Use of gene expression to identify heterogeneity of metastatic behavior among high-grade pleomorphic soft tissue sarcomas Keith Skubitz 1, Princy Francis 2, Amy Skubitz 1, Xianghua Luo 1, and Mef Nilbert 2,3 1 University of Minnesota, 2 Lund University, 3 Hvidovre 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 >/=5- fold 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 1234

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