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Deisboeck, Thomas

Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative ‘systems’ research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute’s recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation.

The World Wide Web has revolutionized how researchers from various disciplines collaborate over long distances. This is nowhere more important than in the Life Sciences, where interdisciplinary approaches are becoming increasingly powerful as a driver of both integration and discovery. Data access, data quality, identity, and provenance are all critical ingredients to facilitate and accelerate these collaborative enterprises and it is here where Semantic Web technologies promise to have a profound impact. This paper reviews the need for, and explores advantages of as well as challenges with these novel Internet information tools as illustrated with examples from the biomedical community.

Pumping protons across a membrane was a critical step at the origin of life on earth, and it is still performed in all living organisms, including in human cells. Proton pumping is paramount to keep normal cells alive, e.g. for lysosomal digestion and for preparing peptides for immune recognition, but it goes awry in cancer cells. They acidify their microenvironment hence membrane voltage is lowered, which in turn induces cell proliferation, a hallmark of cancer. Proton pumping is achieved by means of rotary motors, namely vacuolar ATPases (V-ATPase), which are present at many of the multiple cellular interfaces. Therefore, we undertook an examination of the thermodynamic properties of V-ATPases. The principal result is that the V-ATPase-mediated control of the cell membrane potential and the related and consequent environmental pH can potentially represent a valuable support strategy for anticancer therapies. A constructal theory approach is used as a new viewpoint to study how V-ATPase can be modulated for therapeutic purposes. In particular, V-ATPase can be regulated by using external fields, such as electromagnetic fields, and a theoretical approach has been introduced to quantify the appropriate field strength and frequency for this new adjuvant therapeutic strategy.

Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.

It is conventional strategy to treat highly malignant brain tumors initially with cytoreductive surgery followed by adjuvant radio- and chemotherapy. However, in spite of all such efforts, the patients' prognosis remains dismal since residual glioma cells continue to infiltrate adjacent parenchyma and the tumors almost always recur. On the basis of a simple biomechanical conjecture that we have introduced previously, we argue here that by affecting the 'volume-pressure' relationship and minimizing surface tension of the remaining tumor cells, gross total resection may have an inductive effect on the invasiveness of the tumor cells left behind. Potential implications for treatment strategies are discussed.

Background: Tissue invasion, one of the hallmarks of cancer, is a major clinical problem. Recent studies suggest that the process of invasion is driven at least in part by a set of physical forces that may be susceptible to mathematical modelling which could have practical clinical value. Model and conclusion: We present an analogy between two unrelated instabilities. One is caused by the impact of a drop of water on a solid surface while the other concerns a tumor that develops invasive cellular branches into the surrounding host tissue. In spite of the apparent abstractness of the idea, it yields a very practical result, i.e. an index that predicts tumor invasion based on a few measurable parameters. We discuss its application in the context of experimental data and suggest potential clinical implications.

Cancer expansion depends on host organ conditions that permit growth. Since such microenvironmental nourishment is limited we argue here that an autologous, therapeutically engineered and faster metabolizing cell strain could potentially out-compete native cancer cell populations for available resources which in turn should contain further cancer growth. This hypothesis aims on turning cancer progression, and its microenvironmental dependency, into a therapeutic opportunity. To illustrate our concept, we developed a three-dimensional computational model that allowed us to investigate the growth dynamics of native tumor cells mixed with genetically engineered cells that exhibit a higher proliferation rate. The simulation results confirm in silico efficacy of such therapeutic cells to combating cancer cells on site in that they can indeed control tumor growth once their proliferation rate exceeds a certain level. While intriguing from a theoretical perspective, this bold, innovative ecology-driven concept bears some significant challenges that warrant critical discussion in the community for further refinement.

Background: Highly malignant gliomas are characterized by rapid growth, extensive local tissue infiltration and the resulting overall dismal clinical outcome. Gaining any additional insights into the complex interaction between this aggressive brain tumor and its microenvironment is therefore critical. Currently, the standard imaging modalities to investigate the crucial interface between tumor growth and invasion in vitro are light and confocal laser scanning microscopy. While immensely useful in cell culture, integrating these modalities with this cancer's clinical imaging method of choice, i.e. MRI, is a non-trivial endeavour. However, this integration is necessary, should advanced computational modeling be able to utilize these in vitro data to eventually predict growth behaviour in vivo. We therefore argue that employing the same imaging modality for both the experimental setting and the clinical situation it represents should have significant value from a data integration perspective. In this case study, we have investigated the feasibility of using a specific form of MRI, i.e. magnetic resonance microscopy or MRM, to study the expansion dynamics of a multicellular tumor spheroid in a collagen type I gel. Methods: An U87mEGFR human giloblastoma multicellular spheroid (MTS) containing approximately 4·103 cells was generated and pipetted into a collagen I gel. The sample was then imaged using a T2-weighted 3D spoiled gradient echo pulse sequence on a 14T MRI scanner over a period of 12 hours with a temporal resolution of 3 hours at room temperature. Standard histopathology was performed on the MRM sample, as well as on control samples. Results: We were able to acquire three-dimensional MR images with a spatial resolution of 24 × 24 × 24 μm3. Our MRM data successfully documented the volumetric growth dynamics of an MTS in a collagen I gel over the 12-hour period. The histopathology results confirmed cell viability in the MRM sample, yet displayed distinct patterns of cell proliferation and invasion as compared to control. Conclusion: In this study, we demonstrate that a specific form of MRI, i.e. magnetic resonance microscopy or MRM, can be used to study the dynamic growth of a multicellular tumor spheroid (MTS) with a single cell scale spatial resolution that approaches the level of light microscopy. We argue that MRM can be employed as a complementary non-invasive tool to characterize microscopic MTS expansion, and thus, together with integrative computational modeling, may allow bridging of the experimental and clinical scales more readily.