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Expression data from CD133+ and CD133- glioma cells

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE24716 | GEO | 2010/10/16

SECONDARY ACCESSION(S): PRJNA133603

REPOSITORIES: GEO

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Expression data from CD133+ and CD133- glioma cells

Project description: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. CD133+ and CD133- cells were separated from two glioma xenograft tumors. Both CD133+ and CD133- glioma cells were cultured in serum-free media for 48 hours in the presence of absence of laminin.

2010-10-16 | E-GEOD-24716 | biostudies-arrayexpress

Using a Stem Cell-Based Signature to Guide Therapeutic Selection in Cancer

Project description: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

2010-10-16 | E-GEOD-24717 | biostudies-arrayexpress

Basal gene expression of breast cancer cell lines

2010-10-08 | GSE24578 | GEO

Using a Stem Cell-Based Signature to Guide Therapeutic Selection in Cancer

Project description: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. This SuperSeries is composed of the SubSeries listed below.

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Expression data from GBM and normal neural CD133+ and CD133- cells

Project description:We use gene expression data to provide a three-faceted analysis on the links between molecular subclasses of glioblastima, epithelial-to mesenchymal transition (EMT) and CD133 cell surface protein. The contribution of this paper is three-folded: First, we used a newly identified signature for epithelial-to-mesenchymal transition in human mammary epithelial cells, and demonstrated that genes in this signature have significant overlap with genes differentially expressed in all known GBM subtypes. However, the overlap between the genes up-regulated in the mesenchymal subtype of GBM and in the EMT signature was more significant than other GBM subtypes. Second, we provided evidence that there is a negative correlation between the genetic signature of EMT and that of CD133 cell surface protein, a putative marker for neural stem cells. Third, we studied the correlation between GBM molecular subtypes and the genetic signature of CD133 cell surface protein. We demonstrated that the mesenchymal and neural subtypes of GBM have the strongest correlations with the CD133 genetic signature. While the mesenchymal subtype of GBM demonstrates similarity with the signatures of both EMT and CD133, it also demonstrates some differences with each of these signatures that is partly due to the fact that the signatures of EMT and CD133 are inversely related to each other. Taken together this data sheds light on role of the mesenchymal transition and neural stem cells, and their mutual interaction, in molecular subtypes of glioblastoma multiforme.

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Expression data comparing CD133+ and CD133- B16-F10 melanoma cells

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2019-05-03 | GSE130283 | GEO

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CD133+ vs. CD133- cells in GBML8, a primary glioblastoma tumorsphere culture

Project description:CD133+ and CD133- cells were FACS islated from GBML8 cells to find gene signatures upregulated in cancer stem cells

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