[CANCER RESEARCH 64, 7201–7204, October 15, 2004] Advances in Brief A Gene Expression Signature Associated with Metastatic Outcome in Human Leiomyosarcomas Yin-Fai Lee, 1 Megan John, 1 Alison Falconer, 1 Sandra Edwards, 1 Jeremy Clark, 1 Penny Flohr, 1 Toby Roe, 1 Rubin Wang, 1 Janet Shipley, 1 Robert J. Grimer, 2 D. Chas Mangham, 2 J. Meirion Thomas, 3 Cyril Fisher, 3 Ian Judson, 1 and Colin S. Cooper 1 1 The Male Urological Cancer Research Centre, Institute of Cancer Research, Surrey; 2 Department of Musculoskeletal Pathology, The Royal Orthopedic Hospital National Health Service Trust, Birmingham; and 3 Department of Histopathology, The Royal Marsden National Health Service Trust, London, United Kingdom Abstract Metastasis is a major factor associated with poor prognosis in cancer, but little is known of its molecular mechanisms. Although the clinical behavior of soft tissue sarcomas is highly variable, few reliable determi- nants of outcome have been identified. New markers that predict clinical outcome, in particular the ability of primary tumors to develop metastatic tumors, are urgently needed. Here, we have chosen leiomyosarcoma as a model for examining the relationship between gene expression profile and the development of metastasis in soft tissue sarcomas. Using cDNA mi- croarray, we have identified a gene expression signature associated with metastasis in sarcoma that allowed prediction of the future development of metastases of primary tumors (Kaplan-Meier analysis P  0.001). Our finding may aid the tailoring of therapy for individual sarcoma patients, where the aggressiveness of treatment is affected by the predicted outcome of disease. Introduction Soft tissue tumors are a heterogeneous group of mesenchymal tumors that arise as soft tissue masses and that frequently exhibit the differentiated features of adult soft tissue (1). Major histologic cate- gories of malignant soft tissue sarcomas include leiomyosarcoma (smooth muscle), rhabdomyosarcoma (striated muscle), liposarcoma (fatty tissue), synovial sarcoma, and malignant fibrous histiocytoma. The disease accounts for 1% of all cancers and is associated with a substantial mortality rate of 50%, which is related in part to its propensity for metastasis (1, 2). The clinical behavior of soft tissue sarcomas is highly variable, but few reliable determinants of outcome have been identified (2, 3). New markers that predict clinical outcome, in particular the propensity of primary tumors to develop metastatic tumors, are urgently needed and would be of great clinical use, allowing for more selective treatment strategies. In this study, we have chosen leiomyosarcoma as a model to assess the relationship between gene expression profiles determined on cDNA microarray and the clinical outcome of metastasis in soft tissue sarcomas. Materials and Methods Microarray Procedures. Sarcoma tissue samples were collected from patients undergoing surgery at the Royal Marsden National Health Service Trust, London and the Royal Orthopedic Hospital National Health Service Trust, Birmingham, United Kingdom. Diagnoses were performed by patholo- gists with conventional criteria, immunohistochemistry, and electron micros- copy. This study was done with approval from our local ethics committee. Tumor and control RNA preparation, cDNA microarray slide preparation, RNA labeling, and microarray hybridization were performed as in Lee et al. (4). Hybridized microarray slides were scanned in a GenePix 4000B scanner (Axon Instruments, Foster City, CA). Slides were scanned at photomultiplier tube voltage levels that provided a Cy5:Cy3 hybridization ratio across the slide of 1. We used the GenePix Pro 3.0.6 software (Axon Instruments) to determine ratios of fluorescent intensities (Cy5:Cy3) for individual cDNA after subtraction of background. We had previously established the reliability of these microarray procedures; 12 of 12 genes showing alterations in expression in microarray exhibited similar alterations when examined by Northern blot analyses (5). Analysis of Microarray Data. The scanned image was analyzed with the GenePix Pro 3.0.6 software (Axon Instruments). Fluorescent signals for both channels of the spots were determined. A local background in each channel was also determined for each spot, which is the median fluorescence of pixels in a halo surrounding the same array spot. Spots or areas of array with defects were flagged bad and excluded from subsequent analysis. To enhance the reliability of the expression data, another round of quality filtering was done. Spots with fluorescent spot intensity in each channel, which were 1.4 times the local background (medians) of that channel, were considered well meas- ured (6), and the data were additionally filtered to include only these spots. The median background intensity was subtracted from the median spot intensity to generate the background-corrected signal intensity for use in additional analysis. We used the GeneSpring software (Silicon Genetics, Redwood City, CA) to carry out additional microarray analyses. BRB-ArrayTools (Biometric Re- search Branch, National Cancer Institute, Bethesda, MD) was used for class comparison analyses (univariate F test/two-sample t test). Fluorescent intensity ratios of Cy5:Cy3 for individual spots of the filtered data were determined by dividing the background-corrected intensity for the Cy5 by that of the Cy3 channel. These ratios were then normalized by making the median of all measurements in each sample to be 1. Genes that have expression data in less than half of the samples were filtered out before the class comparison analysis. The samples were log 2 -transformed, and we compared the gene expressions of the 20 primary tumors (P) and 7 metastatic tumors (M) to find genes differ- entially expressed between the two classes using supervised class comparison analysis with a univariate F test (two-sample t test) with randomized variance model and multivariate permutation tests to control the number of false discoveries (based on 1000 random permutations of the class labels of the experiments and controlling the number of false positive to be 30 genes 50% of the time, univariate P equals 0.0122). Two-dimensional hierarchical clustering was then applied to the log-trans- formed data with average-linkage clustering with Pearson correlation around zero as the similarity metric for the 335 genes identified as differentially expressed between primary and metastatic sarcomas. This analysis divided thirty nonmetastatic tumors (P, PM, and LR) into two categories (groups 1 and 2). It is considered unlikely that exposure to chemotherapy would influence our analysis because only a single patient received chemotherapy that finished 5 weeks before surgery. We refined the 335 differentially expressed gene list with two different supervised learning methods to find a reduced set of discriminating genes best Received 5/12/04; revised 8/20/04; accepted 8/31/04. Grant support: Cancer Research UK and the Alexander Boag Sarcoma Fund. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Note: Additional minimum information about microarray experiment (MIAME) com- pliant data will be available at ArrayExpress (www.ebi.ac.uk/arrayexpress) at a later date. Requests for reprints: Yin-Fai Lee, The Male Urological Cancer Research Centre, Institute of Cancer Research, 15 Cotswold Road, Belmont, Sutton, Surrey SM2 5NG, United Kingdom. E-mail: Yin-Fai.Lee@icr.ac.uk. ©2004 American Association for Cancer Research. 7201 Research. on November 18, 2015. © 2004 American Association for Cancer cancerres.aacrjournals.org Downloaded from