Relationships between highly recurrent tumor suppressor alterations in 489 leiomyosarcomas
Bibliographic record
Abstract
BACKGROUND: Leiomyosarcoma (LMS) is the most common soft tissue and uterine sarcoma, but no standard therapy is available for recurrent or metastatic LMS. TP53, p16/RB1, and PI3K/mTOR pathway dysregulations are recurrent events, and some LMS express estrogen receptor (ER) and/or progesterone receptor (PR). To characterize relationships between these pathway perturbations, the authors evaluated protein expression in soft tissue and uterine nonprimary leiomyosarcoma (np-LMS), including local recurrences and distant metastases. METHODS: TP53, RB1, p16, and PTEN expression aberrations were determined by immunohistochemistry (IHC) in tissue microarrays (TMAs) from 227 np-LMS and a comparison group of 262 primary leiomyosarcomas (p-LMS). Thirty-five of the np-LMS had a matched p-LMS specimen in the TMAs. Correlative studies included differentiation scoring, ER and PR IHC, and CDKN2A/p16 fluorescence in situ hybridization. RESULTS: Dysregulation of TP53, p16/RB1, and PTEN was demonstrated in 90%, 95%, and 41% of np-LMS, respectively. PTEN inactivation was more common in soft tissue np-LMS than uterine np-LMS (55% vs 31%; P = .0005). Moderate-strong ER expression was more common in uterine np-LMS than soft tissue np-LMS (50% vs 7%; P < .0001). Co-inactivation of TP53 and RB1 was found in 81% of np-LMS and was common in both soft tissue and uterine np-LMS (90% and 74%, respectively). RB1, p16, and PTEN aberrations were nearly always conserved in p-LMS and np-LMS from the same patients. CONCLUSIONS: These studies show that nearly all np-LMS have TP53 and/or RB1 aberrations. Therefore, therapies targeting cell cycle and DNA damage checkpoint vulnerabilities should be prioritized for evaluations in LMS.
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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".