Enhancing c-MYC degradation via 20S proteasome activation induces <i>in vivo</i> anti-tumor efficacy
Bibliographic record
Abstract
SUMMARY Enhancing proteasome activity is a potential new therapeutic strategy to prevent the accumulation of aberrant high levels of protein that drive the pathogenesis of many diseases. Herein, we examine the use of small molecules to activate the 20S proteasome to reduce aberrant signaling by the undruggable oncoprotein c-MYC, to treat c-MYC driven oncogenesis. Overexpression of c-MYC is found in more than 50% of all human cancer but remains undruggable because of its highly dynamic intrinsically disordered 3-D conformation, which renders traditional therapeutic strategies largely ineffective. We demonstrate herein that small molecule activation of the 20S proteasome targets dysregulated intrinsically disordered proteins (IDPs), including c-MYC, and reduces cancer growth in vitro and in vivo models of multiple myeloma, and is even effective in bortezomib resistant cells and unresponsive patient samples. Genomic analysis of various cancer pathways showed that proteasome activation results in downregulation of many c-MYC target genes. Moreover, proteasome enhancement was well tolerated in mice and dogs. These data support the therapeutic potential of 20S proteasome activation in targeting IDP-driven proteotoxic disorders, including cancer, and demonstrate that this new therapeutic strategy is well tolerated in vivo .
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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".