Multifaceted Kinetics of Immuno-Evasion from Tumor Dormancy

Parameters Estimation in Phase-Space Landscape Reconstruction of Cell Fate: A Systems Biology Approach

The thermodynamical formalism of irreversible processes offers a theoretical framework appropriate to explain the complexity observed at the macroscopic level of dynamic systems. In this context, together with the theory of complex systems and systems biology, the thermodynamical formalism establishes an appropriate conceptual framework to address the study of biological systems, in particular cancer.The Chapter is organized as follows: In Subheading 1, an integrative view of these disciplines is offered, for the characterization of the emergence and evolution of cancer, seen as a self-organized dynamic system far from the thermodynamic equilibrium. Development of a thermodynamic framework, based on the entropy production rate, is presented in Subheading 2. Subheading 3 is dedicated to all tumor growth, as seen through a "phase transitions" far from equilibrium. Subheading 4 is devoted to complexity of cancer glycolysis. Finally, some concluding remarks are presented in Subheading 5.

Systemic therapies in hepatocellular carcinoma: present and future

Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deathsworldwide and is generally presented at an advanced stage, limiting patients' quality of life. The conventional cytotoxic systemic therapy has proved to be ineffective in HCC, since its induction several decades ago. Today it is possible to use our knowledge of molecular hepatocarcinogenesis to provide a targeted therapy. Sorafenib has demonstrated large improvements in overall survival in HCC. This review describes the molecular mechanisms and potential therapeutic targets, focusing on sorafenib, sunitinib, tivantinib, antiangiogenic agents, and current and future immunotherapies. Thus, it will be necessary in the future to classify HCCs into subgroups according to their genomic and proteomic profiling. The identification of key molecules/receptors/signaling pathways and the assessment of their relevance as potential targets will be the main future challenge potentially influencing response to therapy. Defining molecular targeted agents that are effective for a specific HCC subgroup will hopefully lead to personalized therapy.

Mathematical models of immune-induced cancer dormancy and the emergence of immune evasion

Cancer dormancy, a state in which cancer cells persist in a host without significant growth, is a natural forestallment of progression to manifest disease and is thus of great clinical interest. Experimental work in mice suggests that in immune-induced dormancy, the longer a cancer remains dormant in a host, the more resistant the cancer cells become to cytotoxic T-cell-mediated killing. In this work, mathematical models are used to analyse the possible causative mechanisms of cancer escape from immune-induced dormancy. Using a data-driven approach, both decaying efficacy in immune predation and immune recruitment are analysed with results suggesting that decline in recruitment is a stronger determinant of escape than increased resistance to predation. Using a mechanistic approach, the existence of an immune-resistant cancer cell subpopulation is considered, and the effects on cancer dormancy and potential immunoediting mechanisms of cancer escape are analysed and discussed. The immunoediting mechanism assumes that the immune system selectively prunes the cancer of immune-sensitive cells, which is shown to cause an initially heterogeneous population to become a more homogeneous, and more resistant, population. The fact that this selection may result in the appearance of decreasing efficacy in T-cell cytotoxic effect with time in dormancy is also demonstrated. This work suggests that through actions that temporarily delay cancer growth through the targeted removal of immune-sensitive subpopulations, the immune response may actually progress the cancer to a more aggressive state.

Beyond the cancer cell: progression-level determinants highlight the multiscale nature of carcinogenesis risk

Over the last several decades, improved awareness of the prevalence of carcinogens in the environment, along with a growing appreciation of the complexity of the carcinogenesis process, has shifted policy on cancer risk from one of strict avoidance of carcinogens to one of adherence to exposure limits deemed "safe" based on quantitative risk estimation. Meanwhile, given the mutagenic nature of most carcinogens, attention has gravitated to developing a genetic rationale for measuring and comparing risks. This focus has culminated in the now well-established multistage mutational paradigm, which holds that a stepwise sequence of mutations drives cell "initiation" and the subsequent "transformation" of an initiated cell into a cancer cell, and that, once created, a cancer cell will inevitably undergo "progression" to become overt disease. Unanticipated by this paradigm is the effect progression-phase population- and tissue-level bottleneck events may have on this process. Attesting to this is the prevalence of tumor dormancy, a state of arrested growth of an otherwise fully malignant, often microscopic cancer mass, maintained by interactions among cancer cells and between cancer and host cells. The proper inclusion of such progression-modifying influences would clearly behoove risk estimation and improve our understanding of the natural history of cancer by accounting for the less-than-certain risk of eventual cancer disease even when cancer cells are present. Such an improved understanding, in turn, stands to better inform policy-making and influence such clinical practice decisions as whether to treat the increasingly smaller tumors detectable with advancing technologies.

The interplay of intrinsic and extrinsic bounded noises in biomolecular networks

After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a biomolecular network. The influence of intrinsic and extrinsic noises on biomolecular networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: (i) the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, (ii) a model of enzymatic futile cycle and (iii) a genetic toggle switch. In (ii) and (iii) we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possible functional role of bounded noises.