<scp>SMG1</scp> , a nonsense‐mediated <scp>mRNA</scp> decay ( <scp>NMD</scp> ) regulator, as a candidate therapeutic target in multiple myeloma
Bibliographic record
Abstract
Early data suggested that CC-115, a clinical molecule, already known to inhibit the mammalian target of rapamycin kinase (TORK) and DNA-dependent protein kinase (DNA-PK) may have additional targets beyond TORK and DNA-PK. Therefore, we aimed to identify such target(s) and investigate a potential therapeutic applicability. Functional profiling of 141 cancer cell lines revealed inhibition of kinase suppressor of morphogenesis in genitalia 1 (SMG1), a key regulator of the RNA degradation mechanism nonsense-mediated mRNA decay (NMD), as an additional target of CC-115. CC-115 treatment showed a dose-dependent increase of SMG1-mediated NMD transcripts. A subset of cell lines, including multiple myeloma (MM) cell lines sensitive to the endoplasmic reticulum stress-inducing compound thapsigargin, were highly susceptible to SMG1 inhibition. CC-115 caused the induction of UPR transcripts and cell death by mitochondrial apoptosis, requiring the presence of BAX/BAK and caspase activity. Superior antitumor activity of CC-115 over TORK inhibitors in primary human MM cells and three xenograft mouse models appeared to be via inhibition of SMG1. Our data support further development of SMG1 inhibitors as possible therapeutics in MM.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".