Genetic alterations associated with 18F-fluorodeoxyglucose positron emission tomography/computed tomography in head and neck squamous cell carcinoma

• Main Authors: Sangwon Han, Jungsu S. Oh, Hyo Sang Lee, Jae Seung Kim
• Format: Article
• Language: English
• Published: Elsevier 2021-02-01
• Series: Translational Oncology
• Subjects: Squamous cell carcinoma of head and neck; Positron emission tomography computed tomography; Glycolysis; Mutation; Machine learning
• Online Access: http://www.sciencedirect.com/science/article/pii/S1936523320304800

Background: We investigated the relationship between genetic alterations and 18F-FDG PET/CT findings in head and neck squamous cell carcinoma (HNSC). Methods: Using mRNA-sequences of HNSC samples (480 patients) from the Cancer Genome Atlas (TCGA) portal, gene coexpression networks were constructed via a weighted correlation network analysis (WGCNA) algorithm, and their association with the tumor-to-blood signal ratio on 18F-FDG PET/CT data (21 patients) was explored. An elastic-net regression model was developed to estimate the PET tumor-to-blood ratio from the gene networks and to derive an FDG signature score (FDGSS). The FDGSS was evaluated with regard to clinical variables and general mutational profiles, as well as alterations to oncogenic signaling pathways. Findings: The FDGSS values differed across clinical stages (p = 0.027), HPV-status (p< 0.001), and molecular subtypes of HNSC (p< 0.001). Multivariate Cox regression demonstrated that FDGSS was an independent predictor for overall (p = 0.019) and progression-free survival (p = 0.024). FDGSS positively correlated with total mutation rate (p = 0.016), aneuploidy (p < 0.001), and somatic copy number alteration scores (p < 0.001). CDKN2A in the cell cycle pathway (q = 0.014) and the TP53 gene in the TP53 pathway (q = 0.005) showed significant differences between high and low FDGSS patients. Conclusion: FDGSS based on the gene coexpression network was associated with the mutational landscape of HNSC. 18F-FDG PET/CT is therefore a valuable tool for the in vivo imaging of these cancers, being able to visualize the glucose metabolism of the tumor and allow inferences to be made on the underlying genetic alterations in the tumor.