Mammalian target of rapamycin as a therapeutic target in oncology
Bibliographic record
Abstract
Robert T Abraham PhDa* & Christina H Eng PhDaa Oncology Discovery Research, Wyeth, 401 N. Middletown Road, Pearl River, NY 10965, USA +1 845 602 4595; +1 845 602 5557; † Author for correspondenceBackground: The mammalian target of rapamycin (mTOR) has emerged as a validated therapeutic target in cancer and mTOR inhibitors alter tumor cell responses to mitogenic signals and microenvironmental stress. Objectives: The aims of this review are to describe the mTOR signaling pathway and the rationale for the use of rapamycin analogs and other mTOR inhibitors for oncology indications. Methods: This review presents information from recent publications, as well as some more conjectural viewpoints stemming from the early clinical experience with mTOR inhibitors in cancer patients. Results/conclusions: A thorough understanding of the antitumor mechanisms of the existing mTOR inhibitors will drive the development of effective combination therapies to overcome tumor resistance to these agents. Furthermore, the development of second-generation inhibitors of this critical protein target may yield deeper and broader therapeutic activities in human cancers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".