An SSX4 knock-in cell line model and in silico analysis of gene expression data as two approaches for investigating mechanisms of cancer

Abstract

Cancer/testis (CT) genes mapping to the X chromosome (CT-X) are normally expressed in male germ cells but not in adult somatic tissues, with rare exception of oogonia and trophoblast cells; whereas they are aberrantly expressed in various types of cancer. CT-X genes are coordinately expressed and their expression is associated with poor prognosis in various types of cancer. The mechanisms responsible for the reactivation of CT-X genes during tumorigenesis are of great interest because of their prognostic and therapeutic value. In this study, we aimed to develop two approaches by which the mechanisms underlying the regulation of CT-X gene expression in cancer could be identified. Current evidence implicates promoter-specific demethylation as the key event inducing CT-X gene expression in cancer but the mechanisms of this epigenetic deregulation remain to be explored. We presume that coordinately expressed CT-X genes are regulated by common mechanisms. We, thus, decided that the study of a given CT-X gene could elucidate mechanisms pertinent to all. Our first approach was to generate a a model whereby variations of the expression of an individual CT-X gene, namely SSX4, upon various manipulations could be easily monitored. For this pupose, we used the SSX4 targeting vector to generate an SSX4 knock-in (KI) lung cancer cell line (SK-LC-17) with a GFP reporter gene expressed from SSX4 promoter. SKLC-17 is known to express SSX4 as well as other CT-X genes and its SSX4 promoter has been characterized in detail. We, thus, obtained one clone with homogenous GFP expression verified by sequencing for correct integration of SSX4 KI targeting vector. In the long-term, this cell line model will be used to identify transcriptional regulators of CT-X gene expression that function either in a direct manner as epigenetic controllers or indirectly as effectors upstream to epigenetic mechanisms. Based on the fact that CT-X gene expression occurs coordinately in all tumor types, the second series of experiments described herein aimed to develop an approach whereby genes, which are differentially expressed between CT-X expressing (CT-X positive) and nonexpressing (CT-X negative) tissues or cells could be identified. Towards this aim a metaanalysis of publicly available microarray datasets from different types of tumors and cancer cell lines was developed. Using this approach, the CT-X positive group was observed to contain gene expression signatures indicative of higher proliferative and metastatic capacity when compared to the CT-X negative group. Additional studies based on class prediction analysis in a lung cancer cell line dataset were performed to compensate for bias due to tissue specific differences between datasets obtained from the meta-analysis. Lastly, we selected a set of genes that behaved commonly in both meta-analysis and class prediction analysis to be validated in cancer cell lines with known CT-X expression profiles.