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Transcript
Use of gene expression to identify heterogeneity of metastatic behavior among high-grade pleomorphic soft tissue sarcomas Keith Skubitz1, Princy Francis2, Amy Skubitz1, Xianghua Luo1, and Mef Nilbert2,3 1University of Minnesota, 2Lund University, 3Hvidovre Hospital Sarcomas are heterogeneous • Heterogeneity of biological behavior exists even within histologic subtypes of sarcomas, complicating clinical care, clinical trials, and drug development. Example • Assume treatment A has no adverse effect • Assume benefit of treatment A is all or none in a certain percentage of patients • Some biological behaviors that do not correlate well with morphology may be determined by gene expression patterns • A common approach to identify prognostic factors is to search for differences in gene expression between 2 groups defined by an outcome (eg survival) – Requires defining 2 groups – Irrelevant genes may obscure important patterns – Different genes could be important in different subsets • Alternatively, identification of subsets independent of clinical information could be useful • We used PCA with a variety of gene sets in an attempt to identify heterogeneity – Clear cell renal carcinoma (RCC) – Serrous ovarian carcinoma (OVCA) – Aggressive fibromatosis (AF) PCA with 604 probes up or down >/=5fold in ccRCC vs normal kidney B PCA with probes from ubiquitylation in control of cell cycle pathway A • Gene expression patterns that distinguished 2 subsets of RCC (RCC gene set), OVCA (OVCA gene set), and AF (AF gene set) were identified Question • Do the RCC-, OVCA-, and AF-gene sets identify subsets of high-grade pleomorphic STS? Samples • 73 Samples obtained from Lund University • 40 MFH • 20 LMS • 9 other high-grade pleomorphic STS Data • cDNA microarray slides with ~16,000 unique UniGene clusters • About 50% of the genes in the RCC-, OVCA-, and AF- gene sets were present in this data set Methods • Data were pooled to form a set of 234 genes present in at least one of the RCC-, OVCA-, or AF-gene sets • Hierarchical clustering using this gene set was performed Hierarchical Clustering Hierarchichal Clustering 1 2 3 4 Important Caveats • Clustering pattern depends on composition of sample set • Many types of clustering and ways to modify data Conclusions • Analysis of a set of STS using a gene set derived from other tumor systems without regard to clinical data, identified differences in time to metastasis • Thus, an approach to subcategorizing samples before searching for variables that correlate with clinical behavior may be useful Conclusions • Although no confirmation of clinical relevance is available, stratifying patients entering trials by a similar approach could be useful, and would not result in loss of information Conclusions • Although no confirmation of clinical relevance is available, stratifying patients entering trials by a similar approach could be useful, and would not result in loss of information • Banked samples should be obtained for all STS patients entering clinical trials for later analysis