Molecular determinants of outcome with mammalian target of rapamycin inhibition in endometrial cancer
Bibliographic record
Abstract
BACKGROUND: Targeting the phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway is of increasing interest as a therapeutic strategy in many tumors. The aim of this study was to identify molecular markers associated with mTOR inhibitor activity in women with metastatic endometrial cancer. METHODS: Archival tumor samples were collected from 94 women with recurrent or metastatic endometrial cancer who participated in 3 National Cancer Insitute of Canada Clinical Trials Group phase 2 trials investigating single-agent mTOR inhibitors: IND160A and IND160B (temsirolimus) and IND192 (ridaforolimus). Analyses included mutational profiling using the OncoCarta Panel version 1.0 and immunohistochemical expression of the tumor suppressor gene PTEN (phosphatase and tensin homologue) and stathmin, a marker of PI3K activation. Associations between biomarker results and clinical outcomes were assessed. RESULTS: Mutations were found in 32 of 73 analyzed tumors, PIK3CA (21 patients) was the most common mutated gene. Co-mutations were seen in 8 tumors, most frequently KRAS and PIK3CA (4 cases). PTEN loss was observed in 46 of 85 samples analyzed and increased stathmin expression was observed in 15 of 65 analyzed samples. No correlation was observed between biomarkers and response or progression. In patients taking concurrent metformin, there was a trend toward lower progression, of 11.8% versus 32.5% (P = .14). CONCLUSIONS: No predictive biomarker or combination of biomarkers for mTOR inhibitor activity were identified in this study. Restriction and enrichment of study entry, especially based on archival tumor tissue, should be undertaken with caution in trials using these agents.
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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 | 0.003 |
| 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) | 0.001 | 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".