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accession-icon GSE55322
Validated prediction of pro-invasive growth factors using a transcriptome-wide invasion signature derived from a complex 3-D invasion assay
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

The invasion of activated fibroblasts represents a key pathomechanism in fibrotic diseases, carcinogenesis and metastasis. Here, invading fibroblasts contribute to fibrotic extracellular matrix (ECM) formation and the initiation, progression, or resistance of cancer, respectively. To construct a transcriptome-wide signature of fibroblast invasion, we used a multiplex phenotypic 3D invasion assay using murine lung fibroblasts. Microarray-based gene expression profiles of invading and non-invading fibroblasts were highly distinct: 1049 genes were differentially regulated (>1.5-fold). An unbiased pathway analysis (Ingenuity) identified a significant enrichment for the functional clusters invasion of cells, idiopathic pulmonary fibrosis (IPF) and metastasis. Particularly, matrix metalloprotease13 (MMP13), transforming growth factor (TGF)1, Caveolin1 (Cav1), Phosphatase and Tensin Homolog (Pten), and secreted frizzled-related protein1 (Sfrp1) were among the highest regulated genes. In silico analysis by Ingenuity predicted TGF1, epidermal growth factor (EGF), fibroblast growth factor2 (FGF2), and platelet-derived growth factor (PDGF)-BB to induce invasion. As such, these growth factors were tested in the 3D invasion assay and displayed a significant induction of invasion, thus validating the transcriptome profile. Accordingly, our transcriptomic invasion signature describes the invading fibroblast phenotype in unprecedented detail and provides a tool for future functional studies of cell invasion and therapeutic modulation thereof.

Publication Title

Validated prediction of pro-invasive growth factors using a transcriptome-wide invasion signature derived from a complex 3D invasion assay.

Sample Metadata Fields

Sex

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accession-icon GSE138118
Expression profile of Urothelial carcinoma of the urinary bladder (UCB) or bladder cancer from Blood
  • organism-icon Homo sapiens
  • sample-icon 62 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.1 ST Array (hugene21st)

Description

The main aim of this study was to assess the changes in blood gene expression in UCB patients and to identify genes serving as biomarkers for UCB diagnosis and progression.

Publication Title

A Specific Blood Signature Reveals Higher Levels of S100A12: A Potential Bladder Cancer Diagnostic Biomarker Along With Urinary Engrailed-2 Protein Detection.

Sample Metadata Fields

Age

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accession-icon GSE108004
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
  • organism-icon Homo sapiens
  • sample-icon 30 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

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Sample Metadata Fields

Age, Specimen part, Disease, Disease stage

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accession-icon GSE107465
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]
  • organism-icon Homo sapiens
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately.

Publication Title

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Sample Metadata Fields

Age, Specimen part, Disease, Disease stage

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accession-icon SRP126623
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [RNA-Seq]
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents. Overall design: We measured the gene expression of samples from 30 different AML patients with acute myeloid leukemia in order to identify reliable gene expression markers for drug sensitivity. We used this dataset for validation. This Series represents 12 patient samples.

Publication Title

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Sample Metadata Fields

Age, Specimen part, Disease, Disease stage, Subject

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accession-icon GSE69512
Tetraspanin 3 is Required for the Development and Propagation of Acute Myelogenous Leukemia
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Msi2 is a critical regulatior of myeoid leukemia, and these data identify genes that are changed following Msi2 deletion in bcCML and de novo AML stem cells.

Publication Title

Tetraspanin 3 Is Required for the Development and Propagation of Acute Myelogenous Leukemia.

Sample Metadata Fields

Specimen part

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accession-icon GSE100020
Three-dimensional tumor cell growth stimulates autophagic flux and recapitulates chemotherapy resistance
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

Current preclinical models in tumor biology are limited in their ability to recapitulate relevant (patho-) physiological processes, including autophagy. Three-dimensional (3D) growth cultures have frequently been proposed to overcome the lack of correlation between two-dimensional (2D) monolayer cell cultures and human tumors in preclinical drug testing. Besides 3D growth, it is also advantageous to simulate shear stress, compound flux and removal of metabolites, e.g. via bioreactor systems, through which culture medium is constantly pumped at a flow rate reflecting physiological conditions. Here, we show that both Staticic 3D growth and 3D growth within a bioreactor system modulate key hallmarks of cancer cells, including proliferation and cell death as well as macroautophagy, a recycling pathway often activated by highly proliferative tumors to cope with metabolic stress. The autophagy-related gene expression profiles of 2D- and 3D-grown cells are substantially different, with the 3D-grown cells exhibiting an expression profile closely resembling the (patho-) physiological Statice of a tumor. Underscoring the importance of this pathway, autophagy-controlling transcription factors, such as TFEB and FOXO3, are upregulated in tumors, and 3D-grown cells have increased expression compared with cells grown in 2D conditions. Three-dimensional cultures depleted of the autophagy mediators BECN1, ATG5 or ATG7 or the transcription factor FOXO3, are more sensitive to cytotoxic treatment. Accordingly, combining cytotoxic treatment with compounds affecting late autophagic flux, such as chloroquine, renders the 3D-grown cells more susceptible to therapy and increases intracellular doxorubicin concentration to the level of 2D-grown cells. Altogether, 3D cultures are a valuable tool to study drug response of tumor cells, as these models recapitulate (patho-) physiologically relevant pathways, such as autophagy.

Publication Title

Three-dimensional tumor cell growth stimulates autophagic flux and recapitulates chemotherapy resistance.

Sample Metadata Fields

Specimen part, Cell line

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accession-icon GSE17100
Gene expression associated with lentiviral expression of PRAME in normal hematopoietic progenitors.
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

To identify gene expression that distinguishes hematopoietic cells that express PRAME from those that do not, normal CD34+ cells with forced PRAME expression were compared to cells without PRAME expression in culture over time (days 4, 7, 14) using Affymetrix HU-133A microarrays

Publication Title

The preferentially expressed antigen in melanoma (PRAME) inhibits myeloid differentiation in normal hematopoietic and leukemic progenitor cells.

Sample Metadata Fields

Specimen part, Time

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accession-icon GSE110817
A kinome-wide RNAi screen identifies ALK as a target to sensitize neuroblastoma cells for HDAC8-inhibitor treatment
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

The prognosis of advanced stage neuroblastoma patients remains poor and, despite intensive therapy, the 5-year survival rate remains less than 50%. We previously identified histone deacetylase (HDAC) 8 as an indicator of poor clinical outcome and a selective drug target for differentiation therapy in vitro and in vivo. Here we performed kinome-wide RNAi screening to identify genes that are synthetically lethal with HDAC8 inhibitors. These experiments identified the neuroblastoma predisposition gene ALK as a candidate gene. Accordingly, the combination of the ALK/MET inhibitor crizotinib and selective HDAC8 inhibitors (3-6M PCI-34051 or 10M 20a) efficiently killed neuroblastoma cell lines carrying wildtype ALK (SK-N-BE(2)-C, IMR5/75), amplified ALK (NB-1), and those carrying the activating ALK F1174L mutation (Kelly), and, in cells carrying the activating R1275Q mutation (LAN-5), combination treatment decreased viable cell count. The effective dose of crizotinib in neuroblastoma cell lines ranged from 0.05M (ALK-amplified) to 0.8M (wildtype ALK). The combinatorial inhibition of ALK and HDAC8 also decreased tumor growth in an in vivo zebrafish xenograft model. Bioinformatic analyses revealed that the mRNA expression level of HDAC8 was significantly correlated with that of ALK in two independent patient cohorts, the Academic Medical Center cohort (n=88) and the German Neuroblastoma Trial cohort (n=649), and co-expression of both target genes identified patients with very poor outcome. Mechanistically, HDAC8 and ALK converge at the level of receptor tyrosine kinase (RTK) signaling and their downstream survival pathways, such as ERK signaling. Combination treatment of HDAC8 inhibitor with crizotinib efficiently blocked the activation of growth receptor survival signaling and shifted the cell cycle arrest and differentiation phenotype toward effective cell death of neuroblastoma cell lines, including sensitization of resistant models, but not of normal cells. These findings reveal combined targeting of ALK and HDAC8 as a novel strategy for the treatment of neuroblastoma.

Publication Title

A kinome-wide RNAi screen identifies ALK as a target to sensitize neuroblastoma cells for HDAC8-inhibitor treatment.

Sample Metadata Fields

Specimen part

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accession-icon GSE143386
Reduced chromatin binding of MYC is a key effect of HDAC inhibition in MYC amplified meduloblastoma
  • organism-icon Homo sapiens
  • sample-icon 6 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

Reduced chromatin binding of MYC is a key effect of HDAC inhibition in MYC amplified medulloblastoma.

Sample Metadata Fields

Specimen part, Treatment

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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)

fund-icon Fund the CCDL

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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