Can a minimal replicating construct be identified as the embodiment of cancer?

Genetic instability as a driver for immune surveillance

*: BackgroundGenetic instability is known to relate with carcinogenesis by providing tumors with a mechanism for fast adaptation. However, mounting evidence also indicates causal relation between genetic instability and improved cancer prognosis resulting from efficient immune response. Highly unstable tumors seem to accumulate mutational burdens that result in dynamical landscapes of neoantigen production, eventually inducing acute immune recognition. How are tumor instability and enhanced immune response related? An important step towards future developments involving combined therapies would benefit from unraveling this connection. *: MethodsIn this paper we present a minimal mathematical model to describe the ecological interactions that couple tumor adaptation and immune recognition while making use of available experimental estimates of relevant parameters. The possible evolutionary trade-offs associated to both cancer replication and T cell response are analysed, and the roles of mutational load and immune activation in governing prognosis are studied. *: ResultsModeling and available data indicate that cancer-clearance states become attainable when both mutational load and immune migration are enhanced. Furthermore, the model predicts the presence of well-defined transitions towards tumor control and eradication after increases in genetic instability numerically consistent with recent experiments of tumor control after Mismatch Repair knockout in mice. *: ConclusionsThese two main results indicate a potential role of genetic instability as a driver of transitions towards immune control of tumors, as well as the effectiveness of increasing mutational loads prior to adoptive cell therapies. This mathematical framework is therefore a quantitative step towards predicting the outcomes of combined therapies where genetic instability might play a key role.

Adaptive dynamics of unstable cancer populations: The canonical equation

In most instances of tumour development, genetic instability plays a role in allowing cancer cell populations to respond to selection barriers, such as physical constraints or immune responses, and rapidly adapt to an always changing environment. Modelling instability is a nontrivial task, since by definition evolving instability leads to changes in the underlying landscape. In this article, we explore mathematically a simple version of unstable tumour progression using the formalism of adaptive dynamics (AD) where selection and mutation are explicitly coupled. Using a set of basic fitness landscapes, the so-called canonical equation for the evolution of genetic instability on a minimal scenario associated with a population of unstable cells is derived. We obtain explicit expressions for the evolution of mutation probabilities, and the implications of the model on further experimental studies and potential mutagenic therapies are discussed.

Noise-induced bistability in the fate of cancer phenotypic quasispecies: a bit-strings approach

Tumor cell populations are highly heterogeneous. Such heterogeneity, both at genotypic and phenotypic levels, is a key feature during tumorigenesis. How to investigate the impact of this heterogeneity in the dynamics of tumors cells becomes an important issue. Here we explore a stochastic model describing the competition dynamics between a pool of heterogeneous cancer cells with distinct phenotypes and healthy cells. This model is used to explore the role of demographic fluctuations on the transitions involving tumor clearance. Our results show that for large population sizes, when demographic fluctuations are negligible, there exists a sharp transition responsible for tumor cells extinction at increasing tumor cells' mutation rates. This result is consistent with a mean field model developed for the same system. The mean field model reveals only monostability scenarios, in which either the dominance of the tumor cells or the dominance of the healthy cells is found. Interestingly, the stochastic model shows that for small population sizes the monostability behavior disappears, involving the presence of noise-induced bistability. The impact of the initial populations of cells in the fate of the cell populations is investigated, as well as the transient times towards the healthy and the cancer states.

Abrupt transitions to tumor extinction: a phenotypic quasispecies model

The dynamics of heterogeneous tumor cell populations competing with healthy cells is an important topic in cancer research with deep implications in biomedicine. Multitude of theoretical and computational models have addressed this issue, especially focusing on the nature of the transitions governing tumor clearance as some relevant model parameters are tuned. In this contribution, we analyze a mathematical model of unstable tumor progression using the quasispecies framework. Our aim is to define a minimal model incorporating the dynamics of competition between healthy cells and a heterogeneous population of cancer cell phenotypes involving changes in replication-related genes (i.e., proto-oncogenes and tumor suppressor genes), in genes responsible for genomic stability, and in house-keeping genes. Such mutations or loss of genes result into different phenotypes with increased proliferation rates and/or increased genomic instabilities. Despite bifurcations in the classical deterministic quasispecies model are typically given by smooth, continuous shifts (i.e., transcritical bifurcations), we here identify a novel type of bifurcation causing an abrupt transition to tumor extinction. Such a bifurcation, named as trans-heteroclinic, is characterized by the exchange of stability between two distant fixed points (that do not collide) involving tumor persistence and tumor clearance. The increase of mutation and/or the decrease of the replication rate of tumor cells involves this catastrophic shift of tumor cell populations. The transient times near bifurcation thresholds are also characterized, showing a power law dependence of exponent [Formula: see text] of the transients as mutation is changed near the bifurcation value. These results are discussed in the context of targeted cancer therapy as a possible therapeutic strategy to force a catastrophic shift by simultaneously delivering mutagenic and cytotoxic drugs inside tumor cells.

Special Technologies for Ex Vivo Analysis of Cancer

BACKGROUND

Predictive assays for cancer treatment are not new technology, but they have failed to meet the criteria necessary for standardized use in clinical decision-making.

METHODS

The authors summarize the use of predictive assays and the challenges and values associated with these assays in the clinical setting.

RESULTS

Predictive assays commercially available in the clinical setting are not standardized, have significant obstacles to overcome, and cannot be relied upon by health care professionals due to the limited value these assays provide to the decision-making process for the treatment of patients.

CONCLUSIONS

A method that more closely recapitulates the human tumor microenvironment and accurately predicts response with high reproducibility would be beneficial to patient outcomes and quality of life.

The reverse evolution from multicellularity to unicellularity during carcinogenesis

Theoretical reasoning suggests that cancer may result from a knockdown of the genetic constraints that evolved for the maintenance of metazoan multicellularity. By characterizing the whole-life history of a xenograft tumour, here we show that metastasis is driven by positive selection for general loss-of-function mutations on multicellularity-related genes. Expression analyses reveal mainly downregulation of multicellularity-related genes and an evolving expression profile towards that of embryonic stem cells, the cell type resembling unicellular life in its capacity of unlimited clonal proliferation. Also, the emergence of metazoan multicellularity ~600 Myr ago is accompanied by an elevated birth rate of cancer genes, and there are more loss-of-function tumour suppressors than activated oncogenes in a typical tumour. These data collectively suggest that cancer represents a loss-of-function-driven reverse evolution back to the unicellular 'ground state'. This cancer evolution model may account for inter-/intratumoural genetic heterogeneity, could explain distant-organ metastases and hold implications for cancer therapy.

Theories of Lethal Mutagenesis: From Error Catastrophe to Lethal Defection

RNA viruses get extinct in a process called lethal mutagenesis when subjected to an increase in their mutation rate, for instance, by the action of mutagenic drugs. Several approaches have been proposed to understand this phenomenon. The extinction of RNA viruses by increased mutational pressure was inspired by the concept of the error threshold. The now classic quasispecies model predicts the existence of a limit to the mutation rate beyond which the genetic information of the wild type could not be efficiently transmitted to the next generation. This limit was called the error threshold, and for mutation rates larger than this threshold, the quasispecies was said to enter into error catastrophe. This transition has been assumed to foster the extinction of the whole population. Alternative explanations of lethal mutagenesis have been proposed recently. In the first place, a distinction is made between the error threshold and the extinction threshold, the mutation rate beyond which a population gets extinct. Extinction is explained from the effect the mutation rate has, throughout the mutational load, on the reproductive ability of the whole population. Secondly, lethal defection takes also into account the effect of interactions within mutant spectra, which have been shown to be determinant for the understanding the extinction of RNA virus due to an augmented mutational pressure. Nonetheless, some relevant issues concerning lethal mutagenesis are not completely understood yet, as so survival of the flattest, i.e. the development of resistance to lethal mutagenesis by evolving towards mutationally more robust regions of sequence space, or sublethal mutagenesis, i.e., the increase of the mutation rate below the extinction threshold which may boost the adaptability of RNA virus, increasing their ability to develop resistance to drugs (including mutagens). A better design of antiviral therapies will still require an improvement of our knowledge about lethal mutagenesis.

What Is a Quasispecies? Historical Origins and Current Scope

The quasispecies concept is introduced by means of a simple theoretical model that uses as little chemical kinetics and mathematics as possible but fully in the spirit of Albert Einstein who said: "Things should be made as simple as possible but not simpler." More elaborate treatments follow in the forthcoming chapters. It is shown that the most important results of the theory, in particular the existence of error thresholds, are not dependent on simplifying assumptions concerning the distribution of fitness values. Error thresholds are regularly found on landscapes with large and irregular scatter of fitness. After the introduction to theory, it will be shown how experimental data on the evolution of molecules or viruses may be fit to the theoretical model.