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Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes

BACKGROUND: Radiomics-based non-invasive biomarkers are promising to facilitate the translation of therapeutically related molecular subtypes for treatment allocation of patients with head and neck squamous cell carcinoma (HNSCC). METHODS: We included 113 HNSCC patients from The Cancer Genome Atlas...

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Autores principales: Huang, Chao, Cintra, Murilo, Brennan, Kevin, Zhou, Mu, Colevas, A. Dimitrios, Fischbein, Nancy, Zhu, Shankuan, Gevaert, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642281/
https://www.ncbi.nlm.nih.gov/pubmed/31255659
http://dx.doi.org/10.1016/j.ebiom.2019.06.034
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author Huang, Chao
Cintra, Murilo
Brennan, Kevin
Zhou, Mu
Colevas, A. Dimitrios
Fischbein, Nancy
Zhu, Shankuan
Gevaert, Olivier
author_facet Huang, Chao
Cintra, Murilo
Brennan, Kevin
Zhou, Mu
Colevas, A. Dimitrios
Fischbein, Nancy
Zhu, Shankuan
Gevaert, Olivier
author_sort Huang, Chao
collection PubMed
description BACKGROUND: Radiomics-based non-invasive biomarkers are promising to facilitate the translation of therapeutically related molecular subtypes for treatment allocation of patients with head and neck squamous cell carcinoma (HNSCC). METHODS: We included 113 HNSCC patients from The Cancer Genome Atlas (TCGA-HNSCC) project. Molecular phenotypes analyzed were RNA-defined HPV status, five DNA methylation subtypes, four gene expression subtypes and five somatic gene mutations. A total of 540 quantitative image features were extracted from pre-treatment CT scans. Features were selected and used in a regularized logistic regression model to build binary classifiers for each molecular subtype. Models were evaluated using the average area under the Receiver Operator Characteristic curve (AUC) of a stratified 10-fold cross-validation procedure repeated 10 times. Next, an HPV model was trained with the TCGA-HNSCC, and tested on a Stanford cohort (N = 53). FINDINGS: Our results show that quantitative image features are capable of distinguishing several molecular phenotypes. We obtained significant predictive performance for RNA-defined HPV+ (AUC = 0.73), DNA methylation subtypes MethylMix HPV+ (AUC = 0.79), non-CIMP-atypical (AUC = 0.77) and Stem-like-Smoking (AUC = 0.71), and mutation of NSD1 (AUC = 0.73). We externally validated the HPV prediction model (AUC = 0.76) on the Stanford cohort. When compared to clinical models, radiomic models were superior to subtypes such as NOTCH1 mutation and DNA methylation subtype non-CIMP-atypical while were inferior for DNA methylation subtype CIMP-atypical and NSD1 mutation. INTERPRETATION: Our study demonstrates that radiomics can potentially serve as a non-invasive tool to identify treatment-relevant subtypes of HNSCC, opening up the possibility for patient stratification, treatment allocation and inclusion in clinical trials. FUND: Dr. Gevaert reports grants from National Institute of Dental & Craniofacial Research (NIDCR) U01 DE025188, grants from National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIBIB), R01 EB020527, grants from National Cancer Institute (NCI), U01 CA217851, during the conduct of the study; Dr. Huang and Dr. Zhu report grants from China Scholarship Council (Grant NO:201606320087), grants from China Medical Board Collaborating Program (Grant NO:15-216), the Cyrus Tang Foundation, and the Zhejiang University Education Foundation during the conduct of the study; Dr. Cintra reports grants from São Paulo State Foundation for Teaching and Research (FAPESP), during the conduct of the study.
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spelling pubmed-66422812019-07-29 Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes Huang, Chao Cintra, Murilo Brennan, Kevin Zhou, Mu Colevas, A. Dimitrios Fischbein, Nancy Zhu, Shankuan Gevaert, Olivier EBioMedicine Research paper BACKGROUND: Radiomics-based non-invasive biomarkers are promising to facilitate the translation of therapeutically related molecular subtypes for treatment allocation of patients with head and neck squamous cell carcinoma (HNSCC). METHODS: We included 113 HNSCC patients from The Cancer Genome Atlas (TCGA-HNSCC) project. Molecular phenotypes analyzed were RNA-defined HPV status, five DNA methylation subtypes, four gene expression subtypes and five somatic gene mutations. A total of 540 quantitative image features were extracted from pre-treatment CT scans. Features were selected and used in a regularized logistic regression model to build binary classifiers for each molecular subtype. Models were evaluated using the average area under the Receiver Operator Characteristic curve (AUC) of a stratified 10-fold cross-validation procedure repeated 10 times. Next, an HPV model was trained with the TCGA-HNSCC, and tested on a Stanford cohort (N = 53). FINDINGS: Our results show that quantitative image features are capable of distinguishing several molecular phenotypes. We obtained significant predictive performance for RNA-defined HPV+ (AUC = 0.73), DNA methylation subtypes MethylMix HPV+ (AUC = 0.79), non-CIMP-atypical (AUC = 0.77) and Stem-like-Smoking (AUC = 0.71), and mutation of NSD1 (AUC = 0.73). We externally validated the HPV prediction model (AUC = 0.76) on the Stanford cohort. When compared to clinical models, radiomic models were superior to subtypes such as NOTCH1 mutation and DNA methylation subtype non-CIMP-atypical while were inferior for DNA methylation subtype CIMP-atypical and NSD1 mutation. INTERPRETATION: Our study demonstrates that radiomics can potentially serve as a non-invasive tool to identify treatment-relevant subtypes of HNSCC, opening up the possibility for patient stratification, treatment allocation and inclusion in clinical trials. FUND: Dr. Gevaert reports grants from National Institute of Dental & Craniofacial Research (NIDCR) U01 DE025188, grants from National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIBIB), R01 EB020527, grants from National Cancer Institute (NCI), U01 CA217851, during the conduct of the study; Dr. Huang and Dr. Zhu report grants from China Scholarship Council (Grant NO:201606320087), grants from China Medical Board Collaborating Program (Grant NO:15-216), the Cyrus Tang Foundation, and the Zhejiang University Education Foundation during the conduct of the study; Dr. Cintra reports grants from São Paulo State Foundation for Teaching and Research (FAPESP), during the conduct of the study. Elsevier 2019-06-27 /pmc/articles/PMC6642281/ /pubmed/31255659 http://dx.doi.org/10.1016/j.ebiom.2019.06.034 Text en © 2019 Published by Elsevier B.V. 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
Huang, Chao
Cintra, Murilo
Brennan, Kevin
Zhou, Mu
Colevas, A. Dimitrios
Fischbein, Nancy
Zhu, Shankuan
Gevaert, Olivier
Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
title Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
title_full Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
title_fullStr Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
title_full_unstemmed Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
title_short Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
title_sort development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
topic Research paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642281/
https://www.ncbi.nlm.nih.gov/pubmed/31255659
http://dx.doi.org/10.1016/j.ebiom.2019.06.034
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