Immune responses in the thyroid cancer microenvironment: making immunotherapy a possible mission
Bibliographic record
Abstract
The incidence of thyroid cancers has been steadily increasing worldwide over the past few decades. Although five-year survival rates for differentiated thyroid cancers are upwards of 90%, clinical outcomes for patients with undifferentiated, recurrent and/or metastatic disease are often dismal despite conventional interventions. As such, there is a demand for novel treatment options. Cancer immunotherapy represents the ultimate form of personalized medicine by leveraging the specificity and potency of a patient's immune system to kill their tumor. The thyroid cancer microenvironment is rich in immunological cells, making it a reasonable candidate for immunotherapy. This review maps out the immunological features of thyroid cancers and how these can be modulated. There are surprising immunological consequences of conventional therapies that demand attention. Also, hormonal modulation of the immune system is highlighted as a unique and confounding feature of thyroid cancers. A variety of cutting-edge immune-based therapies are discussed, with an emphasis placed on how these can be integrated with the current standard of care. Several high priority areas in need of research are also 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".