Unsupervised Hierarchical Clustering of Head and Neck Cancer Patients by Pre-Treatment Plasma Metabolomics Creates Prognostic Metabolic Subtypes

• Type of Material: Article
• Authors: Ronald Eldridge, Emory University; Zhaohui Qin, Emory University; Nabil Saba, Emory University; Madelyn Houser, Emory University; Neil D Hayes, University of Tennessee Health Science Center; Andrew Miller, Emory University; Deborah Bruner, Emory University; Dean Jones, Emory University; Canhua Xiao, Emory University
• Language: English
• Date: 2023-06-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2023 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/cancers15123184
• Title of Journal or Parent Work: CANCERS
• Volume: 15
• Issue: 12
• Grant/Funding Information: This work was supported by the Georgia Clinical and Translational Science Alliance (UL1TR002378 and KL2TR002381), and the National Institute of Nursing Research (K99/R00NR014587, R01NR015783, and P30CA138292).
• Keywords: Oncology; SQUAMOUS-CELL CARCINOMA; metabolomics; fatty acids; clustering; head and neck cancer; HIGH-RESOLUTION METABOLOMICS; galactose; CHEMOTHERAPY; Life Sciences & Biomedicine; OUTCOMES; PREVALENCE; smoking; survival; human papillomavirus; EXPRESSION; biomarkers; GROWTH-FACTOR RECEPTOR; Science & Technology; INFLAMMATION; arginine and proline; RADIOTHERAPY; SURVIVAL
• Research Categories: Health Sciences, Nursing

Abstract

There is growing evidence that the metabolism is deeply intertwined with head and neck squamous cell carcinoma (HNSCC) progression and survival but little is known about circulating metabolite patterns and their clinical potential. We performed unsupervised hierarchical clustering of 209 HNSCC patients via pre-treatment plasma metabolomics to identify metabolic subtypes. We annotated the subtypes via pathway enrichment analysis and investigated their association with overall and progression-free survival. We stratified the survival analyses by smoking history. High-resolution metabolomics extracted 186 laboratory-confirmed metabolites. The optimal model created two patient clusters, of subtypes A and B, corresponding to 41% and 59% of the study population, respectively. Fatty acid biosynthesis, acetyl-CoA transport, arginine and proline, as well as the galactose metabolism pathways differentiated the subtypes. Relative to subtype B, subtype A patients experienced significantly worse overall and progression-free survival but only among ever-smokers. The estimated three-year overall survival was 61% for subtype A and 86% for subtype B; log-rank p = 0.001. The association with survival was independent of HPV status and other HNSCC risk factors (adjusted hazard ratio = 3.58, 95% CI: 1.46, 8.78). Our findings suggest that a non-invasive metabolomic biomarker would add crucial information to clinical risk stratification and raise translational research questions about testing such a biomarker in clinical trials.