Abstract LB134: Mutational signatures in colorectal cancer from 11 countries reveal new insights in early-onset colorectal cancer
Bibliographic record
Abstract
Abstract Background: Colorectal cancers (CRC) have an important impact on both incidence and mortality worldwide, with varying rates in different parts of the world. Despite an overall decrease in incidence in the past years, an alarming increase in early onset colorectal cancer (CRC in patients under 50 years of age) rates has been observed. As part of the Cancer Research UK Grand Challenge Mutographs project, we aim to better understand the underlying mutagenic causes contributing to the differences in CRC incidence rates through mutational signatures. Methods: We collected epidemiological data and performed whole genome sequencing and mutational signature analysis on 981 CRC tumor samples from 11 countries with varying incidence rates, including intermediate incidence regions (Iran, Colombia, Thailand, and Brazil) and higher incidence regions (Argentina, Russia, Canada, Poland, Czechia, Serbia, and Japan). Results: The average mutational profiles were similar across the countries. Mutational signatures associated with DNA repair deficiencies, including POLE and POLD1 associated signatures (SBS10a/b/c/d and SBS28), MUTYH (SBS36), NTHL1 (SBS30), homologous recombination deficiency (SBS3), and a plethora of microsatellite instability (MSI) signatures, were found in 177 cancers (18% of all samples) and at comparable levels across all countries. Multiple signatures with known etiologies were found in 802 DNA repair proficient colorectal cancers including clock-like (SBS1 and SBS5), reactive oxygen species (SBS18), APOBEC (SBS2 and SBS13), and the microbiome-product colibactin (SBS88) signatures. The colibactin signature SBS88 was observed in 14% of samples overall, being more prevalent in distal and rectum tumors. SBS88 was significantly enriched in younger patients (33% <40y, 23% 40-49y, 20% 50-59y,10% 60-69y, 10% ≥70y; p-value = 0.0001). Tumors from early onset patients also showed higher prevalence of another mutational signature with unknown etiology (19% <40y, 16% 40-49y, 13% 50-59y, 6% 60-69y, 7% ≥70y, p-value = 0.005). Conclusions: These results indicate a potential association between SBS88 with early onset CRC, suggesting that the recent increase in incidence may be at least partially explained by the genotoxic compound colibactin, and associated bacteria. Citation Format: Wellington dos Santos, Marcos Diaz-Gay, Sarah Moody, Sergey Senkin, Behnoush Abedi-Ardekani, Mariya Kazachkova, Stephen Fitzgerald, Saamin Cheema, Valerie Gaborieau, Jingwei Wang, Christine Carreira, Thomas Cattiaux, Priscilia Chopard, Calli Latimer, David Zaridze, Riley Cox, Reza Malekzadeh, Miodrag Ognjanovic, Suleeporn Sangrajrang, Maria P. Curado, Rui M. Reis, Ivana Holcatova, Carlos Vaccaro, Beata Swiatkowska, Jolanta Lissowska, Carolina Wiesner, Tatsuhiro Shibata, Surasak Sangkhathat, Patricia Ashton-Prolla, Laura Humphreys, Sandra Perdomo, Ana C. de Caravalho, Mike Stratton, Paul Brennan, Ludmil Alexandrov. Mutational signatures in colorectal cancer from 11 countries reveal new insights in early-onset colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB134.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".