Keith Skubitz 1 , Princy Francis 2 , Amy Skubitz 1 , Xianghua Luo 1 , and Mef Nilbert 2,3

1. 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

2. Sarcomas are heterogeneous • Heterogeneity of biological behavior exists even within histologic subtypes of sarcomas, complicating clinical care, clinical trials, and drug development.

4. Example • Assume treatment A has no adverse effect • Assume benefit of treatment A is all or none in a certain percentage of patients

6. Some biological behaviors that do not correlate well with morphology may be determined by gene expression patterns

7. 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

8. 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)

9. PCA with 604 probes up or down >/=5-fold in ccRCC vs normal kidney B

10. PCA with probes from ubiquitylation in control of cell cycle pathway A

11. Gene expression patterns that distinguished 2 subsets of RCC (RCC gene set), OVCA (OVCA gene set), and AF (AF gene set) were identified

12. Question • Do the RCC-, OVCA-, and AF-gene sets identify subsets of high-grade pleomorphic STS?

13. Samples • 73 Samples obtained from Lund University • 40 MFH • 20 LMS • 9 other high-grade pleomorphic STS

14. 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

15. 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

16. Hierarchical Clustering

18. Hierarchichal Clustering 1 2 3 4

20. Important Caveats • Clustering pattern depends on composition of sample set • Many types of clustering and ways to modify data

21. 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

22. 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

23. 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