Abstract C024: Somatic mutations in early-onset colorectal cancer: insights from a U.S. Hispanic cohort
Bibliographic record
Abstract
Abstract Background: The alarming rising incidence of colorectal cancer (CRC) among individuals younger than 50 years (early-onset CRC) underscores the need to identify the genetic drivers to improve personalized clinical management and outcomes. The objective of this study was to characterize the somatic mutation profile of early-onset tumors from Hispanics living in Puerto Rico (HPR), a U.S. Hispanic subpopulation with a high CRC burden. Methods: Whole exome sequencing was performed using the HiSeq4000 System (Illumina) on colorectal adenocarcinoma and corresponding mucosa samples from 62 individuals with non-familial, sporadic early-onset CRC and 25 diagnosed with average-onset CRC ( >60 years old). Somatic variant calling and annotation/visualization were performed with Strelka and Ingenuity Variant Analysis software, respectively. Mutational frequency in APC, TP53, KRAS, and SMAD4 was compared to data in TCGA and AACR Project GENIE. Results: The mutational burden in early-onset CRC tumors from HPR was comparable to average-onset tumors. However, mutations in APC (p = 0.015), PIK3CA (p = 0.03), TP53BP1 (p = 0.03), and MUC16 (p = 0.02) were more common in average-onset tumors. The aflatoxin exposure signature (SBS24) was only identified in early-onset tumors, with 22.4% showing a signature fraction >10%. When compared to data on tumors in the TCGA and GENIE datasets, early-onset CRC tumors from HPR showed a distinct somatic mutational frequency in key driver genes. Conclusions: Early-onset CRC tumors display distinct somatic mutational profile compared to average-onset tumors. Additional studies with larger, diverse samples are crucial for understanding the population-specific underlying mechanisms leading to early-onset disease. Citation Format: Maria Gonzalez-Pons, Julie Dutil, Ingrid Montes-Rodríguez Montes-Rodríguez, Luis D. Borrero-Garcia, Lenis Rovira-Torres, Leslie Casiano, Anna M. Napoles, Jung S. Byun, Eliseo Perez-Stable, Kevin L. Gardner, Marcia Cruz-Correa. Somatic mutations in early-onset colorectal cancer: insights from a U.S. Hispanic cohort [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 C024.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".