Stromal gene signature
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ABSTRACT:
PROVIDER: PRJEB19256 | ENA |
REPOSITORIES: ENA
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2007-11-02 | E-TABM-152 | biostudies-arrayexpress
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2018-06-30 | GSE100301 | GEO
Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Keywords: Disease state analysis
2008-08-15 | GSE11078 | GEO
Project description:Relapse and metastatic progression is a frequent event in colorectal cancer patients detected at early stages. The risk of recurrence requires the development of new biomarkers to correctly predict biological behavior of early stage II and stage III patients and their response to adjuvant chemotherapy. Here, we combined the proteomic quantification of secreted proteins involved in metastasis with a transcriptional analysis to develop a risk score algorithm based on the expression of six genes (SEC6). The SEC6 signature was predictive of survival and recurrence for stage II and III patients in four different datasets including a total of 1534 patients and was also associated with deficient mismatch repair, CpG-island methylator positive status and BRAF mutation. SEC6 was also predictive of beneficial or detrimental effects from 5-Fluorouracil-containing regimes and the improved response to more aggressive chemotherapies based on FOLFOX and FOLFIRI. In summary, the SEC6 risk-score algorithm may constitute a new tool for decision-making in colorectal cancer management.
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Project description:To investigate the molecular signatures of the different sub-types of Primary cicatricial alopecia (PCA). We performed high-throughput RNA-sequencing on scalp biopsies of PCA patient. We developed gene expression-based signatures, trained into machine-learning based predictive models and pathways associated with dysregulated gene expression. We performed morphological and cytokine analysis to define the immune cell populations found in PCA sub types. Using bulk RNA sequencing, we identified a common PCA gene signature that was shared between LPP, FFA, and CCCA which revealed a significant over-representation of mast cell signature genes, as well as down regulation of cholesterogenic pathways and up regulation of fibrosis and immune cell signaling genes. Thw Immunohistological analyses revealed an increased presence of mast cells in PCAs lesions.
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Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Experiment Overall Design: We used microarrays to identify lung metastasis-related genes in a series of 23 patients with breast cancer metastases. No replicate, no reference sample.
2009-03-07 | E-GEOD-11078 | biostudies-arrayexpress
Project description:Metastasis is the deadliest phase of cancer progression. Experimental models using immunodeficient mice have been used to gain insights into the mechanisms of metastasis. We report here the identification of a “metastasis aggressiveness gene expression signature” derived using human melanoma cells selected based on their metastatic potentials in a xenotransplant metastasis model. Comparison with expression data from human melanoma patients shows that this metastasis gene signature correlates with the aggressiveness of melanoma metastases in human patients. Many genes encoding secreted and membrane proteins are included in the signature, suggesting the importance of tumor-microenvironment interactions during metastasis. Keywords: disease state
2007-10-02 | GSE8401 | GEO
Project description:Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type, or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value. 6 samples of stroma surrounding invasive breast primary tumors; 6 matched samples of normal stroma. 6 samples of stroma surrounding invasive prostate primary tumors; 6 matched samples of normal stroma.
2011-03-31 | E-GEOD-26910 | biostudies-arrayexpress
Project description:Metastasis is the deadliest phase of cancer progression. Experimental models using immunodeficient mice have been used to gain insights into the mechanisms of metastasis. We report here the identification of a “metastasis aggressiveness gene expression signature” derived using human melanoma cells selected based on their metastatic potentials in a xenotransplant metastasis model. Comparison with expression data from human melanoma patients shows that this metastasis gene signature correlates with the aggressiveness of melanoma metastases in human patients. Many genes encoding secreted and membrane proteins are included in the signature, suggesting the importance of tumor-microenvironment interactions during metastasis. Keywords: disease state analysis
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