Abnormal p53 and p16 staining patterns distinguish uterine leiomyosarcoma from inflammatory myofibroblastic tumour
Bibliographic record
Abstract
AIMS: Uterine myxoid leiomyosarcoma may show relatively bland histological appearances, despite its aggressive behaviour. Distinguishing uterine leiomyosarcoma from the more indolent inflammatory myofibroblastic tumour (IMT), which is amenable to targeted therapies, can be challenging. A significant subset of leiomyosarcomas harbour TP53 and/or CDKN2A genomic alterations. Here, we examined the diagnostic value of p53 and p16 immunohistochemistry in the distinction of uterine conventional and myxoid leiomyosarcoma from IMT, in correlation with targeted sequencing of TP53 and CDKN2A. METHODS AND RESULTS: We performed p53 and p16 immunohistochemistry in 49 tumours, including 23 uterine leiomyosarcomas (12 myxoid, 11 conventional) and 26 IMT (12 uterine, 14 extrauterine). TP53 and CDKN2A coding regions were sequenced in 20 cases (four myxoid, 11 conventional uterine leiomyosarcomas; four uterine, one extrauterine IMT). Abnormal p53 staining patterns (strong/diffuse or null) were observed in six of 12 (50%) myxoid and six of 11 (55%) conventional leiomyosarcomas but none of the IMT (P < 0.0001), correlating with TP53 mutation/deletion (P = 0.0001). P16 loss was detected in five of 10 (50%) myxoid and two of 11 (18%) conventional leiomyosarcomas, but none of the IMT (P = 0.0005), correlating with CDKN2A deletion (P = 0.014). Strong/diffuse p16 staining in six of 21 (29%) leiomyosarcomas and three of 26 (12%) IMT did not correlate with CDKN2A alterations. CONCLUSIONS: Abnormal p53 staining and p16 loss are observed frequently in uterine leiomyosarcomas, with 100% specificity and 70% sensitivity against IMT, and correlating with genomic alterations. Conversely, IMT shows normal p53 and p16 staining, highlighting the use of these markers in the differential diagnosis of uterine mesenchymal neoplasms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".