Clinical Application of Next-Generation Sequencing in Advanced Thyroid Cancers
Bibliographic record
Abstract
Background: With the emergence of mutation-based systemic therapies for patients with advanced thyroid cancer, molecular profiling has become an important component of care. Although next-generation sequencing (NGS) gene panels are accessible to clinicians, there is no consensus on the optimal approach to testing. This study investigates the clinical application of NGS results in the management of advanced thyroid cancer. Methods: Patients with advanced thyroid cancer with NGS completed as part of the Integrated Molecular Profiling in Advanced Cancers Trial (IMPACT; NCT01505400) or Ontario-wide Cancer TArgeted Nucleic Acid Evaluation (OCTANE; NCT02906943) clinical trials at the Princess Margaret Cancer Centre were included. Electronic medical records were reviewed to collect clinicopathologic and treatment data. The OncoKB framework was used to categorize molecular alterations based on levels of actionability. Patients with an actionable alteration by OncoKB framework who had treatment with a drug targeting the alteration were categorized as receiving “matched” therapy. Time-to-event data were analyzed using the Kaplan–Meier method. This study was approved by the University Health Network Research Ethics Board (ID# 19-5888). Results: NGS was performed on 118 patients with advanced thyroid cancer between 2013 and 2020. The most common molecular alterations included BRAF V600E (62%) and NRAS (15%) mutations in papillary thyroid cancer, RET alterations (78%) in medullary thyroid cancer, and BRAF V600E (38%) and TP53 (62%) mutations in anaplastic thyroid cancer. Actionable alterations were found in 87% of patients, and 57% of patients had at least one Level 1 or 2 alteration for which Food and Drug Administration (FDA)-approved drug is available. BRAF and RET alterations made up 86% of Level 1 and 2 alterations. A matched therapeutic approach was undertaken in 13% of patients. Conclusion: This study uses a structured framework to analyze the actionability and clinical use of NGS results in advanced thyroid cancer. Most patients had at least one potentially actionable mutation and 57% of patients had at least one Level 1 or 2 alteration, predominantly driven by BRAF V600E and RET alterations. This study rationalizes the need for routine multigene NGS testing or reflex BRAF and RET testing in the management of patients with advanced thyroid cancer.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".