NOVEL INSIGHTS IN ADVANCED THYROID CARCINOMA: FROM MECHANISMS TO TREATMENTS: Molecular insights into the origin, biology, and treatment of anaplastic thyroid carcinoma
Bibliographic record
Abstract
Anaplastic thyroid carcinoma (ATC) is among the most daunting entities in clinical oncology. Large-scale genomic studies of thyroid cancer within the last decade have uncovered a distinct set of recurrent somatic alterations implicated in the development, aggressiveness, and treatment resistance of ATC. The sequence of events leading to the development of ATC commonly begins with a tumorigenic mutation that constitutively activates the mitogen-activated protein kinase (MAPK) pathway, giving rise to indolent entities such as well-differentiated papillary or follicular thyroid carcinomas. This is followed by recurring alterations that drive oncogenic properties such as enhanced proliferation, genomic instability, replicative immortality, and dedifferentiation, culminating in the emergence of highly aggressive ATC tumors. The truncal MAPK-activating events present therapeutic opportunities, as small molecule inhibitors against key components of this pathway are available. Indeed, genotype-guided targeting of the MAPK pathway is now the standard of care for subgroups of ATC patients, and further efforts exploring additional MAPK inhibitors and the combination of immune checkpoint blockade with MAPK inhibition are overcoming resistance to the current targeted therapies in the clinic and expanding our arsenal against this disease. In this review, we summarize the current understanding of the genomic landscape of ATC, discuss the biological and clinical ramifications of recurring aberrations, and provide an overview of the opportunities and challenges in the clinical management of this lethal malignancy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".