Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcriptomic responses to Ivacaftor and prediction of Ivacaftor clinical responsiveness

ABSTRACT: Transcriptomic responses to Ivacaftor and prediction of Ivacaftor clinical responsiveness

PROVIDER: PRJNA528638 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR8769950_1.fastq.gz	Fastqsanger.gz
	SRR8769950_2.fastq.gz	Fastqsanger.gz
	SRR8769951_1.fastq.gz	Fastqsanger.gz
	SRR8769951_2.fastq.gz	Fastqsanger.gz
	SRR8769952_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 18

Similar Datasets

Transcriptomic responses to Ivacaftor and prediction of Ivacaftor clinical responsiveness

Project description:Rationale: Ivacaftor is a recently FDA-approved drug for the treatment of cystic fibrosis (CF) patients with at least one copy of the G511D mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The transcriptomic effect of Ivacaftor in CF patients remains unclear. Objectives: We aim to examine if and how the transcriptome of patients is influenced by Ivacaftor treatment and to determine if these data allow prediction of Ivacaftor responsiveness. Methods: We performed RNA-sequencing (RNA-seq) on PBMCs from CF patients and compared the transcriptomic changes before and after Ivacaftor treatments. Consensus clustering method is employed to stratify patients into sub-groups based on clinical responses post treatment, and determined differences in baseline gene expression. A random forest model is built to predict Ivacaftor responsiveness. Measurement and Main Results: We identified 239 genes that were significantly influenced by Ivacaftor in PBMC. The functions of these genes relate to cell differentiation, microbial infection, inflammation, Toll-like receptor signaling, and metabolism. We classified patients into “good” and “moderate” responders based on clinical response to Ivacraftor. We identified a panel of signature genes and built a statistical model for predicting CFTR modulator responsiveness. Despite a limited sample size, adequate prediction performance was achieved with an accuracy of 0.92. Conclusions: For the first time, the present study demonstrates profound transcriptomic impacts of Ivafactor in CF patients PBMCs and successfully built a statistical model for predicting the clinical responsiveness to Ivacaftor prior to treatment.

2019-08-27 | GSE128723 | GEO

Transcriptomic Responses to Lumacaftor/Ivacaftor Therapy in Cystic Fibrosis

Project description:Cystic fibrosis (CF) remains a life-shortening disease without a definitive cure. Novel therapeutics targeting the causative defect in the cystic fibrosis transmembrane conductance regulator (CFTR) gene are now in clinical use. Lumacaftor/ivacaftor is a CFTR modulator approved for patients homozygous for the CFTR mutation p.Phe508del, but there are wide variations in treatment responses preventing prediction of patient responses. We aimed to determine changes in gene expression related to treatment initiation and response. Whole-blood transcriptomics was performed using RNA-Seq in 20 patients with CF pre- and 6 months post-lumacaftor/ivacaftor (drug) initiation and 20 non-CF healthy controls. Correlation with clinical variables was performed by stratification via clinical responses. We identified 491 genes that were differentially expressed in CF patients (pre-drug) compared with non-CF controls. In addition, 36 genes were differentially expressed when comparing pre-drug to post-drug profiles within CF patients. Transcriptomics revealed novel pathways in CF patients at baseline compared to non-CF, and in clinical responders to lumacaftor/ivacaftor. Overall changes in gene expression post-lumacaftor/ivacaftor were modest compared to pre-drug CF profiles.

2019-01-03 | GSE124548 | GEO

Transcriptomic Responses to Ivacaftor and Prediction of Ivacaftor Clinical Responsiveness.

Project description:Ivacaftor is a drug that was recently approved by the U.S. Food and Drug Administration for the treatment of patients with cystic fibrosis (CF) and at least one copy of the G511D mutation in the CFTR (CF transmembrane conductance regulator) gene. The transcriptomic effect of ivacaftor in patients with CF remains unclear. Here, we sought to examine whether and how the transcriptome of patients is influenced by ivacaftor treatment, and to determine whether these data allow prediction of ivacaftor responsiveness. Our data originated from the G551D Observational Study (GOAL). We performed RNA sequencing (RNA-seq) on peripheral blood mononuclear cells (PBMCs) from 56 patients and compared the transcriptomic changes that occurred before and after ivacaftor treatment. We used consensus clustering to stratify patients into subgroups based on their clinical responses after treatment, and we determined differences between subgroups in baseline gene expression. A random forest model was built to predict ivacaftor responsiveness. We identified 239 genes (false discovery rate < 0.1) that were significantly influenced by ivacaftor in PBMCs. The functions of these genes relate to cell differentiation, microbial infection, inflammation, Toll-like receptor signaling, and metabolism. We classified patients into "good" and "moderate" responder groups based on their clinical response to ivacaftor. We identified a panel of signature genes and built a statistical model for predicting CFTR modulator responsiveness. Despite a limited sample size, adequate prediction performance was achieved with an accuracy of 0.92. In conclusion, for the first time, the present study demonstrates profound transcriptomic impacts of ivacaftor in PBMCs from patients with CF, and provides a pilot statistical model for predicting clinical responsiveness to ivacaftor before treatment.

| S-EPMC6827069 | biostudies-literature

Transcriptomic Responses to Lumacaftor/Ivacaftor Therapy in Cystic Fibrosis

Project description:Transcriptomic Responses to Lumacaftor/Ivacaftor Therapy in Cystic Fibrosis

| PRJNA512474 | ENA

Maternal Immune Responsiveness As CLinical Target for Preterm Birth Prevention (MIRACL)

Project description:Maternal Immune Responsiveness As CLinical Target for Preterm Birth Prevention (MIRACL)

| PRJNA849320 | ENA

Clinical treatment responsiveness in recurrent canine lymphoma correlates with Anaphase Promoting Complex activity

Project description:Clinical treatment responsiveness in recurrent canine lymphoma correlates with Anaphase Promoting Complex activity

| PRJNA496432 | ENA

Prediction of Recurrence-Free Survival in Postoperative NSCLC Patients—a Useful Prospective Clinical Practice

Project description:Background: One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results: The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions: Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies. Keywords: Recurrence Free Survival Analysis

2007-11-20 | GSE8894 | GEO

FIGHT-207- Anonymized Genomic Alterations and Clinical Responses

Project description:FIGHT-207- Anonymized Genomic Alterations and Clinical Responses

| PRJNA1089711 | ENA

Patient-derived organoids of pancreatic ductal adenocarcinoma for subtype determination and clinical outcome prediction

Project description:Two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the “Classical” and “Basal-like” subtypes. However, the “molecular” classification has not been applied in real-world clinical practice. This study aimed to establish patient-derived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction. We constructed a PDO library for morphologic and RNA-seq analyses and drug response assays in vitro. PDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells. Morphologically, PDOs were classified as gland-like structures (GL type) or densely proliferating inside (DP type). RNA-seq analysis revealed that the “morphological” subtype (GL vs. DP) roughly corresponded to the “molecular” subtype (“Classical” vs. “Basal-like”). The proposed “morphological” classification predicted clinical treatment response and prognosis; the median overall survival of patients with GL type were significantly longer than that of those with DP type (P < 0.005). The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK and autophagy inhibition. PDAC PDOs facilitated subtype determination and clinical outcome prediction, thereby advancing precision medicine for PDAC.

2024-04-11 | GSE228801 | GEO

Rescue of chloride and bicarbonate transport by elexacaftor-ivacaftor-tezacaftor in organoid-derived CF intestinal and cholangiocyte monolayers

Project description:In cystic fibrosis (CF), loss of CF transmembrane conductance regulator (CFTR)-dependent bicarbonate secretion precipitates the accumulation of viscous mucus in the lumen of respiratory and gastrointestinal epithelial tissues. We investigated whether the combination of elexacaftor (ELX), ivacaftor (IVA) and tezacaftor (TEZ), apart from its well-documented effect on Phe508del-CFTR-mediated chloride transport, also restores bicarbonate transport.

2021-12-14 | GSE182347 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data