Abstract B038: Differences in metabolomic profiles predictive of fatigue in early-onset vs. later-onset colorectal cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».