A Novel Chemotherapy Combination to Enhance Proteotoxic Cell Death in Hepatocellular Carcinoma Experimental Models Without Killing Non-Cancer Cells
Bibliographic record
Abstract
Inhibitors of the ubiquitin-proteasome system increase proteotoxic stress and have achieved clinical success for multiple myeloma but not for solid cancers such as hepatocellular carcinoma. Our objective is to identify a combination with proteasome inhibitors that enhances proteotoxic stress and apoptotic cell death in hepatocellular carcinoma but with less toxicity to non-cancer cells. We found that rencofilstat, a pan-cyclophilin inhibitor, combined with ixazomib, a proteasome inhibitor, increased apoptotic cell death in hepatocellular carcinoma but not in umbilical vein or dermal fibroblast non-cancer cells. We then analyzed the effects of rencofilstat + ixazomib on XBP1s and PERK, critical factors in the unfolded protein response used by cells to survive proteotoxic stress. Rencofilstat + ixazomib maintained higher expression of XBP1s and genetic models suggested that XBP1s was a pro-survival protein early and pro-death protein at later times. Simultaneously, decreased PERK expression prevented the block in protein synthesis via phospho-eIF2α and likely further amplified proteotoxic stress. Rencofilstat + ixazomib did not have effects on XBP1s or PERK in non-cancer cells. Further genetic experiments revealed the pro-survival roles for cyclophilin A and B in mediating rencofilstat + ixazomib-induced cell death. In the Hep3B xenograft model, rencofilstat + ixazomib significantly inhibited tumor volumes/weights without general toxicity. We conclude that rencofilstat + ixazomib amplified proteotoxic stress in hepatocellular carcinoma past a threshold pro-survival pathways could not tolerate, whereas non-cancer cells were less affected.
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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 | 0.001 | 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".