Disruption of IRElα through its Kinase Domain Attenuates Multiple Myeloma
Bibliographic record
Abstract
Abstract Multiple myeloma (MM) arises from malignant immunoglobulin-secreting plasma cells and remains an incurable, often lethal disease despite recent therapeutic advances. The unfolded-protein response sensor IRE1α supports protein secretion by deploying a kinase-endoribonuclease module to activate the transcription factor XBP1s. MM cells may coopt the IRE1α-XBP1s pathway; however, the validity of IRE1α as a potential MM therapeutic target is controversial. Here we show that genetic disruption of IRE1α or XBP1s, or pharmacologic IRE1α kinase inhibition, attenuated subcutaneous or orthometastatic growth of MM tumors in mice, and augmented efficacy of two well-established frontline antimyeloma agents, bortezomib or lenalidomide. Mechanistically, IRE1α perturbation inhibited expression of key components of the ER-associated degradation machinery, as well as cytokines and chemokines known to promote MM growth. Selective IRE1α kinase inhibition reduced viability of CD138 + plasma cells while sparing CD138 − cells from bone marrow of newly diagnosed MM patients or patients whose disease relapsed after 1 - 4 lines of treatment in both US- and EU-based cohorts. IRE1α inhibition preserved survival and glucose-induced insulin secretion by pancreatic microislets. Together, these results establish a strong therapeutic rationale for targeting IRE1α with kinase-based small-molecule inhibitors in MM. Significance statement Multiple myeloma (MM) is a lethal malignancy of plasma cells. MM cells have an expanded endoplasmic reticulum (ER) that is constantly under stress due to immunoglobulin hyperproduction. The ER-resident sensor IRE1α mitigates ER stress by expanding the ER’s protein-folding capacity while supporting proteasomal degradation of misfolded ER proteins. IRE1α elaborates these functions by deploying its cytoplasmic kinase-RNase module to activate the transcription factor XBP1s. The validity of IRE1α as a potential therapeutic target in MM has been questioned. Using genetic and pharmacologic disruption in vitro and in vivo, we demonstrate that the IRE1α-XBP1s pathway plays a critical role in MM growth. We further show that IRE1α’s kinase domain is an effective and safe potential small-molecule target for MM therapy.
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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 | 0.001 |
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".