Combination Therapy with HSP90 Inhibitor 17-DMAG Reconditions the Tumor Microenvironment to Improve Recruitment of Therapeutic T cells
Bibliographic record
Abstract
Ineffective recognition of tumor cells by CD8+ T cells is a limitation of cancer immunotherapy. Therefore, treatment regimens that coordinately promote enhanced antitumor CD8+ T-cell activation, delivery, and target cell recognition should yield greater clinical benefit. Using an MCA205 sarcoma model, we show that in vitro treatment of tumor cells with the HSP90 inhibitor 17-DMAG results in the transient (proteasome-dependent) degradation of the HSP90 client protein EphA2 and the subsequent increased recognition of tumor cells by Type-1 anti-EphA2 CD8+ T cells. In vivo administration of 17-DMAG to tumor-bearing mice led to slowed tumor growth, enhanced/prolonged recognition of tumor cells by anti-EphA2 CD8+ T cells, reduced levels of myeloid-derived suppressor cells and regulatory T cells in the tumor microenvironment, and activation of tumor-associated vascular endothelial cells in association with elevated levels of Type-1 tumor-infiltrating lymphocytes. When combined with EphA2-specific active vaccination or the adoptive transfer of EphA2-specific CD8+ T cells, 17-DMAG cotreatment yielded a superior tumor therapeutic regimen that was capable of rendering animals free of disease. Taken together, our findings indicate that 17-DMAG functions as an immune adjuvant in the context of vaccines targeting EphA2.
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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 | 0.001 | 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".