Highly specific Immunoproteasome inhibitor M3258 induces proteotoxic stress and apoptosis in KMT2A::AFF1 driven acute lymphoblastic leukemia
Bibliographic record
Abstract
Proteasome inhibitors (PIs) bortezomib, carfilzomib and ixazomib are approved for the treatment of multiple myeloma and mantle cell lymphoma and have clinical activity in acute lymphoblastic leukemia (ALL). The predominant form of proteasome in these hematologic malignancies is the lymphoid tissue-specific immunoproteasome. FDA-approved PIs inhibit immunoproteasomes and ubiquitously expressed constitutive proteasomes causing on-target toxicities in non-hematological tissues. Replacing PIs with selective immunoproteasome inhibitors (IPIs) should reduce these toxicities. We have previously shown that IPI ONX-0914 causes apoptosis of ALL cells expressing the KMT2A::AFF1 (MLL-AF4) fusion protein but did not elucidate the mechanism. Here we show that a novel, highly specific IPI M3258 induces rapid apoptosis in ALL cells in vitro and is comparable to bortezomib in its ability to reduce tumor growth and to cause tumor regression when combined with chemotherapy in vivo. Treatment of KMT2A::AFF1 ALL cells with M3258, ONX-0914, and bortezomib induced proteotoxic stress that was prevented by the protein synthesis inhibitor cycloheximide, which dramatically desensitized cells to PI-induced apoptosis. Thus, similar to multiple myeloma, ALL cells are sensitive to PIs and IPIs due to increased proteotoxic stress caused by elevated rates of protein synthesis.
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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) | 0.001 | 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".