Data from RITA Inhibits Multiple Myeloma Cell Growth through Induction of p53-Mediated Caspase-Dependent Apoptosis and Synergistically Enhances Nutlin-Induced Cytotoxic Responses
Bibliographic record
Abstract
Abstract Mutations or deletions of p53 are relatively rare in multiple myeloma (MM), at least in newly diagnosed patients. Thus, restoration of p53 tumor suppressor function in MM by blocking the inhibitory role of murine double minute 2 (MDM2) is a promising and applicable therapeutic strategy. RITA and nutlin are two new classes of small molecule MDM2 inhibitors that prevent the p53-MDM2 interaction. Earlier reports showed p53-dependent activity of RITA in solid tumors as well as in leukemias. We and others recently described nutlin-induced apoptosis in MM cells, but it remains unclear whether RITA exerts antimyeloma activity. Here, we found that RITA activates the p53 pathway and induces apoptosis in MM cell lines and primary MM samples, preferentially killing myeloma cells. The activation of p53 induced by RITA was mediated through modulation of multiple apoptotic regulatory proteins, including upregulation of a proapoptotic protein (NOXA), downregulation of an antiapoptotic protein, Mcl-1, and activation of caspases through extrinsic pathways. Moreover, a number of key p53-mediated apoptotic target genes were identified by gene expression profiling and further validated by quantitative real-time PCR. Importantly, the combination of RITA with nutlin displayed a strong synergism on growth inhibition with the combination index ranging from 0.56 to 0.82 in MM cells. Our data support further clinical evaluation of RITA as a potential novel therapeutic intervention in MM. Mol Cancer Ther; 9(11); 3041–51. ©2010 AACR.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".