Abstract 1990: Exploratory analysis of the usefulness of the top-scoring pairs (TSP) of genes for prediction of prostate cancer progression
Authors: Zhao Hongya , Logothetis Christopher J. , Gorlov Ivan P. 
Published: February 2011

Abstract
Predicting prostate cancer progression after radical prostatectomy is most challenging. Gene expression profiling is widely used to identify genes associated with cancer progression. Usually candidate genes are identified in gene-by-gene comparisons of expression, but recent reports suggest that relative expression of a gene pair more efficiently predicts cancer progression.
The rank-based top-scoring pair (TSP) algorithm classifies phenotypes according to relative expression of a gene pair [1]. In many cases TSP provides robust, efficient classification of phenotypes, but a large number of tests can lead to a large number of false-positive results. We modified the standard TSP algorithm by controlling the false-discovery rate and computing sensitivity and specificity of the test and applied the modified approach to Gene Expression Omnibus dataset GSE10645 [2]. The cited study analyzed the association between gene expression and outcome after initial therapy by comparing expression of ∼ 1000 candidate genes in 213 patients with no evidence of disease progression during 7 years after radical retropubic prostatectomy (RRP) with that in 213 patients whose disease progressed to the metastatic form. We used TSP to predict which patients would experience systemic tumor progression and which would stay disease free. Relative expression of TPD52L2/SQLE, BCS1L/SQLE, and CEACAM1/BRCA1 gene pairs predicted patients who will develop metastases after RRP with &gt; 99% specificity but ∼10% sensitivity. The same gene pairs were validated in 3 independent prostate cancer datasets from GEO. Combining 2 pairs of genes (TPD52L2/SQLE and CEACAM/BRCA1) improved sensitivity without compromising specificity: 21.5% sensitivity and 99.5% specificity.
Functional annotation of the TSP genes using the Ingenuity approach showed that they cluster by a limited number of biologic functions and pathways, including the molecular mechanisms of cancer, insulin receptor signaling, integrin signaling, and regulation of actin-based motility by Rho. In validation analysis with independent datasets, lower expression of genes from arginine and proline metabolism relative to that of genes from the insulin receptor-signaling pathway may be used to classify primary tumors vs distant metastases in cancer progression. In conclusion, comparative analysis of the expression of 2 genes and resulting pathways may be a simple, effective classifier for predicting prostate cancer progression.
References
1. Geman D, d'Avignon C, Naiman DQ, Winslow RL. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol 2004; 3: Article 19. Epub 2004 Aug 30
2. Nakagawa T, Kollmeyer TM, Morlan BW, et al. A tissue biomarker panel predicting systemic progression after PSA recurrence post-definitive prostate cancer therapy. PLoS One 2008;3:e2318
Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1990.