Abstract PO-038: Metabolic-inflammatory investigation of head and neck cancer patient reported neuropsychological factors via multiomics integration of the plasma metabolome, lipidome, and circulating inflammation cytokines
Bibliographic record
Abstract
Abstract Background: Neuropsychological factors (NPF) (i.e., fatigue, pain, cognitive dysfunction, depression, stress, and sleep disturbance) are common in head and neck cancer (HNC) and often cluster together. They can impair a patient’s functional status and quality of life and are an obstacle to effective treatment and a full recovery. Metabolism and inflammation may be underlying mechanisms but may work differently across HPV-related and unrelated HNC. We explored the joint interaction of metabolism, inflammation, and NPF via a stratified multiomics network analysis of the pretreatment plasma metabolome, lipidome, and inflammation cytokines across HPV-related and unrelated HNC. Methods: HNC patients completed validated symptom questionnaires (e.g., Multidimensional Fatigue Inventory, Patient Health Questionnaire-8, Perceived Stress Scale, Pittsburg Sleep Quality Index) and donated blood for untargeted (metabolome) and targeted (lipids and cytokines) assays prior to chemoradiotherapy. Metabolites and lipids were measured by liquid-chromatography high resolution mass spectrometry and the cytokines were measured via multiplex assays. A multiomics network analysis algorithm (xMWAS) plotted network graphs for HPV-related and unrelated HNC separately, by estimating pair-wise partial least squares correlations between the metabolites, lipids, cytokines, and NPF variables. To interpret each network, a multilevel community detection algorithm identified highly correlated clusters of variables, representing possible biological relatedness. Metabolic pathway analysis provided functional interpretation of the metabolite-lipid-cytokine-NPF clusters. Results: There were 82 subjects (59 years mean age, 72% male, 81% white, 48% HPV-related) in which, 186 metabolites, 78 lipids, 7 cytokines (C-reactive protein, Tumor necrosis factor-α, interleukin-1β, IL6, IL10, IL1 receptor antagonist, and TNF receptor-1) and 6 NPF were analyzed. We required a minimum correlation of 0.3 and P-value <0.05 to be included in the network. xMWAS modeled 525 correlations in HPV-related HNC compared to 419 correlations in HPV-unrelated. Three clusters were identified for HPV-related HNC: all 6 NPF were contained in a single cluster with 51 metabolites, 10 lipids, but 0 cytokines. Pathway analysis found enriched levels of aminoacyl-tRNA biosynthesis (P<.001), valine, leucine, and isoleucine biosynthesis (P<.001), and glycine, serine and threonine metabolism (P=0.003). Comparatively, five clusters were identified for HPV-unrelated HNC: the 6 NPF were dispersed between two closely linked clusters along with 23 metabolites, 4 lipids, and 4 cytokines (CRP, IL1β, IL10, IL1ra). Enriched pathways included aminoacyl-tRNA biosynthesis (P<.0001), glycine, serine, and threonine metabolism (P<.0001), cysteine and methionine metabolism (P<.0001). Conclusions: In both HPV-related and unrelated HNC, NPF were closely linked via metabolites enriched in amino acid metabolic pathways, suggesting that NPF may have an amino acid metabolic foundation. Cytokines may play a larger role in HPV unrelated HNC. Citation Format: Ronald C. Eldridge, Yufen Lin, Nabil F. Saba, Andrew Miller, Evanthia C. Wommack, Jennifer Felger, Deborah W. Bruner, Canhua Xiao. Metabolic-inflammatory investigation of head and neck cancer patient reported neuropsychological factors via multiomics integration of the plasma metabolome, lipidome, and circulating inflammation cytokines [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-038.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".