Mechanistic target of rapamycin (MTOR) protein expression in the tumor and its microenvironment correlates with more aggressive pathology at cystectomy
Bibliographic record
Abstract
BACKGROUND: The mechanistic target of rapamycin (mTOR) has been implicated in driving tumor biology in multiple malignancies, including urothelial carcinoma (UC). We investigate how mTOR and phosphorylated mTOR (pmTOR) protein expression correlate with chemoresponsiveness in the tumor and its microenvironment at final pathologic staging after neoadjuvant chemotherapy (NAC). METHODS: A single-institution retrospective analysis was performed on 62 patients with cT2-4Nany UC undergoing NAC followed by radical cystectomy. Diagnostic (transurethral resection specimens, TURBT) and postchemotherapy radical cystectomy specimens were evaluated for mTOR and pmTOR protein expression using immunohistochemistry of the tumor, peritumoral stroma, and normal surrounding stroma. Protein expression levels were compared between clinical and pathologic stage. Whole transcriptome analysis was performed to evaluate mRNA expression relative to mTOR pathway activation. RESULTS: Baseline levels of mTOR and pmTOR within TURBT specimens were not associated with clinical stage and response to chemotherapy overall. Nonresponders with advanced pathologic stage at cystectomy (ypT2-4/ypTanyN+) had significantly elevated mTOR tumor staining (P = 0.006) and a sustained mTOR and pmTOR staining in the peritumoral and surrounding normal stroma (NS). Several genes relevant to mTOR activity were found to be up-regulated in the tumors of nonresponders. Remarkably, complete responders at cystectomy (ypT0) had significant decreases in both mTOR and pmTOR protein expression in the peritumoral and normal stroma (P = 0.01-0.03). CONCLUSIONS: Our results suggest that mTOR pathway activity is increased in tumor and sustained in its microenvironment in patients with adverse pathologic findings at cystectomy. These findings suggest the relevance of targeting this pathway in bladder cancer.
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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 | 0.000 | 0.001 |
| 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".