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题目:: Using a Stem Cell-Based Signature to Guide Therapeutic Selection in Cancer
ID:
状态:: 发布时间Oct. 16, 2010 , 更新时间 June 10, 2011 , 提交时间 Oct. 14, 2010,
物种:: Homo sapiens
摘要:: Given the very substantial heterogeneity of most human cancers, it is likely that most cancer therapeutics will be active in only a small fraction of any population of patients. As such, the development of new therapeutics, coupled with methods to match a therapy with the individual patient, will be critical to achieving significant gains in disease outcome. One such opportunity is the use of expression signatures to identify key oncogenic phenotypes that can serve not only as biomarkers but also as a means of identifying therapeutic compounds that might specifically target these phenotypes. Given the potential importance of targeting tumors exhibiting a stem-like phenotype, we have developed an expression signature that reflects common biological aspects of various stem-like characteristics. The Consensus Stemness Ranking (CSR) signature is upregulated in cancer stem cell enriched samples, at advanced tumor stages and is associated with poor prognosis in multiple cancer types. Using two independent computational approaches we utilized the CSR signature to identify clinically useful compounds that could target the CSR phenotype. In vitro assays confirmed selectivity of several predicted compounds including topoisomerase inhibitors and resveratrol towards breast cancer cell lines that exhibit a high-CSR phenotype. Importantly, the CSR signature could predict clinical response of breast cancer patients to a neoadjuvant regimen that included a CSR-specific agent. Collectively, these results suggest therapeutic opportunities to target the CSR phenotype in a relevant cohort of cancer patients. Refer to individual Series. This SuperSeries is composed of the following subset Series: GSE24578: Basal gene expression of breast cancer cell lines GSE24716: Expression data from CD133+ and CD133- glioma cells
实验种类:: transcription profiling by array
样本量:: 22
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数据号:: E-GEOD-24717, GSE24717
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