A High-throughput Approach to Identify Effective Systemic Agents for the Treatment of Anaplastic Thyroid Carcinoma
Bibliographic record
Abstract
BACKGROUND: Despite the use of aggressive multimodality treatment, most anaplastic thyroid carcinoma (ATC) patients die within a year of diagnosis. Although the combination of BRAF and MEK inhibitors has recently been approved for use in BRAF-mutated ATC, they remain effective in a minority of patients who are likely to develop drug resistance. There remains a critical clinical need for effective systemic agents for ATC with a reasonable toxicity profile to allow for rapid translational development. MATERIAL AND METHODS: Twelve human thyroid cancer cell lines with comprehensive genomic characterization were used in a high-throughput screening (HTS) of 257 compounds to select agents with maximal growth inhibition. Cell proliferation, colony formation, orthotopic thyroid models, and patient-derived xenograft (PDX) models were used to validate the selected agents. RESULTS: Seventeen compounds were effective, and docetaxel, LBH-589, and pralatrexate were selected for additional in vitro and in vivo analysis as they have been previously approved by the US Food and Drug Administration for other cancers. Significant tumor growth inhibition (TGI) was detected in all tested models treated with LBH-589; pralatrexate demonstrated significant TGI in the orthotopic papillary thyroid carcinoma model and 2 PDX models; and docetaxel demonstrated significant TGI only in the context of mutant TP53. CONCLUSIONS: HTS identified classes of systemic agents that demonstrate preferential effectiveness against aggressive thyroid cancers, particularly those with mutant TP53. Preclinical validation in both orthotopic and PDX models, which are accurate in vivo models mimicking tumor microenvironment, may support initiation of early-phase clinical trials in non-BRAF mutated or refractory to BRAF/MEK inhibition ATC.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".