New methods of solid tumor analysis are needed to enable prediction of distant recurrence and selection of the optimal treatment for individual patients. Current methods for evaluating adjuvant therapy for solid tumors rely on large scale randomized clinical trials to determine efficacy. Such trials are expensive, difficult to perform and require that many patients receive treatment that is either not needed or ineffective. Many believe that future therapy will be """"""""targeted"""""""" to specific abnormalities in the tumor. To utilize such targeted therapy novel methods of examining tumor composition for specific targets are needed. In this proposal advanced computational analysis is used to combine molecular profiles (microarrays) with NMR determined metabolite markers, linked with information about biological function from genome and pathway databases. The overall goal is to select and identify combinations of markers in liposarcoma that carry significant diagnostic or prognostic value and to identify potential new therapeutic targets. This study will test the hypothesis that we can: (a) better select patients and tumors that have high or low risk of metastasis and (b) develop new targeted therapeutics for liposarcoma through analysis of signaling pathways critical to liposarcoma differentiation and tumorigenesis.
Specific aims : Identify genes and metabolite marker subsets by analysis of integrated gene expression and NMR biochemical profiles that: 1. show associations with liposarcoma (LS) histologic subtype 2. are differentially expressed in LS compared to normal fat tissue and could be used to evaluate signaling pathways central to LS differentiation and tumorigenesis serving as novel therapeutic targets 3. for low grade LS predict the presence of high grade LS elsewhere in the tumor or subsequent presentation with a high grade tumor 4. show association with outcome in liposarcoma. This integrated molecular and biochemical analysis will enable the development of objective and accurate methods for assessing LS diagnosis, progression, risk of metastasis and outcome. The results from this proposal would ultimately enable the identification of new targets, improve the ability to select patients for adjuvant therapy who are at highest risk of relapse and provide a rational approach to selecting combination chemotherapy based on an understanding of protein and lipid metabolism along with solid tumor molecular genetics.
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