No abstract
Background: Kras is a member of the ras family of GTPase proteins that regulate cellular growth and survival in response to stimulation of cell surface receptors such as the Epidermal Growth Factor Receptor (EGFR). Activating mutations in the Kras gene are found in 30–50% of colorectal tumors (CRC). The presence of mutant Kras in tumors has been shown to correlate with lack of response to EGFR inhibitors. Recently, we found that CRC cells respond to TRAIL with an EGFR/HER2-mediated survival response and that by blocking this response, apoptosis is enhanced. A phase I/IIa study is currently investigating effect of AMG655 (DR5 agonist) with panitumumab (EGFR monoclonal antibody) in patients with chemorefractory CRC. The aim of this study was to evaluate the role of Kras mutational status in determining response to TRAIL alone and in combination with EGFR- or MEK-targeted therapies in a panel isogenic paired Kras wild type (WT) and mutant (MT) CRC cells. Methods: Erk1/2 phosphorylation and expression were measured by Western blotting. Apoptosis was measured by flow cytometry andWestern blotting for PARP, caspase-8 and caspase 3 cleavage. Results: Using paired isogenic KrasMT HCT116 and KrasWT HKH-2 and HKe-3 cell lines, we found higher basal levels of pErk1/2 in the KrasMT HCT116 cell line compared to the KrasWT HKH-2 and HKe-3 cell lines. Furthermore, we found that the HKH-2 and HKe-3 KrasWT cells were more sensitive to rhTRAIL compared to the isogenic parental and KrasMT HCT116 cell line. Moreover, we found a statistical significant increase in apoptosis when rhTRAIL was combined with the EGFR monoclonal antibodies panitumumab or cetuximab in KrasWT HKH-2 and HKe-3 cell lines but not in the parental KrasMT HCT116 cell line. In contrast, KrasMT HCT116 cell line was more sensitive to combined TRAIL/MEK-targeted therapy compared to KrasWT HKH-2 clone. Conclusions: Our findings indicate that Kras mutations can predict response to TRAIL alone and in combination with EGFR-targeted agents in CRC cells. Inhibition of MEK1/2 in combination with TRAIL-targeted therapies may provide a strong treatment strategy in Kras mutant CRC tumors. We are currently investigating mechanism of KrasMT induced resistance to TRAIL treatment and these data will be presented during the meeting. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):B243.
Background: Transcriptional predictors are increasingly important for sub-typing cancer patients and understanding disease aetiology, predicting patient outcomes and response to treatment. Colorectal cancers (CRC) can be classified by transcriptionally inferred consensus (CMS) and intrinsic (CRIS) sub-types, annotating transcriptional profiles derived from colorectal cancer samples with these sub-types and other transcriptionally inferred predictors requires an experienced bioinformatician and is time-consuming. Methods: Publically available R packages CMSclassifier, CRISclassifier, MCP-counter and DoRothEA have been integrated into a web interface using the R/Shiny framework which allows users to upload gene expression data which can be additionally normalized with DESeq2. Visualization of this data can be interacted with through using plotly. Results: To address this bottleneck, we developed the classifieRC Shiny app, which enables rapid analysis, annotation of colorectal cancer transcriptional profiles with state-of-the-art transcriptional CRC sub-typing (CMS and CRIS) as well as estimation of cellular composition (MCP-Counter) and transcription factor activity predictions (DoRothEA). classifieRC can be accessed through a web-based interface, a locally deployable R-script or executable software, with capability of publishing datasets and their resulting analysis to Shiny.IO. Conclusions: classifieRc enables researchers to rapidly annotate colorectal transcriptomic datasets with molecular sub-types and of functional predictions without the need for a dedicated bioinformatician, expediting insights related patient cohort analyses and novel discoveries. classifieRc provides an easy to use flexible framework for functional annotation transcriptomic datasets and a platform for development of other disease specific apps. Citation Format: Gerard Quinn, Tamas Sessler, Wendy Allen, Sarah Maguire, Philip Dunne, Darragh McArt, Harper VanSteenhouse, Peter Gallagher, Andrea Lees, Dan Longley, Bruce Seligmann, Mark Wappett, Simon McDade. classifieRc: An interactive web interface for the molecular classification of colorectal cancer from RNA-sequencing data [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3205.
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