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Compiled data for the stem cell numbers, Ns, and division rates, ms, is reanalized in order to show that we can distinguish two groups of human tissues. In the first one, there is a relatively high fraction of maintenance (stem and transit) cells in the tissue, but the division rates are low. The second group, on the other hand, is characterized by very high transit cell division rates, of around one division per day. These groups do not have an embrionary origin. We argue that their properties arise from a combination of the needs of tissue homeostasis (in particular turnover rate) and a bound on cancer risk, which is roughly a linear function of the product Ns ms. The bound on cancer risk leads to a threshold at ms = 8/year, where the fraction of stem cells falls down two orders of magnitude.
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Since the discovery of a cancer initiating side population in solid tumours, studies focussing on the role of so-called cancer stem cells in cancer initiation and progression have abounded. The biological interrogation of these cells has yielded volumes of information about their behaviour, but there has, as of yet, not been many actionable generalised theoretical conclusions. To address this point, we have created a hybrid, discrete/continuous computational cellular automaton model of a generalised stem-cell driven tissue and explored the phenotypic traits inherent in the inciting cell and the resultant tissue growth. We identify the regions in phenotype parameter space where these initiating cells are able to cause a disruption in homeostasis, leading to tissue overgrowth and tumour formation. As our parameters and model are non-specific, they could apply to any tissue cancer stem-cell and do not assume specific genetic mutations. In this way, our model suggests that targeting these phenotypic traits could represent generalizable strategies across cancer types and represents a first attempt to identify the hallmarks of cancer stem cells.
The maintenance of the proliferative cell niche is critical to epithelial tissue morphology and function. In this paper we investigate how current modelling methods can result in the erroneous loss of proliferative cells from the proliferative cell niche. Using an established model of the inter-follicular epidermis we find there is a limit to the proliferative cell densities that can be maintained in the basal layer (the niche) if we do not include additional mechanisms to stop the loss of proliferative cells from the niche. We suggest a new methodology that enables maintenance of a desired homeostatic population of proliferative cells in the niche: a rotational force is applied to the two daughter cells during the mitotic phase of division to enforce a particular division direction. We demonstrate that this new methodology achieves this goal. This methodology reflects the regulation of the orientation of cell division.
Rapidly dividing tissues, like intestinal crypts, are frequently chosen to investigate the process of tumor initiation, because of their high rate of mutations. To study the interplay between normal and mutant as well as immortal cells in the human colon or intestinal crypt, we developed a 4-compartmental stochastic model for cell dynamics based on current discoveries. Recent studies of the intestinal crypt have revealed the existence of two stem cell groups. Therefore, our model incorporates two stem cell groups (central stem cells (CeSCs) and border stem cells (BSCs)), plus one compartment for transit amplifying (TA) cells and one compartment of fully differentiated (FD) cells. However, it can be easily modified to have only one stem cell group. We find that the worst-case scenario occurs when CeSCs are mutated, or an immortal cell arises in the TA or FD compartments. The probability that the progeny of a single advantageous CeSC mutant will take over the entire crypt is more than $0.2$, and one immortal cell always causes all FD cells to become immortals.Moreover, when CeSCs are either mutants or wild-type (w.t.) individuals, their progeny will take over the entire crypt in less than 100 days if there is no immortal cell. Unexpectedly, if the CeSCs are wild-type, then non-immortal mutants with higher fitness are washed out faster than those with lower fitness. Therefore, we suggest one potential treatment for colon cancer might be replacing or altering the CeSCs with the normal stem cells.
A principal component analysis of the TCGA data for 15 cancer localizations unveils the following qualitative facts about tumors: 1) The state of a tissue in gene expression space may be described by a few variables. In particular, there is a single variable describing the progression from a normal tissue to a tumor. 2) Each cancer localization is characterized by a gene expression profile, in which genes have specific weights in the definition of the cancer state. There are no less than 2500 differentially-expressed genes, which lead to power-like tails in the expression distribution functions. 3) Tumors in different localizations share hundreds or even thousands of differentially expressed genes. There are 6 genes common to the 15 studied tumor localizations. 4) The tumor region is a kind of attractor. Tumors in advanced stages converge to this region independently of patient age or genetic variability. 5) There is a landscape of cancer in gene expression space with an approximate border separating normal tissues from tumors.
Environmental and genetic mutations can transform the cells in a co-operating healthy tissue into an ecosystem of individualistic tumour cells that compete for space and resources. Various selection forces are responsible for driving the evolution of cells in a tumour towards more malignant and aggressive phenotypes that tend to have a fitness advantage over the older populations. Although the evolutionary nature of cancer has been recognised for more than three decades (ever since the seminal work of Nowell) it has been only recently that tools traditionally used by ecological and evolutionary researchers have been adopted to study the evolution of cancer phenotypes in populations of individuals capable of co-operation and competition. In this chapter we will describe game theory as an important tool to study the emergence of cell phenotypes in a tumour and will critically review some of its applications in cancer research. These applications demonstrate that game theory can be used to understand the dynamics of somatic cancer evolution and suggest new therapies in which this knowledge could be applied to gain some control over the evolution of the tumour.
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