The Many Uses of p53 Immunohistochemistry in Gynecological Pathology: Proceedings of the ISGyP Companion Society Session at the 2020 USCAP Annual9 Meeting
Bibliographic record
Abstract
Ancillary immunohistochemistry (IHC) has become a reliable adjunct for subclassification of gynecological neoplasms. An important recent development was optimization and validation of p53 IHC, where 3 abnormal IHC patterns (nuclear overexpression, complete absence, cytoplasmic) were shown to predict underlying TP53 mutations with high accuracy in ovarian carcinomas. p53 IHC now helps in distinguishing high-grade serous from low-grade serous carcinomas. Thereafter, the new interpretation of p53 IHC was quickly adapted for other purposes and similar accuracies were shown in endometrial carcinomas, vulvar squamous cell carcinomas, and ovarian mucinous tumors. However, it required further refinement of the p53 IHC interpretation criteria for each tumor site. A proportion of endometrial endometrioid carcinomas shows an ultramutated or hypermutated genotype due to underlying POLE mutations or mismatch repair deficiency sometimes causing subclonal TP53 mutations, and their distribution can be visualized by p53 IHC. Squamous cell carcinomas and ovarian mucinous tumors show a phenomenon called terminal differentiation where basal cells demonstrate an abnormal pattern of p53 IHC but apical cells do not despite an underlying TP53 mutation. High-grade progression of adult granulosa cell tumors due to a subclonal TP53 mutation has been recently described. Another use of p53 IHC is triaging gynecological sarcomas for molecular testing based on the assumption that TP53-mutated gynecological sarcomas do not harbor cancer driving translocations. Therefore, familiarity with interpretation of p53 IHC is becoming increasingly important for the practicing gynecological pathologist. Furthermore, local optimization of the p53 IHC assay using validated protocols including appropriate low expressing control tissues (eg, tonsil) is vital in order to achieve high diagnostic accuracy, especially for abnormal staining patterns such as complete absence or cytoplasmic, and interlaboratory concordance. p53 IHC is a reliable diagnostic adjunct for histotyping and molecular subtyping of ovarian and endometrial carcinomas, and it paves the way for large-scale studies to validate the prognostic value of p53 IHC in several gynecological tumor types. The technical advances, validated interpretation criteria, and its growing versatility in identifying high-risk neoplasms paired with its widespread availability in pathology departments make p53 IHC perhaps the single most useful IHC stain in gynecological pathology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".