Cargando…
A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging
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 stat...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6607084/ https://www.ncbi.nlm.nih.gov/pubmed/31126891 http://dx.doi.org/10.1016/j.ebiom.2019.05.007 |
_version_ | 1783432024063737856 |
---|---|
author | Johnson, Kipp W. Glicksberg, Benjamin S. Shameer, Khader Vengrenyuk, Yuliya Krittanawong, Chayakrit Russak, Adam J. Sharma, Samin K. Narula, Jagat N. Dudley, Joel T. Kini, Annapoorna S. |
author_facet | Johnson, Kipp W. Glicksberg, Benjamin S. Shameer, Khader Vengrenyuk, Yuliya Krittanawong, Chayakrit Russak, Adam J. Sharma, Samin K. Narula, Jagat N. Dudley, Joel T. Kini, Annapoorna S. |
author_sort | Johnson, Kipp W. |
collection | PubMed |
description | 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: 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: 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: 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. |
format | Online Article Text |
id | pubmed-6607084 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-66070842019-07-15 A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging Johnson, Kipp W. Glicksberg, Benjamin S. Shameer, Khader Vengrenyuk, Yuliya Krittanawong, Chayakrit Russak, Adam J. Sharma, Samin K. Narula, Jagat N. Dudley, Joel T. Kini, Annapoorna S. EBioMedicine Research paper 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: 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: 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: 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. Elsevier 2019-05-22 /pmc/articles/PMC6607084/ /pubmed/31126891 http://dx.doi.org/10.1016/j.ebiom.2019.05.007 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research paper Johnson, Kipp W. Glicksberg, Benjamin S. Shameer, Khader Vengrenyuk, Yuliya Krittanawong, Chayakrit Russak, Adam J. Sharma, Samin K. Narula, Jagat N. Dudley, Joel T. Kini, Annapoorna S. A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging |
title | A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging |
title_full | A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging |
title_fullStr | A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging |
title_full_unstemmed | A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging |
title_short | A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging |
title_sort | transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: validation by serial intracoronary oct imaging |
topic | Research paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6607084/ https://www.ncbi.nlm.nih.gov/pubmed/31126891 http://dx.doi.org/10.1016/j.ebiom.2019.05.007 |
work_keys_str_mv | AT johnsonkippw atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT glicksbergbenjamins atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT shameerkhader atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT vengrenyukyuliya atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT krittanawongchayakrit atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT russakadamj atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT sharmasamink atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT narulajagatn atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT dudleyjoelt atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT kiniannapoornas atranscriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT johnsonkippw transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT glicksbergbenjamins transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT shameerkhader transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT vengrenyukyuliya transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT krittanawongchayakrit transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT russakadamj transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT sharmasamink transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT narulajagatn transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT dudleyjoelt transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging AT kiniannapoornas transcriptomicmodeltopredictincreaseinfibrouscapthicknessinresponsetohighdosestatintreatmentvalidationbyserialintracoronaryoctimaging |