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GSE6503
Mus musculus
14 Downloadable Samples
Affymetrix Mouse Expression 430A Array (moe430a)
Description
Age-related defects in stem cells can limit proper tissue maintenance and hence contribute to a shortened life-span. Using highly purified hematopoietic stem cells from mice aged 2 to 21 months, we demonstrate a deficit in function yet an increase in stem cell number with advancing age. Expression analysis of more than 14,000 genes identified 1500 that were age-induced and 1600 that were age-repressed. Genes associated with the stress response, inflammation, and protein aggregation dominated the upregulated expression profile, while the downregulated profile was marked by genes involved in the preservation of genomic integrity and chromatin remodeling. Many chromosomal regions showed coordinate loss of transcriptional regulation, and an overall increase in transcriptional activity with aged, and inappropriate expression genes normally regulated by epigenetic mechanisms was observed. Hematopoietic stem cells from early-aging mice expressing a mutant p53 allele reveal that aging of stem cells can be uncoupled from aging at an organismal level. These studies show that HSC are not protected from aging. Instead, loss of epigenetic regulation at the chromatin level may drive both functional attenuation of cells, as well as other manifestations of aging, including the increased propensity for neoplastic transformation.
Publication Title
Aging hematopoietic stem cells decline in function and exhibit epigenetic dysregulation.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 14 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Effect of FOXO knockdown on E2F1-mediated transcription
Publication Title
FOXO transcription factors control E2F1 transcriptional specificity and apoptotic function.
Sample Metadata Fields
Specimen part, Cell line, Treatment
View Samples- Mus musculus
- 126 Downloadable Samples
- Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)
Description
Human cancers result from a complex series of genetic alterations resulting in heterogeneous disease states. Dissecting this heterogeneity is critical for understanding underlying mechanisms and providing opportunities for therapeutics matching the complexity. Mouse models of cancer have generally been employed to reduce this complexity and focus on the role of single genes. Nevertheless, our analysis of tumors arising in the MMTV-Myc model of mammary carcinogenesis reveals substantial heterogeneity, seen in both histological and expression phenotypes. One contribution to this heterogeneity is the substantial frequency of activating Ras mutations, the frequency of which can be changed by alterations in Myc. Additionally, we show that these Myc-induced mammary tumors exhibit even greater heterogeneity, revealed by distinct histological subtypes as well as distinct patterns of gene expression, than many other mouse models of tumorigenesis. Two of the major histological subtypes are characterized by differential patterns of cellular signaling pathways, including B-Catenin and Stat3 activities. We also demonstrate the predictive nature of this approach though examining metastatic potential. Together, these data reveal that a combination of histological and genomic analyses can uncover substantial heterogeneity in mammary tumor formation and therefore highlight aspects of tumor phenotype not evident in the population as a whole.
Publication Title
Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypes including metastatic potential.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 22 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
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.
Publication Title
Using a stem cell-based signature to guide therapeutic selection in cancer.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Homo sapiens
- 14 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
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.
Publication Title
Using a stem cell-based signature to guide therapeutic selection in cancer.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Homo sapiens
- 8 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
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.
Publication Title
Using a stem cell-based signature to guide therapeutic selection in cancer.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus, Homo sapiens
- 56 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Anchorage-independent cell growth signature identifies tumors with metastatic potential.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Homo sapiens
- 30 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Cultured cancer cells exhibit substantial phenotypic heterogeneity when measured in a variety of ways such as sensitivity to drugs or the capacity to grow under various conditions. Among these, the ability to exhibit anchorage-independent cell growth (colony forming capacity in semisolid media), has been considered to be fundamental in cancer biology, because it has been connected with tumor cell aggressiveness in vivo such as tumorigenic and metastatic potentials, and also utilized as a marker for in vitro transformation. Although multiple genetic factors for anchorage-independence have been identified, the molecular basis for this capacity is still largely unknown. To investigate the molecular mechanisms underlying anchorage independent cell growth, we have used genome-wide DNA microarray studies to develop an expression signature associated with this phenotype. Using this signature, we identify a program of activated mitochondrial biogenesis associated with the phenotype of anchorage-independent growth and importantly, we demonstrate that this phenotype predicts potential for metastasis in primary breast and lung tumors.
Publication Title
Anchorage-independent cell growth signature identifies tumors with metastatic potential.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Mus musculus
- 26 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Cultured cancer cells exhibit substantial phenotypic heterogeneity when measured in a variety of ways such as sensitivity to drugs or the capacity to grow under various conditions. Among these, the ability to exhibit anchorage-independent cell growth (colony forming capacity in semisolid media) has been considered to be fundamental in cancer biology because it has been connected with tumor cell aggressiveness in vivo such as tumorigenic and metastatic potentials, and also utilized as a marker for in vitro transformation. Although multiple genetic factors for anchorage-independence have been identified, the molecular basis for this capacity is still largely unknown. To investigate the molecular mechanisms underlying anchorage-independent cell growth, we have used genome-wide DNA microarray studies to develop an expression signature associated with this phenotype. Using this signature, we identify a program of activated mitochondrial biogenesis associated with the phenotype of anchorage-independent growth and importantly, we demonstrate that this phenotype predicts potential for metastasis in primary breast and lung tumors.
Publication Title
Anchorage-independent cell growth signature identifies tumors with metastatic potential.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 23 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Gene expression profiles were collected from HEK-HT cells expressing H-Ras with Ras-activating (G12V), Raf-activating (G12V,T35S), RalGEF-activating (G12V,E37G), or PI3K-activating (G12V,Y40C) mutations.
Publication Title
A genomic strategy to elucidate modules of oncogenic pathway signaling networks.
Sample Metadata Fields
Specimen part
View Samples