Preclinical modeling of leiomyosarcoma identifies susceptibility to transcriptional CDK inhibitors through antagonism of E2F-driven oncogenic gene expression.
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA759951 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Preclinical modeling of leiomyosarcoma identifies susceptibility to transcriptional CDK inhibitors through antagonism of E2F-driven oncogenic gene expression.
| PRJNA759944 | ENA
Project description:PurposeLeiomyosarcoma (LMS) is a neoplasm characterized by smooth muscle differentiation, complex copy-number alterations, tumor suppressor loss, and the absence of recurrent driver mutations. Clinical management for advanced disease relies on the use of empiric cytotoxic chemotherapy with limited activity, and novel targeted therapies supported by preclinical research on LMS biology are urgently needed. A lack of fidelity of established LMS cell lines to their mesenchymal neoplasm of origin has limited translational understanding of this disease, and few other preclinical models have been established. Here, we characterize patient-derived xenograft (PDX) models of LMS, assessing fidelity to their tumors of origin and performing preclinical evaluation of candidate therapies.Experimental designWe implanted 49 LMS surgical samples into immunocompromised mice. Engrafting tumors were characterized by histology, targeted next-generation sequencing, RNA sequencing, and ultra-low passage whole-genome sequencing. Candidate therapies were selected based on prior evidence of pathway activation or high-throughput dynamic BH3 profiling.ResultsWe show that LMS PDX maintain the histologic appearance, copy-number alterations, and transcriptional program of their parental tumors across multiple xenograft passages. Transcriptionally, LMS PDX cocluster with paired LMS patient-derived samples and differ primarily in host-related immunologic and microenvironment signatures. We identify susceptibility of LMS PDX to transcriptional cyclin-dependent kinase (CDK) inhibition, which disrupts an E2F-driven oncogenic transcriptional program and inhibits tumor growth.ConclusionsOur results establish LMS PDX as valuable preclinical models and identify strategies to discover novel vulnerabilities in this disease. These data support the clinical assessment of transcriptional CDK inhibitors as a therapeutic strategy for patients with LMS.
| S-EPMC9167705 | biostudies-literature
Project description:A genomics-based approach to identify pharmacodynamic biomarkers was used for a CDK (cyclin-dependent kinase) inhibitory drug. R547 is a potent CDK inhibitor with a potent anti-proliferative effect at pharmacologically relevant doses, and is currently in Phase I clinical trials. Utilizing preclinical data derived from microarray experiments, we identified pharmacodynamic biomarkers to test in blood samples from patients in clinical trials. These candidate biomarkers were chosen based on several criteria: relevance to the mechanism of action of R547, dose responsiveness in preclinical models, and measurable expression in blood samples. We identified 26 potential biomarkers of R547 action and tested their clinical validity in patient blood samples by quantitative real-time PCR analysis.
2009-03-26 | GSE15392 | GEO
Project description:A genomics-based approach to identify pharmacodynamic biomarkers was used for a CDK (cyclin-dependent kinase) inhibitory drug. R547 is a potent CDK inhibitor with a potent anti-proliferative effect at pharmacologically relevant doses, and is currently in Phase I clinical trials. Utilizing preclinical data derived from microarray experiments, we identified pharmacodynamic biomarkers to test in blood samples from patients in clinical trials. These candidate biomarkers were chosen based on several criteria: relevance to the mechanism of action of R547, dose responsiveness in preclinical models, and measurable expression in blood samples. We identified 26 potential biomarkers of R547 action and tested their clinical validity in patient blood samples by quantitative real-time PCR analysis.
2009-03-26 | GSE15395 | GEO
Project description:A genomics-based approach to identify pharmacodynamic biomarkers was used for a CDK (cyclin-dependent kinase) inhibitory drug. R547 is a potent CDK inhibitor with a potent anti-proliferative effect at pharmacologically relevant doses, and is currently in Phase I clinical trials. Utilizing preclinical data derived from microarray experiments, we identified pharmacodynamic biomarkers to test in blood samples from patients in clinical trials. These candidate biomarkers were chosen based on several criteria: relevance to the mechanism of action of R547, dose responsiveness in preclinical models, and measurable expression in blood samples. We identified 26 potential biomarkers of R547 action and tested their clinical validity in patient blood samples by quantitative real-time PCR analysis.
2009-03-26 | GSE15389 | GEO
Project description:A genomics-based approach to identify pharmacodynamic biomarkers was used for a CDK (cyclin-dependent kinase) inhibitory drug. R547 is a potent CDK inhibitor with a potent anti-proliferative effect at pharmacologically relevant doses, and is currently in Phase I clinical trials. Utilizing preclinical data derived from microarray experiments, we identified pharmacodynamic biomarkers to test in blood samples from patients in clinical trials. These candidate biomarkers were chosen based on several criteria: relevance to the mechanism of action of R547, dose responsiveness in preclinical models, and measurable expression in blood samples. We identified 26 potential biomarkers of R547 action and tested their clinical validity in patient blood samples by quantitative real-time PCR analysis. Experiment Overall Design: 40 samples were initially run, 37 passed QC and were analyzed, samples were run in 6 independent replicates and were treated with either vehicle, R547 at the IC90, or R547 at 3 times the IC90. Additionally, untreated time zero samples were analyzed as a further control
2009-03-28 | E-GEOD-15389 | biostudies-arrayexpress
Project description:A genomics-based approach to identify pharmacodynamic biomarkers was used for a CDK (cyclin-dependent kinase) inhibitory drug. R547 is a potent CDK inhibitor with a potent anti-proliferative effect at pharmacologically relevant doses, and is currently in Phase I clinical trials. Utilizing preclinical data derived from microarray experiments, we identified pharmacodynamic biomarkers to test in blood samples from patients in clinical trials. These candidate biomarkers were chosen based on several criteria: relevance to the mechanism of action of R547, dose responsiveness in preclinical models, and measurable expression in blood samples. We identified 26 potential biomarkers of R547 action and tested their clinical validity in patient blood samples by quantitative real-time PCR analysis. Experiment Overall Design: 52 samples were initially run, 45 passed QC and were analyzed, samples were run in 4 independent replicates and were treated with either vehicle, R547 at the IC50 concentration, R547 at the IC90, or R547 at 3 times the IC90. Samples were harvested at 2, 6, and 24 hours post treatment. Additionally, untreated time zero samples were analyzed as a further control
2009-03-28 | E-GEOD-15392 | biostudies-arrayexpress
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.
2023-12-13 | GSE222045 | GEO
Project description:Global gene expression analysis of inflammatory leiomyosarcoma (ILMS) and conventional leiomyosarcoma (LMS).
2013-05-22 | GSE37992 | GEO
Project description:Uterine leiomyosarcoma (ULMS) is a poorly understood gynecologic cancer with few effective treatments. This study explores molecular events involved in ULMS with the goal of identifying strategies. Genome-wide transcriptional profiling were used to compare clinically well-annotated specimens of myometrium, leiomyoma and leiomyosarcoma.
2012-04-26 | E-GEOD-36610 | biostudies-arrayexpress