Systematic Identification of Epithelial–Stromal Crosstalk Signaling Networks in Ovarian Cancer
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA475593 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Ovarian cancer is the most lethal malignancy in the United States. While studies on ovarian cancer pathogenesis were mainly focused on the epithelial component of the tumor, understanding in the role of cancer associated fibroblasts (CAFs) in ovarian cancer progression is limited. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer–stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer.
2018-10-22 | GSE115635 | GEO
Project description:BACKGROUND:Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma-cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer-stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer. METHODS:Transcriptome profiles from microdissected ovarian cancer-associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor-bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration-approved agent that suppresses the Smad signaling cascade, or vehicle control (9-11 mice per group). All statistical tests were two-sided. RESULTS:Activation of TGF-?-dependent and TGF-?-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer-bearing mice from 36 to 48 weeks (P = .04). CONCLUSIONS:Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer-stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.
| S-EPMC6410941 | biostudies-literature
Project description:mRNA transcriptome data of normal ovarian stromal cells and tumor ovarian stromal cells
| PRJNA938139 | ENA
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:Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling processes. While thousands of mutations have been identified, mostly by genome-wide sequencing, systematic interpretation of their role in cancer and impact on cellular information processing is presently missing. Here, we propose a computational approach (ReKINect) to identify mutations attacking signaling networks. We demonstrate six types of network-attacking mutations (NAMs) including changes in kinase modulation, network rewiring as well as the genesis and extinction of specific phosphorylation sites. Through global, quantitative analysis of the exomes and (phospho-)proteomes of five ovarian cancer cell lines we identify and validate numerous NAMs. Finally, we explore the entire cancer genome repertoire and predict hundreds of NAMs affecting kinase and SH2 driven signaling. Our approach is scalable with the complexity of cancer genomes and cell signaling, and can be readily applied in personalized precision medicine.
2016-07-08 | PXD000901 | Pride
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:Neoplastic-stromal cell crosstalk regulates matrisome expression in pancreatic cancer
| PRJNA649774 | ENA
Project description:Interepithelial inflammatory crosstalk mediated by stromal fibroblast communication through hyaluronan
| PRJNA669672 | ENA
Project description:Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling processes. While thousands of mutations have been identified, mostly by genome-wide sequencing, systematic interpretation of their role in cancer and impact on cellular information processing is presently missing. Here, we propose a computational approach (ReKINect) to identify mutations attacking signaling networks. We demonstrate six types of network-attacking mutations (NAMs) including changes in kinase modulation, network rewiring as well as the genesis and extinction of specific phosphorylation sites. Through global, quantitative analysis of the exomes and (phospho-)proteomes of five ovarian cancer cell lines we identify and validate numerous NAMs. Finally, we explore the entire cancer genome repertoire and predict hundreds of NAMs affecting kinase and SH2 driven signaling. Our approach is scalable with the complexity of cancer genomes and cell signaling, and can be readily applied in personalized precision medicine.
2017-03-28 | MSV000080700 | MassIVE
Project description:Ascites or solid tumour from patients with ovarian cancer was collected and grown in culture as ex vivo models. Each sample has a mixture of tumour and stromal cells which were separated into individual cultures. Therefore each patient has tumour and stromal cultures originating from the same tissue collection. Variant calling (exome-seq) analysis was performed on these matched models to establish tumour specific mutations. Stromal cells were used to rule out germline mutations.
2020-03-13 | E-MTAB-7225 | biostudies-arrayexpress