Abstract 3665: An EMT gene expression diagnostic predicts resistance to EGFR and MEK-targeted therapies in cell lines and patients

Abstract

The epithelial to mesenchymal transition (EMT) in cancer cells results in the acquisition of metastatic properties and may contribute to chemoresistance. Several studies have shown that transition to a mesenchymal phenotype leads to decreased dependence on EGFR-RAS signaling and insensitivity to EGFR inhibitors. To better understand the importance of EMT as a general predictor of drug response, we defined an EMT gene signature derived from a meta-analysis of differential gene expression signatures representing genes up-regulated following transfection of breast cell lines with various EMT regulators (Taube et al., 2010 Proc Natl Acad Sci USA 107:15449-54). We then determined the expression of the EMT signature across cell line panels and determined whether it predicted sensitivity or resistance to various targeted therapies. Consistent with previous results, expression of EMT signature was significantly associated with resistance to an EGFR inhibitor, lapatinib. Similarly, the EMT signature also predicted resistance to PQIP (IGF1R), GSK1120212 (MEK), GSK690693 (AKT), and perifosine (AKT/PI3K), suggesting that EMT may be a common resistance mechanism to a number of drugs that target growth factor signaling. As more of these targeted agents are entering clinical trials, the ability to characterize the signature may have important implications for drug development. To study the relevance of the EMT signature in clinical tumors, we compared the signature to a collection of tumor co-expression patterns, known as OncoScore modules, which were defined from 40,000+ tumor microarray experiments. Notably, the EMT signature was significantly associated with a major tumor co-expression pattern representing mesenchymal and/or stromal phenotype observed in almost all major solid tumor types. In retrospective microarray scoring analyses of key clinical datasets, the mesenchymal/stromal module predicted resistance to cetuximab. This finding was validated with an independent cohort of colorectal cancer patients treated with cetuximab using the Oncoscore Colon diagnostic. Oncoscore Colon is a qPCR test optimized for formalin-fixed paraffin-embedded tissue that measures the twelve key colon cancer transcriptional modules, including the mesenchymal module. Because the mesenchymal/stromal module monitors a fundamental phenotype of cancer cells important for drug response, this validated qPCR test has broad application to companion diagnostics development and personalized medicine. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3665. doi:1538-7445.AM2012-3665

High EMT Signature Score of Invasive Non-Small Cell Lung Cancer (NSCLC) Cells Correlates with NFκB Driven Colony-Stimulating Factor 2 (CSF2/GM-CSF) Secretion by Neighboring Stromal Fibroblasts

“…This EMT signature was also applied in glioblastoma multiformis (GBM) showing again a correlation with the mesenchymal and proneural subtype of GBM [ 49 ]. In addition, the EMT gene expression signature predicts resistance to EGFR and MEK-targeted therapies in cell lines and almost all solid tumor patient samples [ 27 ], as well as resistance to EGFR and PI3K inhibitors [ 50 ]. Finally, transcription profiling was used to identify genes involved in EMT utilizing a murine EpH4 model [ 51 ].…”

“…[ 28 ] and Eddy et al . [ 27 ]. A set of EMT signature genes was used to rank invasive (black bars) and non-invasive (grey bars) cell lines according to their EMT score.…”

“…Subsequently, we performed gene-wise scaling of expression values by subtracting the mean and dividing by standard deviation over the whole cell line panel. Finally, we obtained the average of the genes in the EMT-signature [ 27 ] which is based on a meta-analysis of cells transfected with EMT inducing genes (GSG, SNAI1, TGFB1, TWIST1) [ 28 ].…”

Immune cell infiltration pattern in non-small cell lung cancer PDX models is a model immanent feature and correlates with a distinct molecular and phenotypic make-up

“…Based on the RNA expression levels of the respective genes the 14 NSCLC PDX models received a specific EMT score which was calculated following Rudisch et al 20 by performing gene-wise scaling of expression values and afterwards obtaining the average of the genes in the EMT signature. 21 …”