Combination therapy with proteasome inhibitors and TLR agonists enhances tumour cell death and IL-1β production
Bibliographic record
Abstract
Proteasome inhibitors have emerged as an effective therapy for the treatment of haematological malignancies; however, their efficacy can be limited by the development of tumour resistance mechanisms. Novel combination strategies including the addition of TLR adjuvants to increase cell death and augment immune responses may help enhance their effectiveness. Although generally thought to inhibit inflammatory responses and NF-κB activation, we found that under specific conditions proteasome inhibitors can promote inflammatory responses by mediating IL-1β maturation and secretion after TLR stimulation. This was dependent on the timing of proteasome inhibition relative to TLR stimulation where reversal of treatment order could alternatively increase or inhibit IL-1β secretion (P < 0.001). TLR stimulation combined with proteasome inhibition enhanced cell death in vitro and delayed tumour development in vivo in NOD SCID mice (P < 0.01). However, unlike IL-1β secretion, cell death occurred similarly regardless of treatment order and was only partially caspase dependent, possessing characteristics of both apoptosis and necrosis as indicated by activation of caspase-1, 3, 8 and RIP3 phosphorylation. Although stimulation of various TLRs was capable of driving IL-1β production, TLR4 stimulation was the most effective at increasing cell death in THP-1 and U937 cells. TLR4 stimulation and proteasome inhibition independently activated the RIP3 necroptotic pathway and ultimately reduced the effectiveness of caspase/necroptosis inhibitors in mitigating overall levels of cell death. This strategy of combining TLR stimulation with proteasome inhibition may improve the ability of proteasome inhibitors to generate immunogenic cell death and increase anti-tumour activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".