Stephanie Seidlits, Ph.D., is an associate professor of Biomedical Engineering at The University of Texas at Austin, where her research applies engineering approaches to develop new therapies for brain cancer. Glioblastoma is the most common, yet highly lethal, cancer originating in the brain. Despite a large body of evidence suggesting that the extracellular matrix surrounding tumors facilitates treatment resistance and disease progression, preclinical models of glioblastoma fail to account for these effects. Dr. Seidlits is developing polysaccharide-based scaffolds as platforms that mimic native brain tissue and support 3D culture of glioblastoma cells derived from patient tumors. These tissue-engineered tumors provide the opportunity to do valuable preclinical studies in a model that is cheaper, faster and more controlled than animals, yet yield results with similar clinical relevance. 

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Stephanie Seidlits, Ph.D., is an associate professor of Biomedical Engineering at The University of Texas at Austin, where her research applies engineering approaches to develop new therapies for brain cancer. Glioblastoma is the most common, yet highly lethal, cancer originating in the brain. Despite a large body of evidence suggesting that the extracellular matrix surrounding tumors facilitates treatment resistance and disease progression, preclinical models of glioblastoma fail to account for these effects. Dr. Seidlits is developing polysaccharide-based scaffolds as platforms that mimic native brain tissue and support 3D culture of glioblastoma cells derived from patient tumors. These tissue-engineered tumors provide the opportunity to do valuable preclinical studies in a model that is cheaper, faster and more controlled than animals, yet yield results with similar clinical relevance.  Furthermore, her lab is developing high-throughput arrays of tissue-engineered GBM tumors to characterize how specific cell-matrix interactions enable tumors to acquire resistance to routine treatments. Information from this model will be integrated with a computational model to predict changes in cancer signaling networks driving treatment resistance and identify new, targeted drugs for use in combination with standard therapies to prevent resistance. This work has significant potential to improve predictions of clinical outcomes by integrating genomic, phenotypic and functional measures and to identify new strategies to treat glioblastoma. 

A native Texan, Dr. Seidlits obtained a B.S. in Bioengineering from Rice University and went on to receive both M.S. and Ph.D. degrees in Biomedical Engineering from The University Texas at Austin. She then trained as an NIH NRSA F32 Post-Doctoral Fellow in Chemical and Biological Engineering at Northwestern University. Since starting her independent research program, Dr. Seidlits has been honored with several awards, including an NSF CAREER Award and 2019 Society for Biomaterials Young Investigator Award. Her research has been supported by the National Institutes of Health, National Science Foundation, CPRIT, American Cancer Society, and American Brain Tumor Association.

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