submitter_institution:CNIO Results

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Microarray (155)

Platform

Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2) (48)

Affymetrix Human Gene 1.0 ST Array (hugene10st) (31)

Affymetrix Mouse Genome 430 2.0 Array (mouse4302) (28)

Affymetrix Human Genome U133A Array (hgu133a) (20)

Affymetrix Human Exon 1.0 ST Array [transcript (gene) version, custom CDF (huex10st) (16)

Affymetrix Murine Genome U74A Version 2 Array (mgu74av2) (8)

Includes Publication

GSE71669

Integration analysis of three omics data using penalized regression methods: An application to bladder cancer

Homo sapiens
31 Downloadable Samples
Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer.

Sample Metadata Fields

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GSE71576

Integration analysis of three omics data using penalized regression methods: An application to bladder cancer (expression)

Homo sapiens
31 Downloadable Samples
Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Omics data integration is becoming necessary to investigate the still unknown genomic mechanisms of complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological mechanisms. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we applied penalized regression methods (LASSO and ENET) to explore relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples and have proposed a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whom expression levels were associated with both SNPs and GPGs. Importantly, we replicated results for 36 (75%) of them in an independent data set (TCGA). We checked the performance of the proposed method with a simulation study and further supported our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexibly and easy to implement when analyzing several omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complexity of disease genetic mechanisms.

Publication Title

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer.

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GSE51798

Transcriptional dissection of pancreatic tumors engrafted in mice

Homo sapiens
34 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Engraftment of primary pancreas ductal adenocarcinomas (PDAC) in mice to generate patient derived xenograft (PDX) models is a promising platform to for biological and therapeutic studies in this disease. However, these models are still incompletely characterized. Here, we measured the impact of the murine environment on the gene expression of the engrafted human tumoral cells. We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published microarray data from PDAC and hepatocellular carcinoma (HCC). Our results showed that PDX models derived from PDAC, or HCC, were clearly different to the cell lines derived from the same cancer tissues. Indeed, PDAC- and HCC-derived cell lines are indistinguishable one from the other based in their gene expression profiles. In contrast, the transcriptomes of PDAC and HCC PDX models are clearly different and more similar to their original tumor than to PDX models from the other tumor type. Interestingly, the main differences between pancreatic PDX models and human PDAC is the expression of genes involved in pathways related with extracellular matrix interactions and cell cycle regulation likely reflecting the adaptations of the tumors to the new environment. Furthermore, most of these differences are detected in the first passages after the tumor engraftment, indicating early phases of the adaptation process. In conclusion, different from conventional cancer cell lines, PDX models of PDAC retain similar gene expression profiles of PDAC. Expression changes are mainly related to genes involved in stromal pathways likely reflecting the adaptation to new environments. We also provide evidence of the stability of gene expression patterns over subsequent passages.

Publication Title

Transcriptional dissection of pancreatic tumors engrafted in mice.

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GSE65258

Gene expression profiling of normal murine lung cells, K-RasG12V driven lung hyperplasias and full-blown lung adenocarcinomas

Mus musculus
28 Downloadable Samples
Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Combined inhibition of DDR1 and Notch signaling is a therapeutic strategy for KRAS-driven lung adenocarcinoma.

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GSE37340

Preference of mitochondrial respiration over the Warbrg effect in breast tumor initiating cells in conditions of oxygen and glucose deprivation.

Homo sapiens
16 Downloadable Samples
Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)

Description

Background: cancer cells rely on glycolysis as main ATP source (Warbrg effect). Tumor-initiating cells (TICs) are the fraction of cells that give raise and repopulate tumors. TICs are exposed to prolonged periods of oxygen and glucose deprivation (OGD), as they live in a hypoxic niche and they withstand prolonged lack of blood vessels during initial tumorigenesis or metastasis formation (avascular phase). Warbrg effect is energetically inefficient; we hypothesize that TICs might have differential metabolic features. Tumor eradication requires killing TICS; finding such features would have therapeutic implications.

Publication Title

No associated publication

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GSE65257

Gene expression profiling of advanced murine K-RasG12V lung adenocarcinomas

Mus musculus
13 Downloadable Samples
Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

We aimed to analyze the transcriptional profile of full-blown murine lung adenocarcinomas driven by K-RasG12V oncogene.

Publication Title

Combined inhibition of DDR1 and Notch signaling is a therapeutic strategy for KRAS-driven lung adenocarcinoma.

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Specimen part

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GSE65256

Gene expression profiling of normal lung cells and K-RasG12V driven hyperplasias

Mus musculus
15 Downloadable Samples
Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

We aimed to analyze the transcriptional profile of lung epithelial cells early after the expression of a resident K-RasG12V oncogene. This approach was based on the rationale that valuable therapeutic targets should be easier to detect in the first stages of tumor development due to tumor heterogeneity which occurr at late stages.

Publication Title

Combined inhibition of DDR1 and Notch signaling is a therapeutic strategy for KRAS-driven lung adenocarcinoma.

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GSE54483

Colorectal cancer classification based on gene expression is not associated with FOLFIRI response

Homo sapiens
10 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Sadanandam et al. (2013) recently published a study based on the use of microarray data to classify colorectal cancer (CRC) samples. The classification claimed to have strong clinical implications, as reflected in the paper title: A colorectal cancer classification system that associates cellular phenotype and responses to therapy. They defined five subtypes: (i) inflammatory; (ii) goblet-like; (iii) enterocyte; (iv) transit-amplifying; and (v) stem-like. Based on drug sensitivity data from 21 patients, they also reported that the so-called stem-like subtype show differential sensitivity to FOLFIRI. This is the key result in their publication, since it implies a direct relation between the subtype and the choice of CRC therapy (i.e. FOLFIRI response). However, our analyses using the same drug sensitivity data and results from additional patients showed that the CRC classification reported by Sadanandam et al. is not predictive of FOLFIRI response.