Abstract PR004: Mutational signature profiling identifies a distinct subgroup of early-onset colorectal cancer associated with younger age at diagnosis, recent birth year and specific genomic features
Bibliographic record
Abstract
Abstract Introduction: The cause/s of the increasing incidence of early-onset colorectal cancer (EOCRC) are unknown. Tumor mutational signatures provide a powerful genomic tool for discovering mutational processes associated with known or unknown etiologies. The aim was to identify subgroups of EOCRCs based on their tumor mutational signature profiles, then validate and genomically characterize these novel subgroups of EOCRC. Methods: Whole exome sequencing (WES) was performed on tumor and matched blood-derived DNA from 275 non-hereditary, mismatch repair proficient (MMRp) EOCRCs from the ANGELS and CCFR studies (age at diagnosis groups: 18-35yrs n=102; 36-45yrs n=128; 46-55yrs n=45). Single base substitution (SBS), insertion/deletion (ID) and doublet (DBS) tumor mutational signatures were calculated using COSMIC v3.4. An independent dataset comprising 1,716 whole-genome sequenced MMRp CRCs from the Genomics England (GEL)(including 240 EOCRCs) served as validation. Unsupervised dimensionality reduction in conjunction with hierarchical clustering was applied to identify signature-based clusters associated with common mutational processes, without reference to clinical features. Results: Hierarchical clustering identified nine subgroups in 275 EOCRCs and five in 1,716 CRCs from GEL (when compared with the largest subgroup from each study). A subgroup defined by dominant SBS89 and DBS8 signatures was present in both EOCRCs and GEL CRCs. In the ANGELS/CCFR EOCRCs, this subgroup comprised 14% and was associated with a younger age at diagnosis (aged 18-35 (24%) vs 36-45 (12%) vs 46-55 (0%) (p=0.006)), with those born in recent decades, (<1960 (0%) vs1960-1979 (9%) vs ≥1980 (24%) (p=0.001)), with a proximal location (proximal (24%), distal (12%), rectal (11%) (p=0.02)), and with the co-occurrence of multiple polyps at diagnosis (p=1x10-9). In the GEL CRCs, the SBS89/DBS8 subgroup was similarly significantly associated with younger age of diagnosis (≤45, p=4x10-10) and patients born more recently (≥1980, p=5x10-7). The SBS89/DBS8 EOCRC subgroup was associated with BRAF p.V600E mutation (55% vs 9%, p=1x10-8)) and high doublet somatic mutation count (mean=4.5 ± 3.0 vs 0.9 ±1.5; p=1x10-13) when compared with EOCRCs without SBS89/DBS8. These results were also observed in the GEL CRCs. Genomic characterization of the SBS89/DBS8 positive CRCs in GEL demonstrated significant differences in large-scale variants, including increased loss of heterozygosity events (17.7 ± 17.3 vs 10.8 ± 13.8; p=0.004). Conclusions: Tumor mutational signature profiling identified a distinct subgroup of CRCs associated with young age at diagnosis, more recent birth year and unique genomic features, that is characterized by high proportions of SBS89 and DBS8 mutational patterns, both of which currently have an unknown etiology. This distinct subgroup may contribute to the recent rising incidence in EOCRC and warrants further investigation to elucidate its underlying mechanisms. Citation Format: Peter Georgeson, Alysha Prisc, Jihoon E. Joo, Khalid Mahmood, Romy Walker, Mark Clendenning, Julia Como, Natalie Diepenhorst, Julie McDonald, Steven Gallinger, Robert Grant, Dylan E. O'Sullivan, Darren R. Brenner, Finlay A. Macrae, Christophe Rosty, Ingrid M. Winship, Mark A. Jenkins, Daniel D. Buchanan. Mutational signature profiling identifies a distinct subgroup of early-onset colorectal cancer associated with younger age at diagnosis, recent birth year and specific genomic features [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 PR004.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".