Abstract 3909: Genomic and transcriptomic analysis suggest that primary pleomorphic sarcomas and leiomyosarcomas are two genetically distinct diseases

Abstract

Abstract Malignant fibrous histiocytoma was the most frequent pleomorphic soft tissue sarcoma (STS) histotype, until laborious investigation showed a differentiation in two thirds of the cases, thereby reclassifying many of the tumors into pleomorphic liposarcomas (LPS) and leiomyosarcomas (LMS). Pleomorphic soft tissue sarcoma without any histologically identifiable line of differentiation is designated as undifferentiated pleomorphic sarcoma (UPS). Gene expression studies in UPS initially suggested that the majority of tumors cluster with LMS and LPS but subsequent studies have identified distinct genomic and expression profiles in liposarcomas. In the present study, it was evaluated genomic and transcriptomic alterations in UPS and LMS samples (35 frozen samples) obtained previously to chemo and or radiotherapy and without metastasis at diagnosis. These primary tumors were extensively characterized by immunohistochemistry. Array CGH and large-scale gene expression analysis were evaluated using the Human 44K Agilent oligoarrays according to manufacturer's instructions. Array CGH data were extracted and flagged with Feature Extraction software (version 10.1.1.1), processed using Genomic Workbench Standard (version.5.0.14), with the statistical algorithm ADM-2 and sensitivity threshold of 6.0. DNA copy number alterations were compared with Database of Genomic Variants (DGV; http://projects.tcag.ca/variation/) and with a reference dataset obtained from 83 healthy Brazilian individuals. Genomic gains were more frequently observed in UPS whereas losses were commonly detected in LMS. Some genomic alterations were frequently observed in both groups. Gains at 7q21.13 and losses at 13q14.3 were the most frequent genomic imbalances verified in UPS and LMS, respectively. Gene expression data were extracted using the Feature Extraction software (v. 10.1.1.1) and analyzed by the R software (v. 2.9.2) e TMEV (v. 4.4.1). An unsupervised and supervised analysis was done using the Significance Analysis for Microarray software (SAM) (FDR 0% and 1000 permutations) and Pearson correlation. It was detected three clusters with 101 genes differentially expressed. One cluster was composed 10 UPS, the second with 13 LMS and the last one with both tumors. All genes that differentiated the groups are involved in pathways related to cancer. The identification of specific genomic and transcriptomic imbalances suggests that UPS and LMS might represent genetically distinct diseases and indicate putative molecular diagnostic markers by integrated data analysis.Financial support FAPESP and CNPq. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3909. doi:10.1158/1538-7445.AM2011-3909