Gene expression profiling of 82 patients with cervical cancer was performed. The expression data were correlated with copy number alterations of the same patients, as assessed with array CGH in a separate study, in order to identify drivers of cervical cancer carcinogenesis.
Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer.
Specimen part
View SamplesWe identified LAMP3 as a key driver gene of anti-viral subnetwork genes in cervical cancer patients. Therefore we tested this prediction using an in vitro system. This is the first direct demonstration of LAMP3 regulatory role in interferon-dependent immune response.
Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer.
Disease, Cell line, Treatment, Time
View SamplesUsing Affymetrix microarray technology we analyzed the gene expression profiles of the most important pathological categories of bladder cancer in order to detect potential marker genes. Applying an unsupervised cluster algorithm we observed clear differences between tumor and control samples, as well as between superficial and muscle invasive tumors. According to cluster results, the T1 high grade tumor type presented a global genetic profile which could not be distinguished from invasive cases. We described a new measure to classify differentially expressed genes and we compared it against the B-rank statistic as a standard method. According to this new classification method, the biological functions overrepresented in top differentially expressed genes when comparing tumor versus control samples were associated with growth, differentiation, immune system response, communication, cellular matrix and enzyme regulation. Comparing superficial versus invasive samples, the most important overrepresented biological category was growth and, specifically, DNA synthesis and mitotic cytoskeleton. On the other hand, some under expressed genes have been clearly related to muscular tissue contamination in control samples. Finally, we demonstrated that a pool strategy could be a good option to detect the best differentially expressed genes between two compared conditions.
DNA microarray expression profiling of bladder cancer allows identification of noninvasive diagnostic markers.
No sample metadata fields
View SamplesDocetaxel-based chemotherapy is the standard first-line therapy in metastatic castration-resistant prostate cancer. However, most patients eventually develop resistance to this treatment.
Identification of docetaxel resistance genes in castration-resistant prostate cancer.
Disease, Disease stage, Cell line, Treatment
View SamplesPanel of 53 melanoma cell lines were gene expression profiled by RNA-Seq for molecular classification Overall design: mRNA profiles of 53 melanoma cell lines
Interleukin 32 expression in human melanoma.
Disease, Disease stage, Cell line, Subject
View SamplesWe analyzed the global transcriptome signature over the time course of the cardiac differentiation from hESC by RNA-seq. We characterized the genome-wide transcriptome profile of 5 distinct stages; undifferentiated hESC (day 0), mesodermal precursor stage (hMP, day 2), cardiac progenitor stage (hCP, day 5), immature cardiomyocyte (hCM14) and hESC-CMS differentiated for 14 additional days (hCM28). While the stem cell signature decreases over the five stages, the signatures associated with heart and smooth muscle development increase, indicating the efficient cardiac differentiation of our protocol. Overall design: Five different temporal samples, two replicates for only first four samples day 0 through day 15
Glucose inhibits cardiac muscle maturation through nucleotide biosynthesis.
Specimen part, Subject
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Non-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance.
Specimen part
View SamplesMelanoma resistance to MAPK- or T cell checkpoint-targeted therapies represents a major clinical challenge, and treatment failures of MAPK-targeted therapies due to acquired resistance often require salvage immunotherapies. We show that genomic analysis of acquired resistance to MAPK inhibitors revealed key driver genes but failedto adequately account for clinical resistance. From a large-scale comparative analysis of temporal transcriptomes from patient-matched tumor biopsies, we discovered highly recurrent differential expression and signature outputs of c-MET, LEF1 and YAP1 as drivers of acquired MAPK inhibitor resistance. Moreover, integration of gene- and signature-based transcriptomic analysis revealed profound CD8 T cell deficiency detected in half of resistant melanomas in association with downregulation of dendritic cells and antigen presentation. We also propose a major methylomic basis to transcriptomic evolution under MAPK inhibitor selection. Thus, this database provides a rich informational resource, and the current landscape represents a benchmark to understanding melanoma therapeutic resistance. Overall design: Melanoma biopsies pre and post MAPKi treatment were sent for RNAseq analysis
Non-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance.
No sample metadata fields
View SamplesThe first clinical trial testing the combination of targeted therapy with a BRAF inhibitor vemurafenib and immunotherapy with a CTLA-4 antibody ipilimumab was terminated early due to significant liver toxicities, possibly due to paradoxical activation of the MAPK pathway by BRAF inhibitors in tumors with wild type BRAF. MEK inhibitors can potentiate the MAPK inhibition in tumor, while potentially alleviating the unwanted paradoxical MAPK activation. With a mouse model of syngeneic BRAFV600E driven melanoma (SM1), we tested whether the addition of the MEK inhibitor trametinib would enhance the immunosensitization effects of the BRAF inhibitor dabrafenib. Combination of dabrafenib and trametinib with pmel-1 adoptive cell transfer (ACT) showed complete tumor regression. Bioluminescent imaging and tumor infiltrating lymphocyte (TIL) phenotyping showed increased effector infiltration to tumors with dabrafenib, trametinib or dabrafenib plus trametinib with pmel-1 ACT combination. Intracellular IFN gamma staining of the TILs and in vivo cytotoxicity studies showed trametinib was not detrimental to the effector functions in vivo. Dabrafenib increased tumor associated macrophages and T regulatory cells (Tregs) in the tumors, which can be overcome by addition of trametinib. Microarray analysis revealed increased melanoma antigen, MHC expression, and global immune-related gene upregulation with the triple combination therapy. Given the up-regulation of PD-L1 seen with dabrafenib and/or trametinib combined with antigen specific ACT, we tested the triple combination of dabrafenib, trametinib with anti-PD1 therapy, and observed superior anti-tumor effect to SM1 tumors. Our findings support the testing of these combinations in patients with BRAFV600E mutant metastatic melanoma.
Improved antitumor activity of immunotherapy with BRAF and MEK inhibitors in BRAF(V600E) melanoma.
Specimen part, Treatment, Compound
View SamplesMelanoma resistance to MAPK- or T cell checkpoint-targeted therapies represents a major clinical challenge, and treatment failures of MAPK-targeted therapies due to acquired resistance often require salvage immunotherapies. We show that genomic analysis of acquired resistance to MAPK inhibitors revealed key driver genes but failedto adequately account for clinical resistance. From a large-scale comparative analysis of temporal transcriptomes from patient-matched tumor biopsies, we discovered highly recurrent differential expression and signature outputs of c-MET, LEF1 and YAP1 as drivers of acquired MAPK inhibitor resistance. Moreover, integration of gene- and signature-based transcriptomic analysis revealed profound CD8 T cell deficiency detected in half of resistant melanomas in association with downregulation of dendritic cells and antigen presentation. We also propose a major methylomic basis to transcriptomic evolution under MAPK inhibitor selection. Thus, this database provides a rich informational resource, and the current landscape represents a benchmark to understanding melanoma therapeutic resistance.
Non-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance.
Specimen part
View Samples