Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma

PURPOSE: This study assesses the ability of multidrug resistance (MDR)-associated gene expression patterns to predict survival in patients with newly diagnosed carcinoma of the ovary. The scope of this research differs substantially from that of previous reports, as a very large set of genes was evaluated whose expression has been shown to affect response to chemotherapy. EXPERIMENTAL DESIGN: We applied a customized TaqMan Low Density Array, a highly sensitive and specific assay, to study the expression profiles of 380 MDR-linked genes in 80 tumor specimens collected at initial surgery to debulk primary serous carcinoma. The RNA expression profiles of these drug resistance genes were correlated with clinical outcomes. RESULTS: Leave-one-out cross-validation was used to estimate the ability of MDR gene expression to predict survival. Although gene expression alone does not predict overall survival (P=0.06), four covariates (age, stage, CA125 level and surgical debulking) do (P=0.03). When gene expression was added to the covariates, we found an 11-gene signature that provides a major improvement in overall survival prediction (log-rank statistic P<0.003). The predictive power of this 11-gene signature was confirmed by dividing high and low risk patient groups, as defined by their clinical covariates, into four specific risk groups based on expression levels. CONCLUSION: This study reveals an 11-gene signature that allows a more precise prognosis for patients with serous cancer of the ovary treated with carboplatin- and paclitaxel-based therapy. These 11 new targets offer opportunities for new therapies to improve clinical outcome in ovarian cancer

Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance

Although in vitro models have been a cornerstone of anti-cancer drug development, their direct applicability to clinical cancer research has been uncertain. Using a state-of-the-art Taqman-based quantitative RT-PCR assay, we investigated the multidrug resistance (MDR) transcriptome of six cancer types, in established cancer cell lines (grown in monolayer, 3D scaffold, or in xenograft) and clinical samples, either containing >75% tumor cells or microdissected. The MDR transcriptome was determined a priori based on an extensive curation of the literature published during the last three decades, which led to the enumeration of 380 genes. No correlation was found between clinical samples and established cancer cell lines. As expected, we found up-regulation of genes that would facilitate survival across all cultured cancer cell lines evaluated. More troubling, however, were data showing that all of the cell lines, grown either in vitro or in vivo, bear more resemblance to each other, regardless of the tissue of origin, than to the clinical samples they are supposed to model. Although cultured cells can be used to study many aspects of cancer biology and response of cells to drugs, this study emphasizes the necessity for new in vitro cancer models and the use of primary tumor models in which gene expression can be manipulated and small molecules tested in a setting that more closely mimics the in vivo cancer microenvironment so as to avoid radical changes in gene expression profiles brought on by extended periods of cell culture