Targeting MDM2 for Treatment of Adenoid Cystic Carcinoma
Bibliographic record
Abstract
PURPOSE: There are no effective treatment options for patients with advanced adenoid cystic carcinoma (ACC). Here, we evaluated the effect of a new small molecule inhibitor of the MDM2-p53 interaction (MI-773) in preclinical models of ACC. EXPERIMENTAL DESIGN: To evaluate the anti-tumor effect of MI-773, we administered it to mice harboring three different patient-derived xenograft (PDX) models of ACC expressing functional p53. The effect of MI-773 on MDM2, p53, phospho-p53, and p21 was examined by Western blots in 5 low passage primary human ACC cell lines and in MI-773-treated PDX tumors. RESULTS: Single-agent MI-773 caused tumor regression in the 3 PDX models of ACC studied here. For example, we observed a tumor growth inhibition index of 127% in UM-PDX-HACC-5 tumors that was associated with an increase in the fraction of apoptotic cells (P = 0.015). The number of p53-positive cells was increased in MI-773-treated PDX tumors (P < 0.001), with a correspondent shift in p53 localization from the nucleus to the cytoplasm. Western blots demonstrated that MI-773 potently induced expression of p53 and its downstream targets p21, MDM2, and induced phosphorylation of p53 (serine 392) in low passage primary human ACC cells. Notably, MI-773 induced a dose-dependent increase in the fraction of apoptotic ACC cells and in the fraction of cells in the G1 phase of cell cycle (P < 0.05). CONCLUSIONS: Collectively, these data demonstrate that therapeutic inhibition of the MDM2-p53 interaction with MI-773 activates downstream effectors of apoptosis and causes robust tumor regression in preclinical models of ACC. Clin Cancer Res; 22(14); 3550-9. ©2016 AACR.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".