The Pituitary Immune Environment and Immunotherapy: From Hypophysitis to Pituitary Neuroendocrine Tumors
Bibliographic record
Abstract
The immune landscape plays an important role in various pituitary diseases, ranging from hypophysitis to pituitary neuroendocrine tumors. Moreover, the use of immune checkpoint inhibitors (ICIs) has dramatically altered the landscape of cancer treatment by improving prognosis and overall survival in a multitude of advanced-staged malignancies, though their use in pituitary neuroendocrine tumors has remained limited. In this review, we will focus on selected topics to highlight the impact of the immune microenvironment on the function of the pituitary gland, namely, animal models of autoimmune hypophysitis, including ICI-induced hypophysitis as a common adverse event, and the importance of its early recognition in patients treated with ICIs. Using a case, we will provide an overview on the epidemiology, pathogenesis, clinical spectrum, diagnosis, predictors, and management of ICI-induced hypophysitis. We will also summarize the role of the immune microenvironment in pituitary neuroendocrine tumors with programmed cell death ligand 1 as a biomarker for treatment. Lastly, we will review the role of ICIs in the management of 40 patients with aggressive and metastatic pituitary neuroendocrine tumors. Current knowledge gaps in these topics will also be highlighted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".