Efficacy and safety of pembrolizumab monotherapy in patients with advanced thyroid cancer in the phase 2 KEYNOTE‐158 study
Bibliographic record
Abstract
BACKGROUND: The authors report results from the thyroid carcinoma cohort of the multicohort phase 2 KEYNOTE-158 study (NCT02628067), which evaluated pembrolizumab monotherapy in patients with previously treated cancers. METHODS: Eligible patients had histologically and/or cytologically confirmed papillary or follicular thyroid carcinoma, failure of or intolerance to prior therapy, and measurable disease per Response Evaluation Criteria in Solid Tumors (RECIST) v1.1. Patients received pembrolizumab (200 mg) every 3 weeks for up to 35 cycles. The primary end point was objective response rate (ORR) per RECIST v1.1 by independent central review. RESULTS: A total of 103 patients were enrolled and received pembrolizumab. Median duration from first dose to data cutoff (October 5, 2020) was 49.4 (range, 43.9-54.9) months. ORR was 6.8% (95% confidence interval [CI], 2.8%-13.5%), and median duration of response was 18.4 (range, 4.2-47.2+) months. ORR was 8.7% (95% CI, 2.4%-20.8%) among patients with programmed cell death ligand 1 (PD-L1) combined positive score (CPS) ≥1 (n = 46) and 5.7% (95% CI, 1.2%-15.7%) among patients with PD-L1 CPS <1 (n = 53). Median overall survival and progression-free survival were 34.5 (95% CI, 21.2 to not reached) and 4.2 (95% CI, 3.9-6.2) months, respectively. Treatment-related adverse events occurred in 69.9% of patients (grade 3-5, 14.6%). CONCLUSIONS: Pembrolizumab demonstrated manageable toxicity and durable antitumor activity in a small subset of patients with advanced thyroid cancer. These results provide evidence of modest antitumor activity in this setting regardless of tumor PD-L1 expression. Future studies evaluating immune checkpoint inhibitors in patients with differentiated thyroid cancer should focus on biomarker-driven patient selection or combination of immune checkpoint inhibitors with other agents, in order to achieve higher response rates than observed in this study.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".