Auto-antibodies to p53 and the Subsequent Development of Colorectal Cancer in a U.S. Prospective Cohort Consortium
Bibliographic record
Abstract
BACKGROUND: Auto-antibodies to tumor suppressor p53 are found in a subset of patients with colorectal cancer. A recent prospective study in the United States has reported a significant 1.8-fold increased odds for colorectal cancer development with prediagnostic seropositivity to p53. In this study, we sought to examine this association in a U.S. colorectal cancer cohort consortium to evaluate the potential utility of p53 auto-antibodies as an early biomarker for colorectal cancer. METHODS: Auto-antibodies to p53 were measured in prediagnostic blood samples of 3,702 incident colorectal cancer cases and 3,702 controls, matched by age, race, and sex, from 9 U.S. prospective cohorts. The association of seropositivity to p53 with colorectal cancer risk, overall and by time between blood draw and diagnosis, was determined by conditional logistic regression. RESULTS: Overall, 5% of controls and 7% of cases were seropositive to p53, resulting in a statistically significant 33% increased colorectal cancer risk [odds ratio (OR), 1.33; 95% confidence interval (CI), 1.09-1.61]. By follow-up time, the association was only significant with colorectal cancer diagnoses within 4 years after blood draw (OR, 2.27; 95% CI, 1.62-3.19), but not thereafter (OR, 0.97; 95% CI, 0.76-1.24). CONCLUSIONS: In this large consortium of prospective cohorts, we found that prediagnostic seropositivity to tumor suppressor p53 was significantly associated with an over 2-fold increased odds of developing colorectal cancer within 4 years after blood draw. IMPACT: Our finding suggests that p53 seropositivity may not be a useful predictor of long-term colorectal cancer risk; however, it might be considered as a marker to aid in the early diagnosis of colorectal cancer.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".