Abstract C021: Characterizing the clinicopathological, genomic and spatial transcriptomic features of colibactin-induced colorectal cancers
Bibliographic record
Abstract
Abstract Introduction: Colorectal cancers (CRCs)with related colibactin-induced DNA damage from pks+E.coli bacteria are identified by the presence of the SBS88 tumor mutational signature and mutations in unique 5bp genomic contexts (extended contexts). CRCs with high SBS88 and high extended contexts (colibactin-positive) remain poorly characterized in terms of identifying distinct clinicopathological, genomic, and transcriptomic features. Methods: We applied a mutational signature-based classifier derived from organoid sequencing data to identify colibactin-positive CRCs in 1,716 non-hereditary, microsatellite stable (MSS) whole-genome sequenced primary tumors from Genomics England (GEL). Clinicopathological, genomic and survival associations were tested in an independent cohort of 4,254 primary MSS CRCs from the GECCO-CCFR consortium. Spatial transcriptomic profiling of the tumor microenvironment of colibactin-positive EOCRCs was performed using Nanostring CosMx technology. Results: Colibactin-positive CRCs were identified in 10.2% (175/1,716) of the GEL CRCs and were associated with an early age at diagnosis (<40 years: 4.0% of positives v 2.0% of negatives; p=0.1), enrichment in patients born on or after 1980 (6.9% v 1.4%; p=0.02), and location in distal colon and rectum compared with proximal colon (45%/47% v 41%/28% distal/rectum; p=5x10-11) when compared with the colibactin-negative CRCs (n=1,400, 81%). Genomic analysis revealed recurrent mutations in established CRC driver genes, the strongest of which were APC:c.835-8A>G (p=1x10-12), SMAD4:c.788-8A>G (p=0.0001) and BRAF:c.1781A>G (p=0.001), with mutations exhibiting site-specificity. Colibactin-positive CRCs exhibited distinctive large-scale genomic alterations, including recurrent loss of heterozygosity spanning TGIF1, SMAD2, DCC, and SMAD4 gene region. Mutation timing analysis revealed that SBS88-associated mutations, extended context mutations and APC:c.835-8A>G are early, clonal events (p=3x10-35, 7x10-81, 3x10-9 respectively). In the GECCO-CCFR CRCs, 5.1% of 4,254 MSS CRCs were identified as colibactin-positive (216 of 4,254) with similar significant genomic hallmarks: earlier age at diagnosis (<50 years p=0.03), distal and rectum site (p=4x10-9), and recurrent mutations in APC, SMAD4 and BRAF (p=4x10-128, 2x10-9, 2x10-8). Spatial transcriptomic profiling of six EOCRCs (three colibactin-positive) obtained 1.5 million cells across 19 cell types. Our preliminary analyses identified distinct stromal and immunological profiles associated with colibactin-positive EOCRCs. Conclusions: A combined SBS88 and extended context mutational signature-based classifier for colibactin-induced DNA damage, identified a distinct subtype of CRC characterized by earlier age at diagnosis, distal and rectal site predominance, specific somatic mutations, and unique genomic instability patterns. These findings provide new insights into colibactin-mediated carcinogenesis and creates opportunities for developing both targeted treatment and prevention strategies for colibactin-associated CRC. Citation Format: Peter Georgeson, Jihoon E. Joo, Alysha Prisc, Lochlan Fennell, Liam Skinner, Bernard J. Pope, Khalid Mahmood, Romy Walker, Mark Clendenning, Julia Como, Natalie Diepenhorst, Julie McDonald, Finlay A. Macrae, Christophe Rosty, Ingrid M. Winship, Amanda Phipps, Mark A. Jenkins, Ulrike Peters, Daniel D. Buchanan. Characterizing the clinicopathological, genomic and spatial transcriptomic features of colibactin-induced colorectal cancers [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 C021.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".