Cancer resistance and Peto's paradox

Is cancer a disease set up by cellular stress responses?

For several decades, the somatic mutation theory (SMT) has been the dominant paradigm on cancer research, leading to the textbook notion that cancer is fundamentally a genetic disease. However, recent discoveries indicate that mutations, including "oncogenic" ones, are widespread in normal somatic cells, suggesting that mutations may be necessary but not sufficient for cancer to develop. Indeed, a fundamental but as yet unanswered question is whether or not the first step in oncogenesis corresponds to a mutational event. On the other hand, for some time, it has been acknowledged the important role in cancer progression of molecular processes that participate in buffering cellular stress. However, their role is considered secondary or complementary to that of putative oncogenic mutations. Here we present and discuss evidence that cancer may have its origin in epigenetic processes associated with cellular adaptation to stressful conditions, and so it could be a direct consequence of stress-buffering mechanisms that allow cells with aberrant phenotypes (not necessarily associated with genetic mutations) to survive and propagate within the organism. We put forward the hypothesis that there would be an inverse correlation between the activation threshold of the cellular stress responses (CSRs) and the risk of cancer, so that species or individuals with low-threshold CSRs will display a higher incidence or risk of cancer.

MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response

BACKGROUND

There is an increasing need for complex computational models to perform in silico experiments as an adjunct to in vitro and in vivo experiments in immunology. We introduce Microscopic Stochastic Immune System Simulator (MiStImm), an agent-based simulation tool, that is designed to study the self-nonself discrimination of the adaptive immune system. MiStImm can simulate some components of the humoral adaptive immune response, including T cells, B cells, antibodies, danger signals, interleukins, self cells, foreign antigens, and the interactions among them. The simulation starts after conception and progresses step by step (in time) driven by random simulation events. We also have provided tools to visualize and analyze the output of the simulation program.

RESULTS

As the first application of MiStImm, we simulated two different immune models, and then we compared performances of them in the mean of self-nonself discrimination. The first model is a so-called conventional immune model, and the second model is based on our earlier T-cell model, called "one-signal model", which is developed to resolve three important paradoxes of immunology. Our new T-cell model postulates that a dynamic steady state coupled system is formed through low-affinity complementary TCR-MHC interactions between T cells and host cells. The new model implies that a significant fraction of the naive polyclonal T cells is recruited into the first line of defense against an infection. Simulation experiments using MiStImm have shown that the computational realization of the new model shows real patterns. For example, the new model develops immune memory and it does not develop autoimmune reaction despite the hypothesized, enhanced TCR-MHC interaction between T cells and self cells. Simulations also demonstrated that our new model gives better results to overcome a critical primary infection answering the paradox "how can a tiny fraction of human genome effectively compete with a vastly larger pool of mutating pathogen DNA?"

CONCLUSION

The outcomes of our in silico experiments, presented here, are supported by numerous clinical trial observations from the field of immunotherapy. We hope that our results will encourage investigations to make in vitro and in vivo experiments clarifying questions about self-nonself discrimination of the adaptive immune system. We also hope that MiStImm or some concept in it will be useful to other researchers who want to implement or compare other immune models.

Association of TGFβ signaling with the maintenance of a quiescent stem cell niche in human oral mucosa

A dogma in squamous epithelial biology is that proliferation occurs in the basal cell layer. Notable exceptions are squamous epithelia of the human oral cavity, esophagus, ectocervix, and vagina. In these human epithelia, proliferation is rare in the basal cell layer, and the vast majority of cells positive for Ki67 and other proliferation markers are found in para- and suprabasal cell layers. This unique human feature of a generally quiescent basal cell layer overlaid by highly proliferative cells offers the rare opportunity to study the molecular features of undifferentiated, quiescent, putative stem cells in their natural context. Here, we show that the quiescent human oral mucosa basal cell layer expresses putative markers of stemness, while para- and suprabasal cells are characterized by cell cycle genes. We identified a TGFβ signature in this quiescent basal cell layer. In in vitro organotypic cultures, human keratinocytes could be induced to express markers of these quiescent basal cells when TGFβ signaling is activated. The study suggests that the separation of basal cell layer and proliferation in human oral mucosa may function to accommodate high proliferation rates and the protection of a quiescent reserve stem cell pool. Psoriasis, an epidermal inflammatory hyperproliferative disease, exhibits features of a quiescent basal cell layer mimicking normal oral mucosa. Our data indicate that structural changes in the organization of epithelial proliferation could contribute to longevity and carcinogenesis.

Cell size and cancer: a new solution to Peto's paradox?

Cancer, one of the leading health concerns for humans, is by no means a human-unique malady. Accumulating evidence shows that cancer kills domestic and wild animals at a similar rate to humans and can even pose a conservation threat to certain species. Assuming that each physiologically active and proliferating cell is at risk of malignant transformation, any evolutionary increase in the number of cells (and thus body mass) will lead to a higher cancer frequency, all else being equal. However, available data fail to support the prediction that bigger animals are affected by cancer more than smaller ones. The unexpected lack of correlation between body size (and life span) and cancer risk across taxa was dubbed Peto's paradox. In this perspective, several plausible explanations of Peto's paradox are presented, with the emphasis on a largely underappreciated relation of cell size to both metabolism and cell division rates across species, which we believe are key factors underlying the paradox. We conclude that larger organisms have bigger and slowly dividing cells with lower energy turnover, all significantly reducing the risk of cancer initiation. Solving Peto's paradox will enhance our understanding the evolution of cancer and may provide new implications for cancer prevention and treatment.

Large-scale spatial population databases in infectious disease research

Modelling studies on the spatial distribution and spread of infectious diseases are becoming increasingly detailed and sophisticated, with global risk mapping and epidemic modelling studies now popular. Yet, in deriving populations at risk of disease estimates, these spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data. Moreover, a variety of different methods have been used to model population distribution at large spatial scales. In this review we describe the main global gridded population datasets that are freely available for health researchers and compare their construction methods, and highlight the uncertainties inherent in these population datasets. We review their application in past studies on disease risk and dynamics, and discuss how the choice of dataset can affect results. Moreover, we highlight how the lack of contemporary, detailed and reliable data on human population distribution in low income countries is proving a barrier to obtaining accurate large-scale estimates of population at risk and constructing reliable models of disease spread, and suggest research directions required to further reduce these barriers.

SUFFICIENT TO RECOGNIZE SELF TO ATTACK NON-SELF: BLUEPRINT FOR A ONE-SIGNAL T CELL MODEL

Current consensus postulates that the class I-antigen processing system is evolved to present microbial antigens to specific T cells. Since such cells are rare and short-lived, they require three to five days to attain fighting strength. During this critical period he innate immune system holds back the briskly multiplying pathogens. Nevertheless, a T cell response is measurable in the lymph nodes draining the infection site within 12 to 18 h. In order to explain this paradox here we suggest a new T cell model. This is based on the observation that T cells require continuous contact of the T cell receptor (TCR) with selecting self-peptide–major histocompatibility complex (MHC) molecules in the periphery for their survival. We postulate that a dynamic steady state, a so-called coupled system is formed through low affinity complementary TCR–MHC interactions between T cells and host cells. Under such condition it is sufficient to recognize what is self in order to attack what is not self. A coupled system is regulated via soluble forms of peptide–MHC and TCR molecules by the law of mass action. In a coupled system one signal is sufficient for T cell activation. The new model implies that a significant fraction of the naive polyclonal T cells are recruited into the first line of defense from the very outset of an infection, so the number of activated T cells is increased by several orders of magnitude compared to conventional models. The one-signal model also predicts that therapeutic administration of soluble agonist or antagonist T cell receptor ligands may be able to fine tune the homeostatic physiological regulatory mechanism and thus improve the treatment of some chronic diseases such as metastatic cancer, HIV/AIDS, and transplantation.

Cancer: more of polygenic disease and less of multiple mutations? A quantitative viewpoint

The focus of cancer research is on cancer-specific mutations, with most clinical trials involving targeted drugs. Huge numbers of DNA lesions and tumor resistance events, in each of the >10¹³ cells of a human individual, form a striking contrast to the low, and also very narrow, cancer incidence window (10⁻¹ -10⁰). A detailed consideration of these quantitative observations seems to question the present paradigm, while suggesting that a systemic regulatory network mechanism is a stronger determinant for overt cancer disease, as compared with cancer-specific gene products. If we shall ever achieve major improvements in survival, we must gain understanding of this systemic network, rather than targeting therapy to a limited set of molecules or mutations. This may give us new opportunities for development of highly potent therapeutic tools.