to date in the scientific literature that reasonably explain epidemiological prediction of multi-step carcinogenesis. Each one deals with a particular set of problems at a given organizational level ranging from populations to genes. Any of the models adopted in those articles so far do not account for both epidemiological and molecular levels of carcinogenesis. MethodsWe have developed a mathematically rigorous system to derive those equations satisfying the basic assumptions of both epidemiology and molecular biology without incorporating arbitrary numerical coefficients or constants devoid of any causal explanation just to fit the empirical data. The dataset we have used encompasses 21 major categories of cancer, 124 selected populations, 108 cancer registries, 5 continents, and 14,067,894 individual cases. ResultsWe generalized all the epidemiological and molecular data using our derived equations through linear and non-linear regression and found all the necessary coefficients to explain the data. We also tested our equations against non-neoplastic conditions satisfying equivalent mathematical assumptions. ConclusionThe aim of this treatise is not only to provide some novel insight into the mathematical modeling of malignant transformation but also to revive the classical tools we already have at our disposal to pave the way towards novel insight into integrated approaches in cancer research.