Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.

Hypoxia is a critical factor in solid tumors that has been associated with cancer progression and aggressiveness. We recently developed a hypoxia fate mapping system to trace post-hypoxic cells within a tumor for the first time. This approach uses an oxygen-dependent fluorescent switch and allowed us to measure key biological features such as oxygen distribution, cell proliferation, and migration. We developed a computational model to investigate the motility and phenotypic persistence of hypoxic and post-hypoxic cells during tumor progression. The cellular behavior was defined by phenotypic persistence time, cell movement bias, and the fraction of cells that respond to an enhanced migratory stimulus. This work combined advanced cell tracking and imaging techniques with mathematical modeling, to reveal that a persistent invasive migratory phenotype that develops under hypoxia is required for cellular escape into the surrounding tissue, promoting the formation of invasive structures (''plumes'') that expand toward the oxygenated tumor regions.

The PanNET Working Group of the 16th International Multiple Endocrine Neoplasia Workshop (MEN2019) convened in Houston, TX, USA, 27–29 March 2019 to discuss key unmet clinical needs related to PanNET in the context of MEN1, with a special focus on non-functioning (nf)-PanNETs. The participants represented a broad range of medical scientists as well as representatives from patient organizations, pharmaceutical industry and research societies. In a case-based approach, participants addressed early detection, surveillance, prognostic factors and management of localized and advanced disease. For each topic, after a review of current evidence, key unmet clinical needs and future research directives to make meaningful progress for MEN1 patients with nf-PanNETs were identified. International multi-institutional collaboration is needed for adequately sized studies and validation of findings in independent datasets. Collaboration between basic, translational and clinical scientists is paramount to establishing a translational science approach. In addition, bringing clinicians, scientists and patients together improves the prioritization of research goals, assures a patient-centered approach and maximizes patient involvement. It was concluded that collaboration, research infrastructure, methodologic and reporting rigor are essential to any translational science effort. The highest priority for nf-PanNETs in MEN1 syndrome are (1) the development of a data and biospecimen collection architecture that is uniform across all MEN1 centers, (2) unified strategies for diagnosis and follow-up of incident and prevalent nf-PanNETs, (3) non-invasive detection of individual nf-PanNETs that have an increased risk of metastasis, (4) chemoprevention clinical trials driven by basic research studies and (5) therapeutic targets for advanced disease based on biologically plausible mechanisms.

Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches) as well as storing simulation data requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster through the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) "database", multiple individuals can run tests simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here we describe DAPT and provide an example demonstrating its use.

SUMMARYHypoxia is a critical factor in solid tumors that has been associated with cancer progression and aggressiveness. We recently developed a hypoxia-fate mapping system that allowed the tracing of post-hypoxic cells within a tumor for the first time. This novel approach, based on an oxygen-dependent fluorescent switch, made the investigation of the post-hypoxic phenotype possible. The system allowed us to measure key biological features such as oxygen distribution, cell proliferation and migration. Using this data, we developed a computational model to investigate the motility and phenotypic persistence of hypoxic and post-hypoxic cells during tumor progression. The behavior of hypoxic and post-hypoxic cells was defined by phenotypic persistence time, cell movement bias and the fraction of cells that respond to an enhanced migratory stimulus. Our studies revealed that post-hypoxic cells have an enhanced persistent migratory phenotype that promotes the formation of invasive structures (“plumes”) expanding towards the oxygenated tumor regions. This work combined advanced cell tracking and imaging techniques with mathematical modeling, and revealed for the first time that a persistent invasive migratory phenotype that develops under hypoxic conditions enhances their escape into non-hypoxic tumor regions to invade the surrounding tissue.