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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".