Abstract B038: Differences in metabolomic profiles predictive of fatigue in early-onset vs. later-onset colorectal cancer
Bibliographic record
Abstract
Abstract Background: Cancer-related fatigue (CRF) affects up to 90% of patients with cancer during chemotherapy and persists in approximately 30% of patients after treatment completion, with some patients describing CRF as “devastating,” “never-ending,” and “totally consuming.” For patients with early-onset cancer (diagnosed<50 years) in particular, CRF is associated with worse quality of life and lower likelihood of returning to normal daily activities, including work. Alterations in certain metabolic pathways have been hypothesized to influence the development of CRF. The purpose of the present study is to identify differences in metabolic longitudinal predictors of CRF in a prospective cohort of patients with colorectal cancer (CRC) using serum metabolomics data. Methods: The ColoCare Study includes six U.S. sites and one Germany site and consists of men and women ages 18 to 89 at diagnosis with newly diagnosed primary CRC of stage I-IV. Patients are consented during a pre-surgery visit to complete questionnaires, provide biologic specimens at multiple time points, and for medical record reviews. Patients were categorized as early (age<50) or later-onset (age≥50). CRF was measured using the three-item fatigue subscale of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30 (EORTC QLQ-C30) before CRC surgery (baseline), and 6, 12, and 24 months post-surgery. CRF scores were categorized as no, low, moderate, and high CRF using previously validated cutpoints. Using blood specimens at each time point, we performed semi-targeted metabolomics to identify aqueous metabolites following comprehensive protocols for measurement and quality control. Using multivariable ordinal logistic regression, we assessed the association between 1) individual metabolites and CRF cross-sectionally at each timepoint, and 2) metabolites measured at baseline and 6 months post-surgery and CRF at 12 months post-surgery. We adjusted for multiple testing using the number of effective independent tests. Further analyses using machine learning methods and metabolic pathway analysis are ongoing. Results: ColoCare Study participants with at least one measurement of CRF and metabolomic profiling (N=1,098) were included in the present study. Early-onset and later-onset patients had similar CRF prevalence. N=173 distinct polar metabolites were detected. Five metabolites (e.g. tryptophan metabolites, xenobiotic metabolites) were inversely associated and a nicotine metabolite (C16H20N2O8) was positively associated with CRF at baseline only among later-onset patients. A phenylalkylamine xenobiotic metabolite (C18H31NO) measured at 6 months post-surgery was inversely associated with CRF at 12 months post-surgery only among early-onset patients. Conclusions: CRF is prevalent in both early-onset and late-onset CRC patients. Metabolites associated with CRF in patients with CRC differed by age at onset. Further research profiling metabolic pathways associated with CRF by age can aid in identifying targetable mechanisms of CRF in early-onset CRC patients. Citation Format: Nicole C. Loroña, Mary Playdon, James Cox, Xiaoyin Li, Aasha I. Hoogland, Patricia A. Erickson, Maria F. Gomez, Sheetal Hardikar, Mmadili N. Ilozumba, Jennifer Ose, Anita Peoples, Brent Small, Victoria Damerell, Vaia Florou, Mark Lewis, Shannon M. Christy, William Grady, Biljana Gigic, David Shibata, Doratha A. Byrd, Adetunji Toriola, Christopher I. Li, Cornelia Ulrich, Heather S L. Jim, Jane C. Figueiredo. Differences in metabolomic profiles predictive of fatigue in early-onset vs. later-onset colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B038.
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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 | 0.000 | 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) | 0.003 | 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".