Immune Checkpoint Inhibitor Therapy for Aggressive Pituitary Neuroendocrine Tumors
Bibliographic record
Abstract
CONTEXT: Pituitary neuroendocrine tumors (PitNETs) that progress following surgery and radiotherapy are in need of additional treatment options. Responses to immune checkpoint inhibitors (ICIs) have been described; however, the feasibility of ICI therapy and biomarkers of response have not been formally assessed. OBJECTIVE: To evaluate the activity of ICI as a treatment for PitNETs. METHODS: We performed a single-center, prospective, phase 2 trial investigating the activity of ipilimumab and nivolumab in patients with PitNETs. The primary endpoint was objective response using the iRANO response criteria. We then explored genetic biomarkers of response to ICIs in 13 patients with PitNETs who were treated with ICIs, on or outside of the trial. RESULTS: Ten patients with a PitNET were enrolled, including 5 corticotroph, 4 lactotroph, and 1 somatotroph tumor, of which 9/10 (4 metastatic and 5 nonmetastatic) patients were evaluable for the primary endpoint. While no objective responses were observed, tumor shrinkage was seen in 2/9 patients. In a biomarker discovery cohort, comprising 7 tumors treated on trial and 6 tumors treated off trial, temozolomide hypermutation, and mismatch repair deficiency (MMRd) were associated with immunological response. In 3 tumors sequenced pre- and post-ICI treatment, we identified evidence of immunoediting, characterized by loss of MMRd and/or a decrease in tumor mutational burden. CONCLUSION: This study demonstrates the safety and feasibility of ICI treatment in aggressive PitNETs. We also identified MMRd and temozolomide hypermutation as potential biomarkers of response to ICI. Overall, our data suggest that ICIs might provide an additional treatment option for PitNET; this should be evaluated more broadly in future studies.
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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) | 0.001 | 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 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".