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Imaging-Genomics Study Of Head-Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes And Genomic Mechanisms Via Integration Of TCGA And TCIA

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, Joy Zhang, Laurence Court, Yuan Ji, Clifton D Fuller, M.D. Anderson
doi: https://doi.org/10.1101/214312
Yitan Zhu
1Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
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Abdallah S.R. Mohamed
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
3Department of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, Alexandria University, Alexandria, Egypt
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Stephen Y Lai
4Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Shengjie Yang
1Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
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Aasheesh Kanwar
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Lin Wei
1Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
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Mona Kamal
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Subhajit Sengupta
1Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
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Hesham Elhalawani
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Heath Skinner
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Dennis S Mackin
5Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Jay Shiao
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Jay Messer
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Andrew Wong
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Yao Ding
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Joy Zhang
5Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Laurence Court
5Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Yuan Ji
1Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
6Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA
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Clifton D Fuller
2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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M.D. Anderson
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ABSTRACT

Purpose Recent data suggest that imaging radiomics features for a tumor could predict important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. For Head and Neck Squamous Cell Carcinoma (HNSCC), we perform a comprehensive study to discover the imaging-genomics associations and explore the potential of predicting tumor genomic alternations using radiomic features.

Methods Our retrospective study integrates whole-genome multi-omics data from The Cancer Genome Atlas (TCGA) with matched computed tomography imaging data from The Cancer Imaging Archive (TCIA) for the same set of 126 HNSCC patients. Linear regression analysis and gene set enrichment analysis are used to identify statistically significant associations between radiomic imaging features and genomic features. Random forest classifier is used to predict two key HNSCC molecular biomarkers, the status of human papilloma virus (HPV) and disruptive TP53 mutation, based on radiomic features.

Results Wide-spread and statistically significant associations are discovered between genomic features (including miRNA expressions, protein expressions, somatic mutations, and transcriptional activities, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of HPV and TP53 mutation status using radiomic features achieves an area under the receiver operating characteristics curve (AUC) of 0.71 and 0.641, respectively.

Conclusion Our analysis suggests that radiomic features are associated with genomic characteristics in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 05, 2017.
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Imaging-Genomics Study Of Head-Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes And Genomic Mechanisms Via Integration Of TCGA And TCIA
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, Joy Zhang, Laurence Court, Yuan Ji, Clifton D Fuller, M.D. Anderson
bioRxiv 214312; doi: https://doi.org/10.1101/214312
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Imaging-Genomics Study Of Head-Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes And Genomic Mechanisms Via Integration Of TCGA And TCIA
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, Joy Zhang, Laurence Court, Yuan Ji, Clifton D Fuller, M.D. Anderson
bioRxiv 214312; doi: https://doi.org/10.1101/214312

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