AMG-232 sensitizes high MDM2-expressing tumor cells to T-cell-mediated killing
Bibliographic record
Abstract
Oncogenic mouse double minute 2 homolog (MDM2) is an E3-ubiquitin ligase that facilitates proteasomal degradation of p53. MDM2 amplification occurs in cancer and has been implicated in accelerated tumor growth, known as hyper-progression, following immune-checkpoint therapy. MDM2 amplification also predicts poor response to immune-checkpoint inhibitors. We sought to evaluate the role of MDM2 in T-cell-mediated immune resistance. Ovarian clear cell carcinoma cell lines carrying wild-type p53 with low/high MDM2 expression were investigated in a T-cell co-culture system evaluating T-cell-mediated tumor killing. Targeting of MDM2 was achieved by siRNA transfection or a selective MDM2 inhibitor, AMG-232 and tumor cells were tested in the T-cell co-culture system. AMG-232 activated p53 signaling in cancer cells and relative resistance to AMG-232 was observed in high MDM2-expressing cell lines. Cell lines with high MDM2 expression were more resistant to T cell-mediated tumor killing. Targeting MDM2 by gene-silencing or pharmacological blockade with AMG-232 enhanced T-cell killing of cancer cells. AMG-232 potentiated tumor cell killing by T-cells in combination with anti-PD-1 antibody treatment, regardless of changes in PD-L1 expression. The AMG-232 was not toxic to the T-cells. MDM2 inhibition lowered expression of Interleukin-6, a pro-inflammatory pro-tumorigenic cytokine. Our data support targeting MDM2 in tumors with overexpression or amplification of MDM2 as a precision therapy approach to overcome drug resistance including hyper-progression in the context of immune checkpoint therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".