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accession-icon GSE58722
Checkpoint blockade immunotherapy relies on T-bet but not Eomes to induce effector function in tumor infiltrating CD8+ T cells
  • organism-icon Mus musculus
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Coinhibitory receptor blockade is a promising strategy to boost immunity against a variety of human cancers. However, many patients still do not benefit from this treatment, and responders often experience immune-related toxicities. These issues highlight the need for improved understanding of checkpoint blockade, but the T cell-intrinsic signaling pathways and gene expression profiles engaged during treatment are not well defined, particularly for combination approaches. We utilized a murine model of CD8+ T cell tolerance to address these issues.

Publication Title

Checkpoint blockade immunotherapy relies on T-bet but not Eomes to induce effector function in tumor-infiltrating CD8+ T cells.

Sample Metadata Fields

Specimen part

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accession-icon GSE80028
Vaccination and topical imiquimod treatment promote immune signatures in melanoma
  • organism-icon Homo sapiens
  • sample-icon 11 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Introduction: Infiltration of cancers by T-cells is associated with improved patient survival and response to immune therapies; however, optimal approaches to induce T-cell infiltration of tumors are not known. This study tests the hypothesis that topical treatment of melanoma metastases with the TLR7 agonist imiquimod treatment plus administration of a multipeptide cancer vaccine will improve immune cell infiltration of melanoma metastases. Patients and Methods: Eligible patients were immunized with a vaccine comprised of 12 melanoma peptides and a tetanus toxoid-derived helper peptide, and imiquimod was applied topically to tumors daily. Adverse events (AE; CTCAE v4.03) were recorded and effects on the tumor microenvironment (TME) were evaluated from sequential tumor biopsies. T-cell responses were assessed by IFNgamma ELIspot assay, and T-cell tetramer staining. Patient tumors were evaluated for immune cell infiltration, cytokine and chemokine production, and gene expression. Results and Conclusions: Four eligible patients were enrolled, and administration of imiquimod and vaccination was well tolerated in these patients. Circulating T-cell responses to the vaccine were detected by ex vivo ELIspot assay in 3 of 4 patients. Treatment of metastases with imiquimod induced immune cell infiltration and favorable gene signatures in the patients with circulating T-cell responses. This study supports further study of topical imiquimod combined with vaccines or other immune therapies for the treatment of melanoma. Precis: This clinical trial tested topical application of imiquimod to melanoma metastases combined with a melanoma vaccine. The regimen dramatically upregulated immune rejection gene signatures in melanoma metastases and increased T-cell infiltrate.

Publication Title

Topical treatment of melanoma metastases with imiquimod, plus administration of a cancer vaccine, promotes immune signatures in the metastases.

Sample Metadata Fields

Specimen part, Disease, Disease stage

View Samples
accession-icon GSE70124
Genomic structure, evolution and molecular classification of acute myeloid leukemia
  • organism-icon Homo sapiens
  • sample-icon 36 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Background: Acute myeloid leukemia (AML) is driven by somatic mutations and genomic rearrangements affecting >20 genes. Many of these are recent discoveries and how this molecular heterogeneity dictates AML pathophysiology and clinical outcome remains unclear. Methods: We sequenced 111 leukemia genes for driver mutations in 1540 AML patients with cytogenetic and clinical data. We modeled AMLs genomic structure, defining genetic interactions, patterns of temporal evolution and clinical correlations. Results: We identified 5,236 driver mutations involving 77 loci, including hotspot mutations in MYC. We found 1 driver mutation in 96% patients, and 2 in 85%. Gene mutations implicated in age related clonal hematopoiesis (DNMT3A, ASXL1, TET2) were the earliest in AML evolution, followed by highly specific and ordered patterns of co-mutation in chromatin, transcription and splicing regulators, NPM1 and signaling genes. The patterns of co-mutation compartmentalize AML into 12 discrete molecular classes, each presenting with distinct clinical manifestation. Amongst these, mutations in chromatin and spliceosome genes demarcate a molecularly heterogeneous subgroup enriched for older AML patients currently classified as intermediate risk and results in adverse prognosis. Two- and three-way genetic interactions often implicating rare genes/mutation-hotspots, markedly redefined clinical response and long-term curability, with the NPM1:DNMT3A:FLT3ITD genotype (6% patients) identifying poor prognosis disease, whereas within the same class NPM1:DNMT3A:NRASG12/13 (3%) associated with favorable outlooks. Conclusions: 79% of AML is molecularly classified in 12 genomic subgroups. These represent distinct molecular phylogenies, implicating complex genotypes. Delineation of higher-order genomic relationships, guide the development of personally tailored classification, prognostication and clinical protocols. Similar studies across cancer types are warranted.

Publication Title

Genomic Classification and Prognosis in Acute Myeloid Leukemia.

Sample Metadata Fields

Specimen part, Disease

View Samples
accession-icon GSE22316
PBRM1 Knockdown in RCC Cell Lines
  • organism-icon Homo sapiens
  • sample-icon 82 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

PBRM1 was found to be mutated in a high percentage of clear cell RCCs. We performed knockdown of PBRM1 via siRNA and compared with scrambled control in three different RCC cell lines.

Publication Title

Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma.

Sample Metadata Fields

Specimen part, Cell line, Treatment

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accession-icon GSE17895
Somatic Mutation Screen of Clear Cell RCC
  • organism-icon Homo sapiens
  • sample-icon 109 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE17818
Somatic Mutation Screen of Clear Cell RCC II
  • organism-icon Homo sapiens
  • sample-icon 109 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Systematic somatic mutation screening of 4000 genes in human clear cell renal cell carcinoma. Information on corresponding somatic mutations in each sample can be found at http://www.sanger.ac.uk/genetics/CGP/Studies/.

Publication Title

Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes.

Sample Metadata Fields

No sample metadata fields

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accession-icon E-MEXP-1310
Transcription profiling of Arabidopsis seedlings treated with NAE(12:0)
  • organism-icon Arabidopsis thaliana
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Arabidopsis ATH1 Genome Array (ath1121501)

Description

Transcript profiling and gene expression studies in NAE-treated seedlings: Seeds were germinated and seedlings maintained for 4 d in liquid MS media supplemented with 35 uM NAE(12:0)(N-lauroylethanolamine) prior to RNA isolation.

Publication Title

N-Acylethanolamine metabolism interacts with abscisic acid signaling in Arabidopsis thaliana seedlings.

Sample Metadata Fields

Age, Specimen part, Compound

View Samples
accession-icon GSE80796
Gene expression profiling of nasal epithelial cells in current and former smokers with and without lung cancer
  • organism-icon Homo sapiens
  • sample-icon 505 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

We previously derived and validated a bronchial epithelial gene expression biomarker to detect lung cancer in current and former smokers. Given that bronchial and nasal epithelium gene expression is similarly altered by cigarette smoke exposure, we sought to determine if cancer-associated gene expression might also be detectable in more readily accessible nasal epithelium. Nasal epithelial brushings were prospectively collected from current and former smokers with pulmonary lesions suspicious for lung cancer in the AEGIS-1 (n=375) and AEGIS-2 (n=130) clinical trials and gene expression profiled using microarrays. Using the 375 AEGIS 1 samples, we identified 535 genes that were differentially expressed in the nasal epithelium of patients who were ultimately diagnosed with lung cancer vs. those with benign disease after one year of follow-up (p<0.001). Using bronchial gene expression data from 299 AEGIS-1 patients (including 157 patients with matched nasal and bronchial expression data), we found significantly concordant cancer-associated gene expression differences between the two airway sites (p<0.001). Differentially expressed genes were enriched for genes associated with the regulation of apoptosis, mitotic cell cycle, and immune system signaling. A nasal lung cancer classifier derived in the AEGIS-1 cohort that combined clinical factors and nasal gene expression had significantly higher AUC (0.80) and sensitivity (0.94) over a clinical-factor only model (p<0.05) in independent samples from the AEGIS-2 cohort (n=130). These results suggest that the airway epithelial field of lung cancer-associated injury in current and former smokers extends to the nose and demonstrates the potential of using nasal gene expression as a non-invasive biomarker for the detection of lung cancer.

Publication Title

Shared Gene Expression Alterations in Nasal and Bronchial Epithelium for Lung Cancer Detection.

Sample Metadata Fields

Sex, Age

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accession-icon GSE32285
Genome-wide analysis of lupus immune complex stimulation and how this response is regulated by C1q
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina HumanRef-8 v3.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Plasmacytoid dendritic cells and C1q differentially regulate inflammatory gene induction by lupus immune complexes.

Sample Metadata Fields

Specimen part, Treatment, Subject

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accession-icon GSE32278
Genome-wide analysis of lupus immune complex stimulation of purified CD14+ monocytes and how this response is regulated by C1q
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina HumanRef-8 v3.0 expression beadchip

Description

The goal of this study was to determine what genes are up- and down-regulated in response to lupus immune complexes in purified CD14+ monocyte stimulations. Our results have shown that novel genes are induced by immune complexes but the response is less robust when using purified monocytes versus total PBMCs

Publication Title

Plasmacytoid dendritic cells and C1q differentially regulate inflammatory gene induction by lupus immune complexes.

Sample Metadata Fields

Specimen part, Treatment, Subject

View Samples

refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Developed by the Childhood Cancer Data Lab

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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