Integrating Next-Generation Computational Models of Cancer Progression and Outcome

Sunday, 16 February 2014
Grand Ballroom E (Hyatt Regency Chicago)
Paul Macklin , University of Southern California , Los Angeles, CA
Cancer progression involves complex, dynamical tumor-host interactions at many time and spatial scales. To detangle this complex system, cancer scientists create and investigate a variety of experimental and computational models. In experimental biology, this has given rise to multiscale data sets gathered from multiple measurement platforms that give important but fragmented insights on cancer, and can be difficult to integrate into a more comprehensive view. This is further compounded by a general lack of standardized quantifications, especially at the multicell and tissue scales. Computational models face similar challenges: a variety of sophisticated models simulate specific cancers or cancer aspects, but a lack of data standardizations (particularly at the multicell and tissue scales) has hindered data sharing, interoperability, and model integration. In this talk, we draw upon insights from hurricane forecasting, computer animation, and other fields to explore solutions to these problems.

We will present examples of combining computational models to integrate multiscale in vitro and in vivo experimental data and gain new insights on cancer progression and outcome. We will show progress in developing data standards to describe in vitro and in vivo cancer experiments and simulations at the cell and tissue scales, and how these are giving rise to repositories of not only time series data, but also digitized cell lines and tissues. We will discuss efforts to create graphical tools that can edit and recombine these digitized cells and tissues to configure novel cancer simulations, much like graphic artists can import characters and scenes into computer graphics software to create games. Lastly, we explore the long-term implications. We will discuss a future where data standards and software integration platforms allow computational oncologists to seamlessly combine tumor growth, therapy response, and other modules into sophisticated and accurate simulators to improve patient clinical outcomes. We will consider the potential impact of making these computer models accessible not only to modelers, but also to biologists, clinicians, educators, and the general public. We will discuss a future where biopsies could be seeded into bioreactors and assayed to create patient-specific digitized cell lines, which can then be read by ensembles of compatible simulators to explore, compare, and help guide the patient’s treatment choice. Creating this future will require cooperation throughout the cancer research and clinical communities, and is already underway.