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A Novel Vertically-Integrated Platform for the Discovery and Molecular Characterization of Lung Cancer Stem Cells

Video   Mar 03, 2015


About the Speaker
I am a translational physician-scientist who began studying lung cancer over 20 years ago, with the explicit goal of eradicating the disease. At UCLA, I began studying viral gene transfer into lung cancer cells, while working as the Chief Architect of the UCLA Translational Gene and Cell Based Therapy program for treating lung cancer. That program led to UCLA being awarded a NCI Specialized Program of Research Excellence (SPORE) grant in Lung Cancer, the development of a clinical grade vector by NIH-Rapid Assistance to Intervention Development (RAID), and regulatory approvals by the NIH/OBA and FDA for a Phase 1 clinical trial in 2007. However, a key caveat limiting translational potential was that our preclinical models are inadequate representations of disease in situ; suggesting that tumor heterogeneity confounded not only our gene therapy strategies, but possibly the effectiveness of all therapeutics. Thus, our work since 2006 seeks to comprehend and functionally define intra-tumoral heterogeneity. Our recent publications provide key pilot feasibility data, and a philosophical foundation on which we are challenging existing paradigms. We bring an innovative (world-leading), scientifically sound approach, and comprehensive 'bedside to bench to bedside' capabilities to the ongoing study and rationally-derived targeted treatment of lung cancer.Abstract
We have developed an approach to study intratumoral heterogeneity, and to uncover the molecular basis of aggressive tumor cell phenotypes.  Some tumor cell subsets in an individual tumor are more competent than others in forming tumors, or to metastasize, or to resist therapy. Collectively, these properties have been attributed to 'cancer stem cells' or CSC.  We have developed novel processes and methods to extract CSC from clinical biospecimens, primarily culture them in vitro, and live-sort candidate CSC-subsets in order to jointly validate distinctive phenotypes and directly associate these properties with molecular signatures using OMICs.  This strategy will enable to both discover novel 'behavior-specific' therapeutic targets, as well as rational combinatorial approaches to treat cancer.


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