Dataset Information

Next-Generation Sequencing Facilitates Quantitative Analysis of Control and BET bromodomain Inhibitor-treated Uterine Leiomyosarcoma Cell Transcriptome

ABSTRACT: We report that BET bromodomain inhibition alters multiple biological pathways in uterine leiomyosarcoma cells

ORGANISM(S): Homo sapiens

PROVIDER: GSE275087 | GEO | 2024/09/18

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Objective: The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. Methods: We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, twenty genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using Gene-Mania. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets). Results: We discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication, cell cycle, and DNA damage checkpoint. Among evaluated machine learning classifiers, the DNN had the highest accuracy and average AUC value in training data set. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed AUC of 0.917 and 0.914 supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample. Conclusion: The transcriptome-based classifier accurately distinguished uterine leiomyoma from leiomyosarcoma.

Project description:Bromodomain inhibition comprises a promising therapeutic strategy in cancer, particularly for hematologic malignancies. To date, however, genomic biomarkers to direct clinical translation have been lacking. We conducted a cell-based screen of genetically-defined cancer cell lines using a prototypical inhibitor of BET bromodomains. Integration of genetic features with chemosensitivity data revealed a robust correlation between MYCN amplification and sensitivity to bromodomain inhibition. We characterized the mechanistic and translational significance of this finding in neuroblastoma, a childhood cancer with frequent amplification of MYCN. Genome-wide expression analysis demonstrated downregulation of the MYCN transcriptional program accompanied by suppression of MYCN transcription. Functionally, bromodomain-mediated inhibition of MYCN impaired growth and induced apoptosis in neuroblastoma. BRD4 knock-down phenocopied these effects, establishing BET bromodomains as transcriptional regulators of MYCN. BET inhibition conferred a significant survival advantage in three in vivo neuroblastoma models, providing a compelling rationale for developing BET bromodomain inhibitors in patients with neuroblastoma. Significance: Biomarkers of response to small-molecule inhibitors of BET bromodomains, a new compound class with promising anti-cancer activity, have been lacking. Here, we reveal MYCN amplification as a strong genetic predictor of sensitivity to BET bromodomain inhibitors, demonstrate a mechanistic rationale for this finding, and provide a translational framework for clinical trial development of BET bromodomain inhibitors for pediatric patients with MYCN-amplified neuroblastoma. JQ1 is a novel thieno-triazolo-1,4-diazepine, which displaces BET bromodomains from chromatin by competitively binding to the acetyl lysine recognition pocket. BE(2)-C and Kelly cells were treated in triplicate with 1 ÂµM JQ1 or DMSO for 24 hours. RNA was extracted and a decrease in MYCN transcript was confirmed by real time RT-PCR as described above. The samples were profiled using the Affymetrix PrimeView Human Gene Expression Array (Affymetrix) at Beth Israel Deaconess Medical Center (Boston, MA, USA).

Dataset Information

Next-Generation Sequencing Facilitates Quantitative Analysis of Control and BET bromodomain Inhibitor-treated Uterine Leiomyosarcoma Cell Transcriptome

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure