Tumor evolution and stress response
We view cancer progression as a fundamentally evolutionary and ecological process, representing competition among cells with different fitness within an ever changing tumor microenvironmental ecosystem. From this viewpoint genetic alterations underlying cancer are only a small part of overall disease progression, and we are excited at the prospect that microenvironment targeting therapies will become more effective once they are built on a solid understanding of the population and evolutionary dynamics of neoplasms.

We have developed expertise and model systems to enable a new class of in vitro experimental tumor evolution studies, in physiologically relevant yet fully controlled and dynamically addressable tissues.

Evolution

We are are currently exploring the impact of the spatiotemporally varying oxygen and pH micro environments characteristic of growing tumors on the evolution of metabolic tumor hallmarks such as acquisition of a glycolytic phenotype, as well as response to chemotherapies. This work incorporates advanced experimental techniques to control and monitor the soluble microenvironement in 3D model systems (above, right), as well as powerful new microfluidic techniques, developed by our collaborator Dr. Chang Lu in Chemical Engineering, to perform broad spectrum analysis of molecular alterations within small numbers of cells collected in these platforms. This approach has the potential to provide important new insights and screening platforms for therapies which embrace the evolutionary nature of cancer progression and the inevitable march towards therapy resistance faced by traditional cancer drugs.

Related recent papers:
Patient-derived glioblastoma stem cells respond differentially to targeted therapies, Oncotarget, 2016

Paramagnetic Structures within a Microfluidic Channel for Enhanced Immunomagnetic Isolation, Scientific Reports, 2016

A physical sciences network characterization of nonmalignant and metastatic cells, Scientific Reports, 2013

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Funding:
This project is supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award number R21EB019123.

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