Hsp90 inhibition leads to an increase in surface expression of multiple immunological receptors in cancer cells
Bibliographic record
Abstract
Heat shock protein 90 (Hsp90) is a molecular chaperone important for maintaining protein homeostasis (proteostasis) in the cell. Hsp90 inhibitors are being explored as cancer therapeutics because of their ability to disrupt proteostasis. Inhibiting Hsp90 increases surface density of the immunological receptor Major Histocompatibility Complex 1 (MHC1). Here we show that this increase occurs across multiple cancer cell lines and with both cytosol-specific and pan-Hsp90 inhibitors. We demonstrate that Hsp90 inhibition also alters surface expression of both IFNGR and PD-L1, two additional immunological receptors that play a significant role in anti-tumour or anti-immune activity in the tumour microenvironment. Hsp90 also negatively regulates IFN-γ activity in cancer cells, suggesting it has a unique role in mediating the immune system's response to cancer. Our data suggests a strong link between Hsp90 activity and the pathways that govern anti-tumour immunity. This highlights the potential for the use of an Hsp90 inhibitor in combination with another currently available cancer treatment, immune checkpoint blockade therapy, which works to prevent immune evasion of cancer cells. Combination checkpoint inhibitor therapy and the use of an Hsp90 inhibitor may potentiate the therapeutic benefits of both treatments and improve prognosis for cancer patients.
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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".