Résultats de recherche pour « Carcinogenesis »

Any process in which competing solutions replicate with errors and numbers of their copies depend on their respective fitnesses is the evolutionary optimization process. As during carcinogenesis mutated genomes replicate according to their respective qualities, carcinogenesis obviously qualifies as the evolutionary optimization process and conforms to common mathematical basis. The optimization view accents statistical nature of carcinogenesis proposing that during it the crucial role is actually played by the allocation of trials. Optimal allocation of trials requires reliable schemas' fitnesses estimations which necessitate appropriate, fitness landscape dependent, statistics of population. In the spirit of the applied conceptual framework, features which are known to decrease efficiency of any evolutionary optimization procedure (or inhibit it completely) are anticipated as "therapies" and reviewed. Strict adherence to the evolutionary optimization framework leads us to some counterintuitive implications which are, however, in agreement with recent experimental findings, such as sometimes observed more aggressive and malignant growth of therapy surviving cancer cells.

Carcinogenesis is a complex process that involves dynamically inter-connected modular sub-networks that evolve under the influence of micro-environmentally induced perturbations, in non-random, pseudo-Markov chain processes. An appropriate n-stage model of carcinogenesis involves therefore n-valued Logic treatments of nonlinear dynamic transformations of complex functional genomes and cell interactomes. Lukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous, Boolean or "fuzzy", logic models of genetic activities in vivo. Such models are then applied to cell transformations during carcinogenesis based on very extensive genomic transcription and translation data from the CGAP databases supported by NCI. Such models are represented in a Lukasiewicz-Topos with an n-valued Lukasiewicz Algebraic Logics subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in carcinogeness. Specific models for different types of cancer are then derived from representations of the dynamic state-space of LT non-random, pseudo-Markov chain process, network models in terms of cDNA and proteomic, high throughput analyses by ultra-sensitive techniques. This novel theoretical analysis is based on extensive CGAP genomic data for human tumors, as well as recently published studies of cyclin signaling. Several such specific models suggest novel clinical trials and rational therapies of cancer through re-establishment of cell cycling inhibition in stage III cancers.

A new approach to the modular, complex systems analysis of nonlinear dynamics in cell cycling network transformations involved in carcinogenesis is proposed. Carcinogenesis is a complex process that involves dynamically inter-connected biomolecules in the intercellular, membrane, cytosolic, nuclear and nucleolar compartments that form numerous inter-related pathways. One such family of pathways contains the cell cyclins. Cyclins are proteins that link several critical pro-apoptotic and other cell cycling/division components, including the tumor suppressor gene TP53 and its product, the Thomsen-Friedenreich antigen (T antigen), Rb, mdm2, c-Myc, p21, p27, Bax, Bad and Bcl-2, which all play major roles in neoplastic transformation of many tissues. This novel theoretical analysis based on recently published studies of cyclin signaling, with special emphasis placed on the roles of cyclins D1 and E, suggests novel clinical trials and rational therapies of cancer through reestablishment of cell cycling inhibition in metastatic cancer cells.

In this work we explore the temporal dynamics of spatial heterogeneity during the process of tumorigenesis from healthy tissue. We utilize a spatial stochastic process model of mutation accumulation and clonal expansion in a structured tissue to describe this process. Under a two-step tumorigenesis model, we first derive estimates of a non-spatial measure of diversity: Simpson's Index, which is the probability that two individuals sampled at random from the population are identical, in the premalignant population. We next analyze two new measures of spatial population heterogeneity. In particular we study the typical length scale of genetic heterogeneity during the carcinogenesis process and estimate the extent of a surrounding premalignant clone given a clinical observation of a premalignant point biopsy. This evolutionary framework contributes to a growing literature focused on developing a better understanding of the spatial population dynamics of cancer initiation and progression. Although initially motivated by understanding questions in cancer, these results can be applied more generally to help understand the dynamics of heterogeneity and diversity in a variety of spatially structured, evolving populations.

Alcohol consumption interferes with the functioning of multiple organ systems, causing changes in the chemistry, physiology and pathology of tissues and cellular organelles. Although epigenetic modifications underlie the development of cancer, exposure to carcinogenic chemicals, such as alcohol, can also contribute to disease development. However, the effects of chronic alcoholism on normal or pre-carcinogenic cells/tissues in different organelles are not well understood. Therefore, we herein study the effect of alcohol consumption on colonic nucleus using control and azoxymethane (AOM) and dextran sulfate sodium (DSS) treated carcinogenic mice. Previous studies showed that progression of carcinogenesis is associated with increase in the degree of intranuclear nanoscale structural disorder. In the present work, we quantify the degree of nanostructural disorder as a measure of carcinogenesis. To accomplish this, transmission electron microscopy (TEM) imaging of respective colonic epithelial cell nuclei are used to construct disordered optical lattices, and the properties of nanoscale disorder are then studied by analyzing the inverse participation ratio (IPR) of the spatially localized eigenfunctions of these optical lattices. Nanoscale structural disorder strength, as a marker of cancer progression, is measured in the length scale of 10 to 75 nm. Results show no significant visible effect in nanoscale structural changes on colon cell nuclei from alcohol exposure. However, alcohol was found to act as an enhancer of nanoscale disorder in precancerous cells and, hence, carcinogenic processes. To the best of our knowledge, this is the first study to quantify the effect of alcohol on early carcinogenic biological cells, using mesoscopic condensed matter physics.

There is a considerable debate between research groups applying the two stage clonal expansion model for lung cancer risk estimation, whether radon exposure affects initiation and transformation or promotion. The objective of the present study is to quantify the effects of radon progeny on these stages with biophysical models. For this purpose, numerical models of mutation induction and clonal growth were applied in order to estimate how initiation, transformation and promotion rates depend on tissue dose rate. It was found that rates of initiation and transformation increase monotonically with dose rate, while effective promotion rate decreases with time, but increases in a supralinear fashion with dose rate. Despite the uncertainty of the results due to the lack of experimental data, present study suggests that effects of radon exposure on both mutational events and clonal growth are significant, and should be considered in epidemiological analyses applying mathematical models of carcinogenesis.