Real-world treatment outcomes and clinicopathologic determinants of response for patients with advanced thyroid cancer treated with first-line lenvatinib.
Bibliographic record
Abstract
e18132 Background: Lenvatinib, a multi-targeted tyrosine kinase inhibitor, is approved for the treatment of locally recurrent or metastatic, progressive radioactive iodine (RAI)-refractory (herein termed ‘advanced’) thyroid cancer, based on the randomized phase III SELECT (Study of Lenvatinib in Differentiated Cancer of the Thyroid) trial where it improved progression-free survival by nearly 15 months. Since then, the use of lenvatinib has substantially increased, however not all patients respond, and there remains a lack of real-world data characterizing efficacy. In the present study, we aimed to evaluate the use and effectiveness of first-line lenvatinib in a genomically-characterized cohort of advanced thyroid cancer patients, and to identify clinicopathological and molecular correlates of drug response. Methods: Patients with advanced, follicular cell-derived thyroid cancer who underwent next-generation sequencing (NGS) at Princess Margaret Cancer Centre and commenced first-line lenvatinib monotherapy between 2015 to 2023 were included. Data were collected retrospectively, and Kaplan-Meier method, log-rank tests and univariable/multivariable proportional hazard models were employed. Results: In total, 77 patients were included (48% female, majority papillary (52%), poorly differentiated (17%) or invasive encapsulated follicular variant papillary (16%)). Most patients (79%) underwent total thyroidectomy and adjuvant RAI (median cumulative dose of 231 mCi). At lenvatinib initiation, the median age was 62.9 years, 68% of patients were ECOG performance status ≥2, 81% had lung metastases and 53% had bone metastases. Most patients started on either 10 mg or 14 mg of lenvatinib daily. The median time to treatment discontinuation was 33 months and the median overall survival was 72.9 months. Older age, ECOG ≥2, liver metastases and TP53 mutation(s) were associated with a shorter time to treatment discontinuation; ECOG ≥2 and TP53 mutation(s) remained significant on multivariable analysis ( p = 0.025 and p = 0.016, respectively). Conclusions: The present study shows the real-world experience using lenvatinib in patients with advanced thyroid cancer, and demonstrates impressive efficacy, including in patients with heterogenous clinical and molecular features. Additionally, our findings provide early evidence for the use of clinicopathologic biomarkers to guide lenvatinib initiation. Larger-scale, multicentre studies will be needed to validate these results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".