Anti-PTHrP blockade limits CD8+ T-cell exhaustion in anti-cancer immunotherapy
Bibliographic record
Abstract
Cancer is a major global health concern, with immune suppression hindering treatment. Immunotherapy, specifically immune checkpoint blockage on T cells, has revolutionized cancer treatment. T-cell exhaustion is an abnormal activation state that develops when continuous exposure to antigens, like cancer. In this context, recent evidence suggests that parathyroid hormone-related protein (PTHrP) plays a previously underappreciated role in fostering an immunosuppressive tumor microenvironment. Further, blocking PTHrP activity reduces primary tumor growth, prevents metastasis, and prolongs survival in mice with various cancers. Here, we confirm that administration of anti-PTHrP monoclonal antibodies can reduce the growth of B16-PDL1 melanoma tumors and that although the therapy did not alter the presence of CD4+ and CD8+ TILs, we noted that all stages of T-cell exhaustion were reduced. Further, the expression of cytolytic proteins PERFORIN and GZMB also increased. By contrast, anti-PTHrP therapy increased the relative presence of pre-pro B cells with a decline in mature B cells in the bone marrow. Overall, our data indicates that anti-PTHrP therapy acts by reducing T-cell exhaustion and by affecting B-cell development. These provide further mechanistic evidence to support the application of anti-PTHrP blockade as an alternate therapeutic approach to boost anti-tumor immunity.
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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) | 0.003 | 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".