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Applying Evolutionary Biology Strategies to Cancer Research

Workshop report: Applying Evolutionary Biology Strategies to Cancer Research

Executive Summary

The National Cancer Institute (NCI) Office of Physical Sciences – Oncology (OPSO) launched the Physical Sciences – Oncology Centers (PS-OC) Program in 2009 to advance our understanding of cancer by bringing novel “physical sciences perspectives” to cancer from teams of physical scientists and cancer researchers working closely together. In May 2012, the OPSO convened a series of two workshops to reflect on progress made in the area of ‘evolution and evolutionary theory of cancer’, a major focus of the PS-OC Program, and identify additional aspects and problems in cancer research that would benefit from more scientific efforts in this area. The first Strategic Workshop in the series, “Applying Evolutionary Biology Strategies to Cancer Research”, aimed to bring physical sciences perspectives and approaches from evolution and evolutionary theory to enable a deeper understanding of cancer and inform better approaches to detect, treat, and prevent this complex disease.

The premise of this workshop was that cancer, as viewed from a physical sciences perspective, is considered an evolving complex adaptive system. Application of physical sciences approaches from evolutionary biology can further our understanding of the complex heterogeneity of cancer within adaptive landscapes. By examining all length scales, from subcellular to ecosystem, the presenters reinforced the need for advancing technologies to provide new knowledge to understand potential underlying evolutionary principles operating in cancer. Roundtable discussions, consisting of experts from evolutionary biology, physical sciences, and oncology, identified recent advances in the field and big questions in cancer research that would benefit from physical sciences approaches in evolutionary biology.

Key findings and ideas generated by the presentations and discussions included:

Continue to apply advancing technologies and models from evolutionary biology to understand cancer dynamics

Deep digital genomic sequencing, novel microfluidic devices, and game theory models are proving useful for understanding the clonal evolution of tumors, the origin and dynamics of cellular heterogeneity, and the development of resistance to both chemotherapy and radiation therapy. New evolutionary principles derived from these data can serve as a framework for creating quantitative models to understand and predict the dynamics of cancer evolution. For example, experiments using engineered microhabitats indicate that ‘selection occurs faster in small populations than a large one’ and may provide insights into the impact of heterogeneity in tumors. Future advances should focus on developing evolutionary models to help identify evolutionary nodes or bottlenecks and compile comprehensive cancer-related data.

Understanding and targeting the evolutionary strategy in cancer

Several examples of evolutionary strategies in microbiology and ecology (e.g., bacteria, biofilms, and predator-prey games) may be applicable to gain a better understanding of cancer progression and therapeutic response. In single bacterium, evidence suggests that selective pressure (e.g., antibiotics) can trigger mutations in specific hotspots related to DNA repair to increase the overall rate of mutagenesis and increase the potential to evolve. Within biofilms, bacterial subclones evolve as a population to maximize productivity of the entire biofilm, not necessarily the individual clone. In ecology, predator-prey evolutionary strategies are dependent on the evolution of the environment as well as the species. Preliminary evidence indicates similar evolutionary strategies in cancer, such as the observation that cancer cells migrate into higher concentrations of doxorubicin during evolution into a drug resistance phenotype. Cancer research should focus on using physical sciences approaches in evolutionary biology to identify potential evolutionary strategies at all length scales that can be used to target or predict the evolutionary responses of cancer cells to therapeutics.

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