Abstract PL04-03: Cancer stem cells

Abstract

Abstract The existence of cancer stem cells (CSCs) as a small fraction of tumor cells responsible for driving the malignancy is a heavily debated topic in cancer research. Although plenty of experimental evidence suggests that tumors are maintained by CSCs (1,2), those experiments are often criticized for the difficulty of their interpretation (3). The cancer stem cell paradigm, if true, would have immense implications in cancer research, in particular regarding our understanding of relapse, metastasis and therapy resistance. In this scenario, mathematical modeling could play an important role in providing evidence of the (non-) existence of cancer stem cells in human tumors. Using mathematical analysis, studies have already shown that the CSC paradigm induces fundamental alterations in the dynamics of growth and progression of tumors, both in terms of invasive patterns and heterogeneity (4). Certainly, the organization of a malignant clone, whether it is based on CSCs or not, is reflected in its phylogenetic structure, starting from the founding tumor cell. This phylogenetic information is, in turn, carried by cancer cells as a landscape of neutral DNA mutations, such as microsatellites (5) or methylation errors (6). These alterations represent a molecular clock that reflects the processes of cell proliferation and differentiation. By integrating this information with mathematical models of phylogenetic structure it is then possible to infer fundamental properties of the organization of tumors (7) and premalignant tissues (6,8,9). To model phylogenetic trees, several mathematical and computational models have been developed. The approaches vary from clustering-based analyses (10), forward simulation of the phylogenetic structure (11) or so-called coalescence, or backward, simulation, reviewed in (12). These novel techniques integrate in vivo molecular cancer data with mathematical modeling and statistical inference methods (12), with the aim of inferring biological parameters of the system under consideration. As a result it is possible to perform indirect in silico measurements on the virtual biological system. These measurements are then automatically validated against molecular data using statistical techniques. In terms of cancer prevention, this approach has already demonstrated its potential in revealing important characteristics of the colon crypt as the unit of origin of colorectal cancer (6,8,9). We propose that these methods could be used to investigate neoplastic and metaplastic stages of tumors with the goal of understanding the role played by cancer stem cells in carcinogenesis.