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Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications
PURPOSE: To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non–small cell lung cancer (NSCLC). MATERIALS AND METHODS: A cohort of 113 patients with NSCLC diagnosed between April 2008 and September...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Radiological Society of North America
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749594/ https://www.ncbi.nlm.nih.gov/pubmed/28727543 http://dx.doi.org/10.1148/radiol.2017161845 |
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author | Zhou, Mu Leung, Ann Echegaray, Sebastian Gentles, Andrew Shrager, Joseph B. Jensen, Kristin C. Berry, Gerald J. Plevritis, Sylvia K. Rubin, Daniel L. Napel, Sandy Gevaert, Olivier |
author_facet | Zhou, Mu Leung, Ann Echegaray, Sebastian Gentles, Andrew Shrager, Joseph B. Jensen, Kristin C. Berry, Gerald J. Plevritis, Sylvia K. Rubin, Daniel L. Napel, Sandy Gevaert, Olivier |
author_sort | Zhou, Mu |
collection | PubMed |
description | PURPOSE: To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non–small cell lung cancer (NSCLC). MATERIALS AND METHODS: A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. RESULTS: RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. For example, nodule attenuation and margins are associated with the late cell-cycle genes, and a metagene that represents the EGF pathway was significantly correlated with the presence of ground-glass opacity and irregular nodules or nodules with poorly defined margins. CONCLUSION: Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of NSCLC. Online supplemental material is available for this article. |
format | Online Article Text |
id | pubmed-5749594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Radiological Society of North America |
record_format | MEDLINE/PubMed |
spelling | pubmed-57495942019-01-01 Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications Zhou, Mu Leung, Ann Echegaray, Sebastian Gentles, Andrew Shrager, Joseph B. Jensen, Kristin C. Berry, Gerald J. Plevritis, Sylvia K. Rubin, Daniel L. Napel, Sandy Gevaert, Olivier Radiology Original Research PURPOSE: To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non–small cell lung cancer (NSCLC). MATERIALS AND METHODS: A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. RESULTS: RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. For example, nodule attenuation and margins are associated with the late cell-cycle genes, and a metagene that represents the EGF pathway was significantly correlated with the presence of ground-glass opacity and irregular nodules or nodules with poorly defined margins. CONCLUSION: Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of NSCLC. Online supplemental material is available for this article. Radiological Society of North America 2018-01 2017-07-20 /pmc/articles/PMC5749594/ /pubmed/28727543 http://dx.doi.org/10.1148/radiol.2017161845 Text en 2017 by the Radiological Society of North America, Inc. http://creativecommons.org/licenses/by/4.0/ Published under a (http://creativecommons.org/licenses/by/4.0/) CC BY 4.0 license. |
spellingShingle | Original Research Zhou, Mu Leung, Ann Echegaray, Sebastian Gentles, Andrew Shrager, Joseph B. Jensen, Kristin C. Berry, Gerald J. Plevritis, Sylvia K. Rubin, Daniel L. Napel, Sandy Gevaert, Olivier Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications |
title | Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications |
title_full | Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications |
title_fullStr | Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications |
title_full_unstemmed | Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications |
title_short | Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications |
title_sort | non–small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749594/ https://www.ncbi.nlm.nih.gov/pubmed/28727543 http://dx.doi.org/10.1148/radiol.2017161845 |
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