Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive.

Abstract

PURPOSE:

Recent data suggest that imaging radiomic features of a tumor could be indicative of important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. We performed a comprehensive study to discover the imaginggenomic associations in head and neck squamous cell carcinoma (HNSCC) and explore the potential of predicting tumor genomic alternations using radiomic features.

METHODS:

Our retrospective study integrated whole-genome multiomics data from The Cancer Genome Atlas with matched computed tomography imaging data from The Cancer Imaging Archive for the same set of 126 patients with HNSCC. Linear regression and gene set enrichment analysis were used to identify statistically significant associations between radiomic imaging and genomic features. Random forest classifier was used to predict the status of two key HNSCC molecular biomarkers, human papillomavirus and disruptive TP53 mutation, on the basis of radiomic features.

RESULTS:

Widespread and statistically significant associations were discovered between genomic features (including microRNA expression, somatic mutations, and transcriptional activity, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of human papillomavirus and TP53 mutation status using radiomic features achieved areas under the receiver operating characteristic curve of 0.71 and 0.641, respectively.

CONCLUSION:

Our exploratory study suggests that radiomic features are associated with genomic characteristics at multiple molecular layers in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.

• Authors: Yitan Zhu, Abdallah S R Mohamed, Stephen Y Lai, Shengjie Yang, Aasheesh Kanwar, Lin Wei, Mona Kamal, Subhajit Sengupta, Hesham Elhalawani, Heath Skinner, Dennis S Mackin, Jay Shiao, Jay Messer, Andrew Wong, Yao Ding, Lifei Zhang, Laurence Court, Yuan Ji, Clifton D Fuller
• Journal: JCO clinical cancer informatics (JCO Clin Cancer Inform) Vol. 3 Pg. 1-9 (02 2019) ISSN: 2473-4276 [Electronic] United States
• PMID: 30730765 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Chemical References

• Biomarkers, Tumor

Topics

• Aged
• Biomarkers, Tumor
• Computational Biology (methods)
• DNA Copy Number Variations
• Diagnostic Imaging
• Female
• Gene Expression Profiling
• Genetic Predisposition to Disease
• Genomics (methods)
• Humans
• Image Interpretation, Computer-Assisted
• Image Processing, Computer-Assisted
• Male
• Middle Aged
• Mutation
• Neoplasm Staging
• Reproducibility of Results
• Retrospective Studies
• Squamous Cell Carcinoma of Head and Neck (diagnostic imaging, genetics, pathology)
• Tomography, X-Ray Computed
• Workflow