Utility of p53 Immunohistochemical Staining for Risk Stratification of Mantle Cell Lymphoma
Bibliographic record
Abstract
Background: Inactivating TP53 mutations in mantle cell lymphoma (MCL) are associated with poor prognosis. While next-generation sequencing (NGS) is the gold standard for assessing TP53 , p53 immunohistochemistry (IHC) is an orthogonal means of evaluating TP53 status that has not been well characterized in MCL. In this single tertiary care center laboratory study, we aimed to evaluate the concordance of p53 IHC with the TP53 status in cases of MCL in hopes of evaluating if the former could act as an accurate, timely and cost-effective way of risk stratifying these patients. Methods: A total of 47 cases of MCL that had TP53 NGS performed were included in this study. The main objective was to correlate NGS findings with p53 IHC results. Secondary objectives included assessment of possible associations between TP53 status and other variables (demographics, unique histopathological and IHC features). The turn-around time and cost for NGS and p53 IHC were also compared. Results: Thirteen out of 47 (28%) cases were TP53 -mutated by NGS. p53 IHC showed good concordance with NGS, with moderate to high sensitivity (11/13, 85%) and excellent specificity (34/34, 100%). Secondary objectives revealed increased SOX11-negative status in TP53 -mutated cases (3/13, 23% vs. 1/29, 3%, P = 0.045). The cost and turn-around time of NGS were approximately of 30- and sixfold those of p53 IHC, respectively. Conclusion: p53 IHC shows good concordance with NGS in MCL, with high specificity and moderate sensitivity for identifying inactivating TP53 mutations. Based on our findings, p53 IHC may be an efficient and cost-effective tool in risk stratification of MCL. J Hematol. 2024;13(5):200-206 doi: https://doi.org/10.14740/jh1333
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".