Integrative Survival-Based Molecular Profiling of Human Pancreatic Cancer
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA146901 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE32676: Integrative Survival-Based Molecular Profiling of Human Pancreatic Cancer [mRNA] GSE32678: Integrative Survival-Based Molecular Profiling of Human Pancreatic Cancer [miRNA] GSE32682: Integrative Survival-Based Molecular Profiling of Human Pancreatic Cancer [SNP] Refer to individual Series
2011-10-07 | E-GEOD-32688 | biostudies-arrayexpress
Project description:To perform an integrative profile of human pancreatic cancer (PDAC) to identify prognosis-significant genes and their related pathways. A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA & micro RNA (miRNA) expression in 25 early stage PDAC was performed. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n= 150 and 42) were then used as in vitro validation of the array findings. We find that EGFR, SRC signaling, and PI3K/AKT pathway activation are strongly linked to clinical disease progression. Furthermore, we identify two discrete subsets of pancreatic tumors characterized by either SRC or PI3K/AKT signaling that may dictate variable responses to targeted therapy. 42 human PDAC tumors and 7 non-malignant pancreas samples snap-frozen at the time of surgery were chosen. Representative H&E sections of each were evaluated by a practicing gastrointestinal pathologist to confirm diagnosis and determine relative percentage of malignant cells. Samples with tumor cell content >30% were chosen for final multi-platform analysis (N=25). Human pancreatic samples were profiled on Affymetrix HGU133 Plus 2 arrays per manufacturer's instructions.
2011-10-07 | E-GEOD-32676 | biostudies-arrayexpress
Project description:To perform an integrative profile of human pancreatic cancer (PDAC) to identify prognosis-significant genes and their related pathways. A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA & micro RNA (miRNA) expression in 25 early stage PDAC was performed. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n= 150 and 42) were then used as in vitro validation of the array findings. We find that EGFR, SRC signaling, and PI3K/AKT pathway activation are strongly linked to clinical disease progression. Furthermore, we identify two discrete subsets of pancreatic tumors characterized by either SRC or PI3K/AKT signaling that may dictate variable responses to targeted therapy. 42 human PDAC tumors and 7 non-malignant pancreas samples snap-frozen at the time of surgery were chosen. Representative H&E sections of each were evaluated by a practicing gastrointestinal pathologist to confirm diagnosis and determine relative percentage of malignant cells. Samples with tumor cell content >30% were chosen for final multi-platform analysis (N=25). Genomic miRNAa of human pancreatic samples were profiled on miRCURY LNA™ microRNA Array kit v.11.0 - human, mouse & rat arrays per manufacturer's instructions.
2011-10-07 | E-GEOD-32678 | biostudies-arrayexpress
Project description:To perform an integrative profile of human pancreatic cancer (PDAC) to identify prognosis-significant genes and their related pathways. A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA & micro RNA (miRNA) expression in 25 early stage PDAC was performed. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n= 150 and 42) were then used as in vitro validation of the array findings. We find that EGFR, SRC signaling, and PI3K/AKT pathway activation are strongly linked to clinical disease progression. Furthermore, we identify two discrete subsets of pancreatic tumors characterized by either SRC or PI3K/AKT signaling that may dictate variable responses to targeted therapy.
2011-10-08 | GSE32682 | GEO
Project description:To perform an integrative profile of human pancreatic cancer (PDAC) to identify prognosis-significant genes and their related pathways. A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA & micro RNA (miRNA) expression in 25 early stage PDAC was performed. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n= 150 and 42) were then used as in vitro validation of the array findings. We find that EGFR, SRC signaling, and PI3K/AKT pathway activation are strongly linked to clinical disease progression. Furthermore, we identify two discrete subsets of pancreatic tumors characterized by either SRC or PI3K/AKT signaling that may dictate variable responses to targeted therapy.
2011-10-08 | GSE32678 | GEO
Project description:To perform an integrative profile of human pancreatic cancer (PDAC) to identify prognosis-significant genes and their related pathways. A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA & micro RNA (miRNA) expression in 25 early stage PDAC was performed. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n= 150 and 42) were then used as in vitro validation of the array findings. We find that EGFR, SRC signaling, and PI3K/AKT pathway activation are strongly linked to clinical disease progression. Furthermore, we identify two discrete subsets of pancreatic tumors characterized by either SRC or PI3K/AKT signaling that may dictate variable responses to targeted therapy.
2011-10-08 | GSE32676 | GEO
Project description:To perform an integrative profile of human pancreatic cancer (PDAC) to identify prognosis-significant genes and their related pathways. A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA & micro RNA (miRNA) expression in 25 early stage PDAC was performed. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n= 150 and 42) were then used as in vitro validation of the array findings. We find that EGFR, SRC signaling, and PI3K/AKT pathway activation are strongly linked to clinical disease progression. Furthermore, we identify two discrete subsets of pancreatic tumors characterized by either SRC or PI3K/AKT signaling that may dictate variable responses to targeted therapy. 42 human PDAC tumors and 7 non-malignant pancreas samples snap-frozen at the time of surgery were chosen. Representative H&E sections of each were evaluated by a practicing gastrointestinal pathologist to confirm diagnosis and determine relative percentage of malignant cells. Samples with tumor cell content >30% were chosen for final multi-platform analysis (N=25). Copy number analysis of Affymetrix SNP 6.0 arrays was performed for 25 PDAC samples. The HapMap270 file supplied by Affymetrix was used as the reference model for copy number inference.
2011-10-07 | E-GEOD-32682 | biostudies-arrayexpress
Project description:Deeper insight into Pancreatic Ductal Adenocarcinoma (PDAC) survival heterogeneity using integrative analyses of individual and global transcriptome based networks
| PRJNA630983 | ENA
Project description:Purpose: The goals of this study are to delineate genes when comparing long-term (LT) and short-term (ST) PDAC survivors, and to exploit the merits of extensive integrative individual- and group-based transcriptome profiling. Method: Using a discovery cohort of 19 PDAC patients, we first performed differential gene expression (DGE) analysis comparing LT to ST PDAC groups. Second, we adopted unsupervised systems biology approaches to obtain meaningful gene modules showing associations to clinical features. Third, we created individual-level gene expression perturbation profiles and identified key regulators across the perturbed profiles of LT patients at the pathway and motif level. Furthermore, we applied two network context gene prioritization approaches for the depiction of PDAC specific regulatory genes. In particular, we used the random walk-based DADA method to develop PDAC disease modules considering the use of PDAC seed genes (via prior biological knowledge). As an alternative, we used NetICS to prioritize PDAC survival associated genes, by integrating information regarding group-based and individual-specific perturbed genes. Result: DGE analysis resulted in differences in the expression of genes involved in immune responses, cell cycle and metabolic pathways. Validation of a selection of DGEs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Detailed inspection of individual-specific omics changes across LT survivors revealed biological signatures associated with focal adhesion and extracellular matrix (ECM) receptors, commonly perturbed in at least 2 out of 9 LT survivors, implying a potential role in molecular-level heterogeneity of LT PDAC survivors. Network centric approaches such as NetICS (integrating group-based and individual-specific perturbed genes) and DADA (degree-aware disease gene prioritizing), identified various known oncogenes such as CUL1, SCF62, EGF, FOSL1, MMP9, and TGFB1. In addition, we identified TAC1, KCNH7, IRS4, DKK4, further warranting detailed follow-up investigations. Conclusion:Our proposed analytic workflow, combining clinical and omics data, and individual-level and group-level transcriptome profiling, highlighted transcriptome marks of PDAC long-term survival heterogeneity. The identified genes open up avenues towards better understanding the mechanisms underlying PDAC survival extension.
2022-05-02 | GSE150043 | GEO
Project description:Integrative Survival-Based Molecular Profiling of Human Pancreatic Cancer [mRNA]
| PRJNA154379 | ENA