Modeling variation in tumors in vivo

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Computational Systems Biology in Cancer: Modeling Methods and Applications

In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy.

Graded Foxo1 activity in Treg cells differentiates tumour immunity from spontaneous autoimmunity

Regulatory T (Treg) cells expressing the transcription factor Foxp3 have a pivotal role in maintaining immunological self-tolerance; yet, excessive Treg cell activities suppress anti-tumour immune responses. Compared to the resting Treg (rTreg) cell phenotype in secondary lymphoid organs, Treg cells in non-lymphoid tissues exhibit an activated Treg (aTreg) cell phenotype. However, the function of aTreg cells and whether their generation can be manipulated are largely unexplored. Here we show that the transcription factor Foxo1, previously demonstrated to promote Treg cell suppression of lymphoproliferative diseases, has an unexpected function in inhibiting aTreg-cell-mediated immune tolerance in mice. We find that aTreg cells turned over at a slower rate than rTreg cells, but were not locally maintained in tissues. aTreg cell differentiation was associated with repression of Foxo1-dependent gene transcription, concomitant with reduced Foxo1 expression, cytoplasmic localization and enhanced phosphorylation at the Akt sites. Treg-cell-specific expression of an Akt-insensitive Foxo1 mutant prevented downregulation of lymphoid organ homing molecules, and impeded Treg cell homing to non-lymphoid organs, causing CD8(+) T-cell-mediated autoimmune diseases. Compared to Treg cells from healthy tissues, tumour-infiltrating Treg cells downregulated Foxo1 target genes more substantially. Expression of the Foxo1 mutant at a lower dose was sufficient to deplete tumour-associated Treg cells, activate effector CD8(+) T cells, and inhibit tumour growth without inflicting autoimmunity. Thus, Foxo1 inactivation is essential for the migration of aTreg cells that have a crucial function in suppressing CD8(+) T-cell responses; and the Foxo signalling pathway in Treg cells can be titrated to break tumour immune tolerance preferentially.

Immune response to mutant neo-antigens: Cancer's lessons for aging

Extending observations on the immunogenicity of neo-antigens that arise in the course of oncogenesis and tumor progression, we suggest that somatic mutations affecting normal tissues also lead to generation of new epitopes. We hypothesize that, at least under inflammatory conditions, immune responses against such neo-antigens may lead to the elimination or functional impairment of normal cells, thus contributing to aging.

Models of metastasis in drug discovery

By definition, animal models provide only an approximation of clinical reality. One reason for this, for example, is that although metastases are the primary cause of mortality from neoplasia, by are rarely considered a target in drug discovery and development. Due to the impact of metastasis on clinical disease, we posit that metastasis should be considered in drug discovery, in addition, to more traditional biologic concepts, including drug pharmacology and toxicity. Drug discovery and developmental studies can incorporate orthotopic and spontaneous metastasis models (syngeneic and xenogeneic) with their inherent host-tumor microenvironmental interactions, in addition to confirmatory autochthonous and/or genetically engineered models (GEMs). This requires a rational and hierarchical approach using models of metastatic disease optimally using resected, orthotopic primary tumors and clinically relevant outcome parameters. In this chapter, we provide protocols for models of metastasis that can be used in translational and drug discovery studies.

Lessons in signaling and tumorigenesis from polyomavirus middle T antigen

The small DNA tumor viruses have provided a very long-lived source of insights into many aspects of the life cycle of eukaryotic cells. In recent years, the emphasis has been on cancer-related signaling. Here we review murine polyomavirus middle T antigen, its mechanisms, and its downstream pathways of transformation. We concentrate on the MMTV-PyMT transgenic mouse, one of the most studied models of breast cancer, which permits the examination of in situ tumor progression from hyperplasia to metastasis.

Mutation in aging mice occurs in diverse cell types that proliferate postmutation

To determine the relationship between aging, cell proliferation and mutation in different cell types, hearts, brains and kidneys from G11 PLAP mice between 1 week and 24 months of age were examined. Mutant cells were detected in tissue sections by staining for Placental Alkaline Phosphatase (PLAP) activity, an activity that marks cells that have sustained a frameshift mutation in a mononucleotide tract inserted into the coding region of the human gene encoding PLAP. The number of PLAP(+) cells increased with age in all three tissues. The types of cells exhibiting a mutant phenotype included cells that are proliferative, such as kidney epithelial cells, and cells that do not frequently replicate, such as cardiac muscle cells and neurons. In the brain, PLAP(+) cells appeared in various locations and occurred at similar frequencies in different regions. Within the cerebellum, PLAP(+) Purkinje cell neurons appeared at a rate similar to that seen in the brain as a whole. PLAP(+) cells were observed in kidney-specific cell types such as those in glomeruli and collecting tubules, as well as in connective tissue and blood vessels. In the heart, PLAP(+) cells appeared to be cardiac muscle cells. Regardless of tissue and cell type, PLAP(+) cells occurred as singletons and in clusters, both of which increased in frequency with age. These data show that age-associated accumulation of mutant cells occurs in diverse cell types and is due to both new mutation and proliferation of mutant cells, even in cell types that tend to not proliferate.

Computational systems biology in cancer: modeling methods and applications

In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy.

Murine models to evaluate novel and conventional therapeutic strategies for cancer

Animal models, by definition, are an approximation of reality, and their use in developing anti-cancer drugs is controversial. Positive retrospective clinical correlations have been identified with several animal models, in addition to limitations and a need for improvement. Model inadequacies include experimental designs that do not incorporate biological concepts, drug pharmacology, or toxicity. Ascites models have been found to identify drugs active against rapidly dividing tumors; however, neither ascitic nor transplantable subcutaneous tumors are predictive of activity for solid tumors. In contrast, primary human tumor xenografts have identified responsive tumor histiotypes if relevant pharmacodynamic and toxicological parameters were considered. Murine toxicology studies are also fundamental because they identify safe starting doses for phase I protocols. We recommend that future studies incorporate orthotopic and spontaneous metastasis models (syngeneic and xenogenic) because they incorporate microenvironmental interactions, in addition to confirmatory autochthonous models and/or genetically engineered models, for molecular therapeutics. Collectively, murine models are critical in drug development, but require a rational and hierarchical approach beginning with toxicology and pharmacology studies, progressing to human primary tumors to identify therapeutic targets and models of metastatic disease from resected orthotopic, primary tumors to compare drugs using rigorous, clinically relevant outcome parameters.

Co-mutagenic activity of arsenic and benzo[a]pyrene in mouse skin

Exposure to inorganic arsenic in drinking water is linked to skin, lung and bladder cancer in humans. The mechanism of arsenic-induced cancer is not clear, but exposure to arsenic and polycyclic arylhydrocarbons (PAH) is more carcinogenic than exposure to either type of carcinogen alone. Arsenic can also generate reactive oxygen species, suggesting that oxidation of DNA may play a role in carcinogenesis. Oxidization of guanosines in polyG tracts is known to cause frameshift mutations, and such events can be detected in situ using the G11 placental alkaline phosphatase (PLAP) transgenic mouse model, which reports frameshift mutations in a run of 11 G:C basepairs by generating cells containing heat-resistant alkaline phosphatase activity. PAH can also induce frameshift mutations. In the study described here, FVB/N mice carrying the G11 PLAP transgene were crossed to C57Bl/6 mice. Half of the hybrid mice were given drinking water with sodium arsenite (10 mg/L) for 10 weeks. Half of the arsenic treated mice were also exposed to benzo[a]pyrene (BaP) by skin painting (500 nmol/week) for 8 weeks. Another group of mice was exposed to BaP but not arsenic. The effect on frameshift mutation was assessed by staining sections of skin tissue to detect cells with PLAP activity. Arsenic alone had no significant effect. On average, mice given BaP alone had approximately three times more PLAP-positive (PLAP+) cells. By contrast, mice exposed to both arsenic and BaP exhibited 10-fold more PLAP+ cells in the skin, and these cells were often arranged in large clusters, suggesting derivation from stem cells. Whereas combined treatment produced more PLAP+ cells, stable BaP adduct levels and arsenic burdens were not higher in mice exposed to both agents compared to mice exposed to either one agent or the other.