Abstract C001: Novel Sex-Specific Metabolic Phenotypes in Early Onset Colorectal Cancer
Bibliographic record
Abstract
Abstract Introduction: The overall incidence of late-onset colorectal cancer (LOCRC, ≥50 years old) has decreased. However, the rates of early-onset colorectal cancer (EOCRC, <50 years old) have steadily increased, posing a major public health concern as EOCRC patients typically have poorer clinical outcomes. Sex-specific factors, including hormones and metabolites, have been underexplored as potential therapeutic targets for EOCRC. Aim: We propose that male and female EOCRC patients exhibit different metabolic responses in their colorectal tumors, which could have significant implications for personalized treatment approaches. Methods: We conducted a comprehensive metabolomics analysis on surgically resected colorectal tumors and matched adjacent normal mucosa from EOCRC and LOCRC patients (n=372). Disease-specific survival analysis was performed for individual patients. To determine the influence of sex and metabolite abundance on tumor progression, we employed multivariate Cox proportional hazard and early-late index models. We further validated the clinical significance of our findings using independent datasets from the NCBI Gene Expression Omnibus (n=582 patients), and a retrospective validation cohort from the SEER database (n=79506 EOCRC patients). Results: Our discovery and validation cohorts revealed that male patients had significantly worse disease-specific survival in EOCRC. Metabolomic analysis revealed distinct metabolic sub-phenotypes influenced by sex. Younger male patients exhibited worse disease-specific survival compared to LOCRC, even after adjusting for the stage of CRC. Tumors from younger male patients showed enhanced amino acid utilization, characterized by increased asparagine and tryptophan metabolism, and increased fatty acid uptake to fuel growth. Independent validation revealed that high asparagine synthetase (ASNS) expression correlated with age in male EOCRC patients only. Conclusion: Modulating sex-biased tumor metabolomes may represent a potentially effective targeted strategy for the prevention and treatment of EOCRC in both men and women. Citation Format: Oladimeji Aladelokun, Abhishek Jain, Xinyi Shen, Samuel Butensky, Shiying Xiao, Allison Janak, Domenica Berardi, Carol Yang, Reza Aalizadeh, Sunny Siddique, Xiaomei Ma, Philip Paty, Rolando Garcia-Milian, Alison Berner, Jatin Roper, Sajid Khan, Caroline Johnson. Novel Sex-Specific Metabolic Phenotypes in Early 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 C001.
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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) | 0.003 | 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".