Validating a prognostic plasma metabolomic biomarker to improve precision medicine in head and neck cancer patients - Over the past decade, precision medicine research in head and neck cancer (HNC) has been singularly focused
on a single biomarker, human papillomavirus (HPV), to target low-risk patients for treatment de-intensification
(i.e., reduce therapy to improve quality of life without sacrificing survival). De-intensification is vital since standard
treatment involves toxic doses of chemoradiation that causes adverse events and debilitating long-term side
effects in most patients. In 2019, however, two phase-III clinical trials confirmed that HPV was insufficient to
target the most promising de-intensified therapy. This failure presents a timely opportunity to test new prognostic
markers to target de-intensified therapy in the 41 existing clinical trials, but a lack of promising biomarkers hinders
progress. HNC is a disease marked by extensive metabolic heterogeneity that is emerging as a rich source of
novel prognostic biomarkers but has yet to be fully investigated. For instance, many of the somatic mutations in
HNC that are associated with prognosis (e.g., TP53 and PIK3CA) are upstream regulators of key metabolic
processes in glycolysis, lipids, amino acids, and cell signaling. Recent studies have showed that the Warburg
effect, the atypical metabolic state in which a tumor upregulates glycolysis but bypasses mitochondrial oxidative
phosphorylation (OXPHOS), is a key discriminating characteristic between HNC cell lines linked to prognosis.
Most recently, five different studies in 2020 found that gene expression probes targeting metabolic regulating
genes created signatures that can predict HNC patient survival. The evidence leads to one conclusion—
metabolism is intimately involved in the development, progression, and survival of HNC. Thus, metabolomics
may be the best approach to discover the translational biomarkers vital to the future of HNC precision medicine.
However, the data required to investigate and validate an HNC metabolomic biomarker, particularly in a clinical
setting, is severely lacking. My program of research aims to fill that gap. In my KL2 research, which includes 209
HNC patients with blood plasma metabolomics, I used machine learning algorithms to identify a pretreatment
metabolomic endotype comprised of glycolysis, OXPHOS, lipid biosynthesis, and amino acid metabolites. The
endotypes categorized patients into one of two distinct groups that were associated with overall survival (hazard
ratio = 2.39, 95% CI: 1.19-4.78). Most importantly, the prognostic ability of the endotype was independent of
relevant clinical factors such as HPV, smoking, age, sex, race, tumor site and stage, suggesting that it may
succeed as an HNC biomarker where others have failed. The goal of this R03 is to validate my KL2 findings with
an independent cohort (Aim 1) and investigate how the metabolomic endotype in blood is related to metabolism
and metabolic characteristics in the tumor (Aim 2). Positive findings from this study will help me translate this
biomarker to ongoing clinical trials data within NRG Oncology, a multisite cancer clinical trials consortium, to test
whether the pretreatment metabolomic blood biomarker can identify HNC patients who will respond to de-
intensified therapy and spare them the toxic effects of standard chemoradiotherapy.
