Abstract B005: Comparative genomic analysis between early-onset and average-onset colorectal cancer patients at the Cleveland Clinic
Bibliographic record
Abstract
Abstract The etiology underlying the alarming rise in early-onset colorectal cancer (EOCRC; age at diagnosis <50 years) remains largely unknown. Studies of the genomic and molecular landscapes of EOCRC tumors may elucidate unique mutational features potentially linked to novel environmental exposures and provide clues about driver events in cancer development and progression. To date, conflicting evidence in the literature exists regarding whether or not EOCRCs have distinct genomic and tumor microenvironment characteristics or have more aggressive disease biology than average-onset colorectal cancers (AOCRC; age at diagnosis ≥ 50 years). Here, we compared clinical characteristics and somatic genomic features of tumors from patients with EOCRC and AOCRC whose tumors underwent standard of care next generation sequencing at the Cleveland Clinic, a tertiary cancer care center, between 2019 and 2025. Sequencing was completed using the Caris (tumor only) or Tempus (paired tumor and normal) platforms, and data were compiled into a Cleveland Clinic-specific cBioPortal instance, linked to the local tumor registry, and analyzed on a deidentified basis. Five hundred forty-eight patients with a colorectal cancer diagnosis (112 EOCRC and 436 AOCRC) had data available through this platform. Of those, the average age at diagnosis was 60.6 (SD:13.4), and 44% were female. Eighty percent were of White race (13% Black) and 7.2% were of Hispanic ethnicity. Whole exome sequencing was conducted on a combination of primary tumors (62.2%) and metastatic lesions (37.7%). As anticipated, the majority of patients had metastatic disease (73%) at diagnosis, as compared to 5.7% stage I, 5.5% stage II, and 16% stage III. The overall proportion of rectal cancer was higher in EOCRC than AOCRC (35% vs. 27%); however, this difference was not statistically significant after adjusting for sex (P=0.17). Advanced stage disease at diagnosis was similar between average and early onset (90% vs. 88%; P=0.30), likely due to insurance coverage for clinical sequencing in this patient population. After adjusting for sex, age of onset was not associated with MSI-H status (P=0.50). No significant difference in tumor mutational burden was observed between EOCRC and AOCRC (P=0.50). In univariate analyses, EOCRC tumors were significantly enriched for mutations in KRAS (OR=2.01, 95% confidence interval (CI) 1.23-3.27, FDR P=0.036) and PIK3CA (OR=2.42, CI 1.18-4.85, FDR P=0.042). APC (OR=1.63, FDR P=0.078) and TP53 (OR=1.57, FDR P=0.089) showed trends toward higher prevalence of mutations among EOCRC tumors, while BRAF, SMAD4, LRP1B, and SYNE1 did not show significant differences between age groups. This analysis suggests potential differences in the frequency of common driver mutations between EOCRC and AOCRC cases with primarily advanced stage disease. Citation Format: Stephanie L. Schmit, Daniel Sobieski, Ying Ni, Jacob Mansell, Camila Gonzalez, Sarah McClaren, Alok A. Khorana, David Liska. Comparative genomic analysis between early-onset and average-onset colorectal cancer patients at the Cleveland Clinic [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 B005.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".