Abstract C009: Distinct metabolic and genetic alterations in tumors from early-onset versus late-onset colorectal cancer
Bibliographic record
Abstract
Abstract Early-onset colorectal cancer (EO-CRC) occurring in individuals under age 50 is rapidly increasing globally, while the incidence of late-onset colorectal cancer (LO-CRC) has decreased over recent years. Previous studies have identified metabolites linked to CRC biology, however tumor-specific differences between EO-CRC and LO-CRC have not been explored. This study aimed to compare the tumor metabolome of EO-CRC and LO-CRC patients to reveal the unique biochemical state of EO-CRC. Mass spectrometry-based untargeted metabolomics was performed on tumor and patient-matched normal tissues from EO-CRC (n=53) and LO-CRC (n=314) patients to identify metabolites significantly altered in tumors (q≤0.05). Metabolite set enrichment analysis, metabolic pathway, and network analyses were performed, to identify the relationship between the altered metabolites and biological function. Analysis revealed 155 metabolites significantly altered between normal and tumor tissues. Homovanillic acid (HVA), a metabolite of dopamine, was uniquely downregulated in EO-CRC. Despite shared changes to HVA-metabolizing genes between EO- and LO-CRC the disruption in catecholamine metabolism may be specific to EO-CRC biology. Pathway and network analysis, supported by gene expression validation, showed that PD-L1 was uniquely decreased in EO-CRC suggesting immunosuppression. Additionally, phospholipid signaling was favored in EO-CRC, whereas LO-CRC tumors showed alterations to EGFR signaling and oxidative stress-related genes. In summary, this study reveals the metabolic nuances in tumor tissues from patients with EO-CRC and LO-CRC, indicating catecholamine metabolism, phospholipid signaling and immunosuppression in the biology of EO-CRC. These findings provide new insight into the metabolism of EO-CRCs that may inform new therapeutic strategies for this group of CRC patients. Citation Format: Caroline Johnson, Abhishek Jain, Allison Janak, Oladimeji Aladelokun, Rolando Garcia-Milian, Xiaomai Ma, Philip Paty, Sajid Khan. Distinct metabolic and genetic alterations in tumors from early-onset versus late-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 C009.
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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 | 0.000 |
| 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 | 0.000 | 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".