Analytic, Preanalytic, and Clinical Validation of p53 IHC for Detection of <i>TP53</i> Missense Mutation in Prostate Cancer
Bibliographic record
Abstract
Abstract Purpose: TP53 missense mutations may help to identify prostate cancer with lethal potential. Here, we preanalytically, analytically, and clinically validated a robust IHC assay to detect subclonal and focal TP53 missense mutations in prostate cancer. Experimental Design: The p53 IHC assay was performed in a CLIA-accredited laboratory on the Ventana Benchmark immunostaining system. p53 protein nuclear accumulation was defined as any p53 nuclear labeling in &gt;10% of tumor cells. Fifty-four formalin-fixed paraffin embedded (FFPE) cell lines from the NCI-60 panel and 103 FFPE prostate cancer tissues (88 primary adenocarcinomas, 15 metastases) with known TP53 mutation status were studied. DU145 and VCaP xenografts were subjected to varying fixation conditions to investigate the effects of preanalytic variables. Clinical validation was performed in two partially overlapping radical prostatectomy cohorts. Results: p53 nuclear accumulation by IHC was 100% sensitive for detection of TP53 missense mutations in the NCI-60 panel (25/25 missense mutations correctly identified). Lack of p53 nuclear accumulation was 86% (25/29) specific for absence of TP53 missense mutation. In FFPE prostate tumors, the positive predictive value of p53 nuclear accumulation for underlying missense mutation was 84% (38/45), whereas the negative predictive value was 97% (56/58). In a cohort of men who experienced biochemical recurrence after RP, the multivariable HR for metastasis among cases with p53 nuclear accumulation compared with those without was 2.55 (95% confidence interval, 1.1–5.91). Conclusions: IHC is widely available method to assess for the presence of deleterious and heterogeneous TP53 missense mutations in clinical prostate cancer specimens. Clin Cancer Res; 23(16); 4693–703. ©2017 AACR.
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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 | 0.007 | 0.004 |
| 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 | 0.001 |
| 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".