A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging.
Aged
Aged, 80 and over
Algorithms
Biomarkers
Computational Biology
/ methods
Female
Gene Expression Profiling
/ methods
Gene Ontology
Humans
Hydroxymethylglutaryl-CoA Reductase Inhibitors
/ administration & dosage
Male
Middle Aged
Models, Biological
Plaque, Atherosclerotic
/ diagnosis
Prognosis
ROC Curve
Tomography, Optical Coherence
Transcriptome
Treatment Outcome
Optical coherence tomography
Personalized medicine
Predictive modeling
Statin
Journal
EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039
Informations de publication
Date de publication:
Jun 2019
Jun 2019
Historique:
received:
15
02
2019
revised:
15
04
2019
accepted:
03
05
2019
pubmed:
28
5
2019
medline:
19
12
2019
entrez:
26
5
2019
Statut:
ppublish
Résumé
Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy. FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8-10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers. Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism. In this pilot study, transcriptomic models could predict if FCT increased following 8-10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.
Sections du résumé
BACKGROUND
BACKGROUND
Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy.
METHODS
METHODS
FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8-10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers.
FINDINGS
RESULTS
Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism.
INTERPRETATION
CONCLUSIONS
In this pilot study, transcriptomic models could predict if FCT increased following 8-10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.
Identifiants
pubmed: 31126891
pii: S2352-3964(19)30308-1
doi: 10.1016/j.ebiom.2019.05.007
pmc: PMC6607084
pii:
doi:
Substances chimiques
Biomarkers
0
Hydroxymethylglutaryl-CoA Reductase Inhibitors
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
41-49Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.