YY1 Expression Predicts Survival in Follicular and Diffuse Large B-Cell Lymphoma.

Recent gene expression profiling has identified gene signatures predictive of outcome, Indicator genes, for diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). However, measurement of Indicator genes in routine practice remains difficult. We have demonstrated utility of real-time PCR measurement of Indicator genes in globally amplified polyA cDNA as a practical method for their clinical analysis (Sakhinia et al 2005). PolyA PCR enables global mRNA amplification from picogram amounts of RNA and the polyA cDNA pool generated is indefinitely renewable, representing a “molecular block”. Real-time PCR measurement of the expression levels of specific Indicator genes then allows gene signatures to be detected in the polyA cDNA. In this project we applied real-time PCR for 36 Indicator genes, to polyA cDNAs prepared from 122 archived human frozen lymph nodes; specifically 63 cases of FL, 25 of DLBCL and 10 reactive lymph nodes were analysed; the remaining 24 cases were FL with either evidence of focal transformation, of subsequent transformation or (4 cases) paired frozen samples of FL and subsequent DLBCL. PolyA RT-PCR was performed on extracted RNA and resultant cDNA probed for 36 candidate Indicator genes (selected from Husson et al 2002, Shipp et al 2002 and Rosenwald et al 2003), by real-time PCR with quantification against human DNA, and normalisation to the mean of four housekeeping genes. Statistical analysis was performed using the Mann Whitney test and Kruskal-wallis with a p≤ 0.05 for statistical significance; all results detailed below are significant to at least p≤ 0.05. Ten genes showed statistically significant different expression between FL and DLBCL, including Cyclin B, COL3A1, NPM3, H731, PKC.B1, OVGL, ZFPC150, HLA-DQ-a, and XPB. Of these, cyclin b, a cell cycle gene, NPM3, nucleolar phosphoprotein, and COL3A1were higher in DLBCL. Six genes showed statistically significant higher expression in the neoplastic nodes compared to reactive nodes, namely PKCB-1, BCL-6, EAR2, ZFX, Cyclin B, YY.1. Comparison of gene expression levels in cases of FL that either did not or did subsequently transform to DLBCL demonstrated significant upregulation of ACTA, HLA-DQ-a in the latter cases of FL. High levels of YY.1 were associated with a shorter survival interval in both FL and DLBCL by Kaplan-meir survival analysis. This is of particular interest given the reported role of YY.1 in producing resistance to Rituximab due to downregulation of FAS -induced apoptosis (Vega et al, 2005). This was reported for a cell culture study and has not been shown in clinical studies, though the result in this project suggests a possible detrimental role for YY.1 clinically and that it may act as a predictor of Rituximab response. These results :demonstrate the possibility of using polyA PCR for global amplification of clinical samples and real-time PCR to measure diagnostically informative gene expression profiles in the resultant polyA cDNA “molecular block” andidentify novel prognostic markers for FL and DLBCL. The method is simple, sensitive and robust, and amy be used as a platform for clinical measuremen of prognostic gene signatures. Whilst lymphoma represents a relatively small group of cancer patients the generic nature of microarray gene profiles for cancer subtypes will facilitate simple extension of the method to other tumour types.