Survival benefit of endocrine dysfunction following immune checkpoint inhibitors for nonthyroidal cancers
Bibliographic record
Abstract
PURPOSE OF REVIEW: Our goal is to review pertinent data evaluating the association between immune checkpoint inhibitor (ICI)-induced endocrine dysfunction and survival in cancer patients as well as to understand the potential molecular links between these. RECENT FINDINGS: ICIs have revolutionized cancer therapy but have also led to multiple immune-related adverse events (irAEs). Studies have demonstrated a link between the development of irAEs and improved survival, suggesting that ICI-induced antitumor immunity and autoimmunity are coupled. Thyroid irAEs are most frequently and strongly associated with improved survival, particularly in the context of overt thyroid dysfunction. Other endocrine irAEs, such as hypophysitis and diabetes are quite rare wherein the treatment approach or the disease process itself may mitigate improvement in survival. Preclinical and translational data indicate a role for CD4+ T cells, regulatory T cells and/or cytokines mediating irAEs, including thyroiditis. SUMMARY: The development of irAEs is associated with improved tumor responses and survival in cancer patients. Thyroid irAEs, alone or in combination with other irAEs, are most strongly associated with improved outcomes. Biomarkers of response to ICIs are lacking, despite well-characterized pathologic and genomic susceptibilities predicting ICI efficacy. Early detection of thyroid irAEs may identify patients most likely to have durable antitumor response to ICIs. Although irAEs and antitumor immunity appear 'coupled', translational studies indicate the potential for their 'uncoupling', which could enable antitumor efficacy with greater safety margins.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".