Abstract C007: Age-stratified insight into genomic alterations in colon adenocarcinoma via AACR GENIE v18.0
Bibliographic record
Abstract
Abstract Background: The incidence of colon cancer is rising more rapidly in patients under 50 years of age (early-onset colon cancer; EOCC) than in any other age group. This trend is likely a consequence of multifactorial causes, including environmental exposures, shifts in lifestyle factors, and expanded use of screening tests which are now recommended for individuals starting at age 45. However, fully defining the genomic landscape and understanding the biological mechanisms driving colorectal cancer in younger patients remains an ongoing effort. Methods: Genomic sequencing data from primary tumors diagnosed as colon adenocarcinoma were obtained from AACR Project GENIE Cohort v18.0, comprising 7,633 total patients, and accessed through the Synapse and cBioPortal platforms. Patients were grouped into EOCC (<50 years) and traditional-onset colon cancer (≥50 years). To better delineate aging-associated differences, we performed additional analyses using more restrictive age cutoffs of ≤45 years (n=1,267 patients) and ≥55 years (n=4,676 patients) for the respective groups. The clinical variable “Age at Which Tumor Sequencing Was Reported (Years)” was used as a proxy for “age at diagnosis.” Genes enrichments with fewer than 10 patients were excluded from this analysis. Results: The total cohort included 49.8% male (n=3801), 46.7% female (n=3563), and 3.5% patients of unknown sex/gender (n=269). Evaluation of the rate of patient-level genomic alterations between age groups revealed enrichment of mutations in APC (71.53% vs 63.95%, q-value 1.895e-4), TP53 (70.74% vs 64.68%, q-value 6.248e-3), TCF7L2 (16.48% vs 12.46%, q-value 0.0314), CTNNB1 (9.6% vs 6.15%, q-value 4.746e-3), and MSH2 (6.29% vs 3.81%, q-value 0.0211) in patients with EOCC. In comparison, mutations in BRAF (19.58% vs 8.2%, q-value 1.20e-21), RNF43 (17.06% vs 9.07%, q-value 3.25e-8), ZNRF3 (8.75% vs 3.55%, q-value 4.529e-3), SMARCA4 (9.2% vs 5.55%, q-value 7.461e-3), HLA-B (6.53% vs 3.03%, q-value 0.0269), AMER1 (10.18% vs 6.37%, q-value 0.0321), GNAS (4.08% vs 2.25%, q-value 0.0366), and HNF1A (4.62% vs 2.41%, q-value 0.0211) were enriched in traditional-onset colon cancer. Assessment of individual point mutations revealed enrichment of BRAF V600E (15.81% vs 4.42%, q-value 4.10e-29), KRAS Q61K (0.94% vs 0.08%, q-value 0.0446), and RNF43 G659Vfs*41 (10.16% vs 3.93%, q-value 1.12e-8) in traditional-onset colon cancer. Lastly, assessment of CNA amplifications revealed enrichment of NCOA3 (5.65% vs 2.28%, q-value 0.0276) in EOCC. Conclusion: We have identified novel associations between genomic alterations and patient age in colon cancer using AACR GENIE v.18, the largest public cancer genomic dataset available to date. Our findings reveal alterations not previously characterized in this age group, suggesting that EOCC may represent a biologically distinct disease entity rather than an earlier manifestation of traditional colon cancer. Further investigation into divergent molecular phenotypes in colon cancer by age at diagnosis is warranted. Citation Format: Johnathan D. DeBetta, Ashani T. Weeraratna, Elizabeth M. Jaffee, Daniel J. Zabransky. Age-stratified insight into genomic alterations in colon adenocarcinoma via AACR GENIE v18.0 [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 C007.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.014 | 0.003 |
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".