Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA283814 | ENA |
REPOSITORIES: ENA
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Project description:Background: Epithelial-stromal crosstalk plays a critical role in invasive breast cancer (IBC) pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis. Results: We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive re-wiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, ER-positive IBC, and ER-negative IBC, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in IBC mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas. Conclusions: Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially-localized transcriptomic data. The analysis provides new biological insights into the re-wiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer. 36 flash-frozen human primary breast cancer samples were subjected to laser capture microdissection to separately isolate matched tumor epithelial and tumor-associated stromal components. RNA was isolated, subjected to 2 rounds of amplification, and hybridized on Agilent 4x44K microarrays along with a common reference (single round-amplified commercially obtained Universal Human Reference RNA) in a dyeswap design. For two samples of tumor-associated stroma, a second technical replicate was performed. Samples were labelled as ER-positive based on ESR1 gene expression levels in the tumor epithelium, using univariate Gaussian mixture model-based clustering via the mclust package in R.
2015-05-12 | E-GEOD-68744 | biostudies-arrayexpress
Project description:Background: Epithelial-stromal crosstalk plays a critical role in invasive breast cancer (IBC) pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis. Results: We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive re-wiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, ER-positive IBC, and ER-negative IBC, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in IBC mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas. Conclusions: Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially-localized transcriptomic data. The analysis provides new biological insights into the re-wiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.
2015-05-12 | GSE68744 | GEO
Project description:Validation dataset of 298 ER-positive patients treated with tamoxifen for 5 years. All patients in this dataset have ER+ breast cancer and were uniformly treated with tamoxifen for 5 years. The objective of the study was to correlate levels of genomic markers to outcomes (relapse free survival) in this cohort of uniformly treated patients.
2010-08-13 | GSE17705 | GEO
Project description:Validation dataset of 298 ER-positive patients treated with tamoxifen for 5 years. All patients in this dataset have ER+ breast cancer and were uniformly treated with tamoxifen for 5 years. The objective of the study was to correlate levels of genomic markers to outcomes (relapse free survival) in this cohort of uniformly treated patients. Tissue samples were processed and profiled by two different labs (MD Anderson Cancer Center and Jules Bordet Institute).
2010-08-13 | E-GEOD-17705 | biostudies-arrayexpress
Project description:Background: We hypothesize that important genomic differences between breast cancer subtypes occur early in carcinogenesis. Therefore, gene expression might distinguish histologically normal breast epithelium (NlEpi) from breasts containing estrogen receptor positive (ER+) compared with estrogen receptor negative (ER-) cancers. Methods: We examined gene expression in 46 cases of microdissected NlEpi from previously untreated women undergoing breast cancer surgery. From 30 age-matched cases (15 ER+, 15 ER-) we used Affymetryix U133A arrays. From 16 independent cases (9ER+, 7 ER-), we validated seven selected genes using qPCR. We then compared gene expression between NlEpi and invasive breast cancer using 4 publicly available datasets. Results: 216 probes (corresponding to 198 unique genes) distinguished the NlEpi from breasts with ER+ (NlEpiER+) compared to ER- cancers (NlEpiER-). These include genes characteristic of ER+ and ER- cancers themselves, (e.g., ESR1, GATA3, and CX3CL1, FABP7, respectively). QPCR validated the microarray results in both a sampling of the 30 original cases (84%) and all of the 16 independent cases (77%). Gene expression in NlEpiER+ and NlEPIERNlEpiER- resembled gene expression in ER+ and ER- cancers, respectively: 36%-53% of the genes or probes examined in each the 4 external datasets overlapped between NlEpi and the corresponding cancer subtype. Conclusions: Gene expression differs in NlEpi of breasts containing ER+ compared to ER- breast cancers. These differences echo differences in ER+ and ER- invasive cancers. Thus, breast cancer subtypes may be detectable before histologic abnormalities. NlEpi gene expression may help define subtype-specific risk signatures, identify initial subtype specific genomic differences, and suggest new targets for subtype-specific prevention and therapy. We determined that 216 probesets significantly differed between histologically normal epithelium from ER+ breast cancer patients and from ER- breast cancer patients, and that gene expression in each type of histologically normal epithelium resembles expression of the corresponding subtype of invasive breast cancer (i.e., ER+ or ER-). These findings suggest that characteristic features of breast cancer subtypes are detectable prior to any histologic abnormality. This suggestion has implications for understanding breast cancer biology and devising new tools for assessing breast cancer risk. 30 total laser capture microdissected histologically normal breast tissue samples were analyzed with Affymetrix HU133A microarrays. All samples were age-matched between histologically normal epithelial samples from ER+ breast cancer patients (n=15) and histologically normal epithelial samples from ER- breast cancer patients (n=15). Sample numbers correspond to individual patient samples. Of the 4 publicly available datasets mentioned above, the only dataset with a GEO number was GSE3494, corresponding to the Miller dataset. The supplementary file below lists the 251 Samples used from the GSE3494 study. We did not reanalyze the data - there was no change made to the Miller dataset; we only used these data for confirmation of our own dataset.
2010-12-07 | E-GEOD-21947 | biostudies-arrayexpress
Project description:Chromosomal instability (CIN) is a driver of cancer metastasis and immune evasion. Yet, the extent to which this effect depends on the immune system remains unknown. Here we show that CIN-induced chronic activation of the cGAS-STING pathway in cancer cells induces signal re-wiring downstream of STING, promoting a pro-metastatic tumor microenvironment (TME). Using ContactTracing, a newly developed, validated, and benchmarked tool to infer conditionally-dependent cell-cell interactions from single cell transcriptomic data, we identify a cancer cell-derived STING-dependent ER stress response that remodels a TME replete with immune suppressive myeloid cells and dysfunctional T cells. Simultaneously, CIN-induced chronic STING activation leads to interferon-specific tach-yphylaxis reinforcing immune suppression. Reversal of CIN, depletion of cancer cell STING, or inhibition of ER stress signaling upends CIN-dependent effects on the TME and suppresses metastasis in immune competent, but not severely immune compromised settings. Treatment with STING inhibitors reduces CIN-driven metastasis in melanoma, breast, and colorectal cancer. Finally, we show that CIN and pervasive cGAS activation in micronuclei are associated with ER stress signaling, immune suppression, and metastasis in human triple-negative breast cancer; highlighting a viable strategy to identify and therapeutically intervene in tumors spurred by CIN-induced inflammation.
2023-06-20 | GSE189856 | GEO
Project description:dataset of 60 patients with ER-positive primary breast cancer and treated with tamoxifen monotherapy for 5 years. Data were generated from LCMed cancer cells. Sample_keyword: breast cancer, tamoxifen, recurrence Keywords: other
2004-06-07 | GSE1378 | GEO
Project description:dataset of 60 patients with ER-positive primary breast cancer and treated with tamoxifen monotherapy for 5 years. Data were generated from whole tissue sections of breast cancers. Sample_keyword: breast cancer, tamoxifen, recurrence Keywords: other
2004-06-07 | GSE1379 | GEO
Project description:Crosstalk and complexity within signaling pathways has limited our ability to devise rational strategies for using network biology to treat human disease. This is particularly problematic in cancer where oncogenes that drive or maintain the tumorigenic state alter the normal flow of molecular information within signaling networks that control growth, survival and death. Understanding the architecture of oncogenic signaling pathways, and how these networks are re-wired by ligands or drugs, could provide opportunities for the specific targeting of oncogene-driven tumors. Here we use a systems biology-based approach to explore synergistic therapeutic strategies to optimize the killing of triple negative breast cancer cells, an incompletely understood tumor type with a poor treatment outcome. Using targeted inhibition of oncogenic signaling pathways combined with DNA damaging chemotherapy, we report the surprising finding that time-staggered EGFR inhibition, but not simultaneous co-administration, can dramatically sensitize the apoptotic response of a subset of triple-negative cells to conventional DNA damaging agents. A systematic analysis of the order and timing of inhibitor/genotoxin presentation—using a combination of high-density time-dependent activity measurements of signaling networks, gene expression profiles, cell phenotypic responses, and mathematical modeling—revealed an approach for altering the intrinsic oncogenic state of the cell through dynamic re-wiring of oncogenic signaling pathways. This process converts these cells to a less tumorigenic state that is more susceptible to DNA damage-induced cell death, through re-activation of an extrinsic apoptotic pathway whose function is suppressed in the oncogene-addicted state. Three or 4 replicates of 3 different cell lines at time points 0minutes, 30minutes, 6 hours and 1 day after EGFR inhibition with erlotinib
2012-05-14 | E-GEOD-30516 | biostudies-arrayexpress
Project description:Background: Up to 40% of patients with estrogen receptor positive (ER+) breast cancer experience treatment resistance and disease recurrence, often caused by the upregulation of growth factor receptors. Understanding the mechanisms of resistance, and the identification of novel therapeutic targets is, therefore, of vital importance if breast cancer prognosis is to be further improved. Fibroblast growth factor receptor 1 (FGFR1) is a potential driver of endocrine resistance; however, clinically successful attempts at targeting FGFR1 activity in breast cancer are rare and may result from an inability to correctly identify patients that could benefit from such treatment. The identification of additional genes associated with an FGFR1-mediated mechanism of resistance will provide a more precise classification scheme for FGFR1-dependency in breast cancer. Methods: Live cell imaging and RNA sequencing of ER+ breast cancer cell lines (CAMA1, T47D, tamoxifen resistant T47D) were used to investigate FGFR1-dependency in breast cancer, mechanisms of endocrine resistance and the effects of treatment with tamoxifen and erdafitinib. An FGFR1-amplified ER+ breast cancer patient-derived xenograft (PDX) model was used to assess the effects of targeting FGFR1 in vivo. Gene expression analysis of human breast cancer from The Cancer Genome Atlas (TCGA) was used for clinical validation. Results: We evaluated the effects of targeting FGFR1 in ER+/HER2- breast cancer with aberrant FGFR1 amplification and/or overexpression. We found that the FGFR tyrosine kinase inhibitor (TKI) erdafitinib inhibited cell proliferation of FGFR1-amplified CAMA1 cells in vitro. Additionally, erdafitinib significantly enhanced the anti-proliferative effect of 4-hydroxytamoxifen (4-OHT) and its anti-tumor effect was also demonstrated in vivo. Further, we demonstrated that the proliferation of FGFR1-overexpressing tamoxifen-resistant T47D (TR-T47D) cells is dependent on FGFR1 activity. Moreover, these TR-T47D cells are re-sensitized to tamoxifen upon treatment with erdafitinib. Finally, we found that upregulation of genes related to epithelial-mesenchymal-transition (EMT) correlates with FGFR1 overexpression, and is reversed by FGFR inhibition. Conclusions: In this study we demonstrated that targeting FGFR1 in ER+/HER2- breast cancer with aberrant FGFR1 activity has the potential to inhibit tumor growth and to re-sensitize resistant tumors to tamoxifen. Furthermore, we identified a functional relationship between EMT, FGFR activity and endocrine resistance in breast tumorigenesis.
2022-08-01 | GSE200029 | GEO