Year of Award:
Molecular & Cellular Analysis Technologies
GUDKOV, ANDREI V
Other PI or Project Leader:
CLEVELAND CLINIC LERNER COL/MED-CWRU
Identification of genes and pathways that contribute to tumorigenesis should lead to the defining of novel targets for therapeutic intervention and provide biomarkers for better diagnosis, staging, and risk assessment for individual cancer patients. Further progress in molecular genetics of cancer would greatly benefit from a reliable methodology of assigning gene functions based on phenotypic changes resulting from modulations in gene expression. Existing techniques of this kind are based on screening of genetically modified cells for genetic elements favoring cell growth under restrictive conditions (positive selection). Recently, a novel gene discovery methodology, named selection subtraction approach (SSA), was developed that allowed for a direct negative selection. Although proven useful for isolation of killing or growth suppressive genetic elements, SSA's capabilities are limited by the necessity to construct expression libraries. This proposal is focused on developing a new Insertional Selection Subtraction Approach (ISSA) that combines the advantages of SSA with the power of retroviral insertional mutagenesis and is based on a completely new vector system. The insertional mutagenesis arm is enhanced by the addition of a regulatable promoter, splice donor sequences, and the ability to trap polyadenylation signals. The second arm of ISSA involves 'tagging' or 'bar-coding' each mutant (SSA), thereby allowing to monitor the relative abundance of mutants within the population during selection by using 'retrophage arrays,' the key component of SSA. ISSA technique promises to become a universal functional screening tool, free from major drawbacks of its precursors. After 'technical' testing of ISSA, its power will be determined by identification of genes involved in regulation of cell sensitivity to TNF, a well-characterized system that has been already well studied by numerous approaches, including functional selection.