Accepted Abstracts: J Cancer Sci Ther
The ability for clinicians to develop personalized therapeutics based on a patient?s genetic composition is a challenge, particularly in cancer diagnostics. In many cancers, the genetic composition can vary significantly within the same tumor sample. This not only complicates developing an optimal treatment for a cancer, but also challenges cancer diagnostics as well. Currently, personalized medicine in cancer is based on high-cost genetic testing technology and most of the information acquired is based on correlation studies. Targeted therapy agents are increasingly available for clinical applications, but have produced disappointing results when tested in clinical trials, indicating that there are many challenges that must be addressed to advance this field. In this proposal, we introduce a first-in-the field technique, surprisal analysis (SA), that will revolutionize and reinvigorate the quest for personalized medicine in cancer therapeutics. Furthermore, SA is extended beyond just a theoretical approach by developing targeted microfluidic biotechnology for clinical use.
Sohila Zadran completed her undergraduate studies in Molecular Cell Biology, with an emphasis Neurobiology at the University of California, Berkeley. She received her doctoral degree in Neuroscience, with an emphasis in Neural Engineering, at the University of Southern California and completed her post-doctoral training at the California Institute of Technology. She is currently a cancer scientist at the University of California, Los Angeles. She is also the founder of Agarionan Corp, a biotech firm affiliated with the California Institute of Quantitative Biology (QB3), a company dedicated to the development of novel cancer biotechnologies.