TP53 Mutational Analysis Enhances the Prognostic Accuracy of IHC4 and PAM50 Assays
Bibliographic record
Abstract
IHC4 and PAM50 assays have been shown to provide additional prognostic information for patients with early breast cancer. We evaluated whether incorporating TP53 mutation analysis can further enhance their prognostic accuracy. We examined TP53 mutation and the IHC4 score in tumors of 605 patients diagnosed with stage I-III breast cancer at National Taiwan University Hospital (the NTUH cohort). We obtained information regarding TP53 mutation and PAM50 subtypes in 699 tumors from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. We found that TP53 mutation was significantly associated with high-risk IHC4 group and with luminal B, HER2-enriched, and basal-like subtypes. Despite the strong associations, TP53 mutation independently predicted shorter relapse-free survival (hazard ratio [HR] = 1.63, P = 0.007) in the NTUH cohort and shorter breast cancer-specific survival (HR = 2.35, P = <0.001) in the METABRIC cohort. TP53 mutational analysis added significant prognostic information in addition to the IHC4 score (∆ LR-χ(2) = 8.61, P = 0.002) in the NTUH cohort and the PAM50 subtypes (∆ LR-χ(2) = 18.9, P = <0.001) in the METABRIC cohort. We conclude that incorporating TP53 mutation analysis can enhance the prognostic accuracy of the IHC4 and PAM50 assays.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".