Genomic characterization of colorectal tumors: insights into significantly mutated genes, pathways, and survival outcomes
Bibliographic record
Abstract
Abstract Background Identifying significantly mutated genes in tumors aids in understanding disease etiology and survival and may aid in the discovery of new drug targets. We aimed to detect and characterize mutated genes from a large, well-characterized group of colorectal cancers. Methods In tumor and paired normal samples from 6,111 colorectal patients, we sequenced 199 genes identified from whole exome sequencing of over 1,100 tumors. Analyses focused on non-silent mutations. We classified significantly mutated genes after stratification by hypermutation status, and estimated associations of mutated genes/pathways with disease-specific (DS)-survival using Cox regression, adjusting for age, sex, mutation burden, hypermutation status, and study while accounting for multiple comparisons ( n = 4,874). Results We identified 57 genes that were significantly mutated in colorectal cancer, including 9 that were not previously reported. Among individual genes, only BRAF p.V600E mutations were significantly associated with poorer survival after correction for multiple testing (HR 1.96, P = 2.07 × 10 − 10 ), with a more pronounced association among those with non-hypermutated tumors (HR 2.24, P = 1.79 × 10 − 12 ). We also observed statistically significant associations with survival for four mutated pathways: TP53/ATM (HR 1.24, P = 7.96 × 10 − 4 ), RTK/RAS (HR 1.33, P = 3.81 × 10 − 6 ), TGF-beta (HR 1.25, P = 1.85 × 10 − 3 ), and WNT (HR 0.81, P = 2.52 × 10 − 03 ). Conclusions We identified 9 significantly mutated genes, some of which are known drug targets. Among individual genes, only the BRAF p.V600E mutation was significantly associated with DS-survival, suggesting a limited survival impact from mutations driving colorectal cancer development.
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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) | 0.001 | 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".