Prognostic Significance of MTOR Pathway Component Expression in Neuroendocrine Tumors
Bibliographic record
Abstract
PURPOSE: Clinical studies have implicated the mechanistic target of rapamycin (serine/threonine kinase; MTOR) pathway in the regulation of neuroendocrine tumor (NET) growth. We explored whether expression of MTOR pathway components has prognostic significance in NET patients. PATIENTS AND METHODS: We evaluated immunohistochemical expression of MTOR and phospho (p) -MTOR; its downstream targets RPS6KB1, RPS6, and EIF4EBP1; and its upstream regulators, in a cohort of 195 archival neuroendocrine tumors. We correlated expression levels with clinical outcomes, after adjusting for other prognostic variables. RESULTS: We observed anticipated correlations between expression of upstream components of the MTOR pathway and their downstream targets. Expression of PIK3CA, MTOR, or p-EIF4EBP1 was associated with high MKI67 (Ki-67) labeling index. We failed to identify clinical correlations associated with expression of the upstream regulators TSC1, TSC2, AKT, p-AKT, PDPK1, PTEN, PIK3R1, or PIK3CA. In contrast, high expression of MTOR or its activated downstream targets p-RPS6KB1, p-RPS6, or p-EIF4EBP1 was associated with adverse clinical outcomes. CONCLUSION: Our observations suggest that expression of MTOR or its downstream targets may be adverse prognostic factors in neuroendocrine tumors.
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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".