Selpercatinib in RET fusion-positive non-small-cell lung cancer (SIREN): a retrospective analysis of patients treated through an access program
Bibliographic record
Abstract
Introduction: Rearranged during transfection (RET) gene fusions are rare genetic drivers in non-small cell lung cancer (NSCLC). Selective RET-inhibitors such as selpercatinib have shown therapeutic activity in early clinical trials; however, their efficacy in the real-world setting is unknown. Methods: A retrospective efficacy and safety analysis was performed on data from RET fusion-positive NSCLC patients who participated in a selpercatinib access program (named patient protocol) between August 2019 and January 2021. Results: Data from 50 patients with RET fusion-positive advanced NSCLC treated with selpercatinib at 27 centers in 12 countries was analyzed. Most patients were Non-Asian (90%), female (60%), never-smokers (74%), with a median age of 65 years (range, 38–89). 32% of the patients had known brain metastasis at the time of selpercatinib treatment. Overall, 13 patients were treatment-naïve, while 37 were pretreated with a median of three lines of therapy (range, 1–8). The objective response rate (ORR) was 68% [95% confidence interval (CI), 53–81] in the overall population. The disease control rate was 92%. The median progression-free survival was 15.6 months (95% CI, 8.8–22.4) after a median follow-up of 9 months. In patients with measurable brain metastases ( n = 8) intracranial ORR reached 100%. In total, 88% of patients experienced treatment-related adverse events (TRAEs), a large majority of them being grade 1 or 2. The most common grade ⩾ 3 TRAEs were increased liver enzyme levels (in 10% of patients), prolonged QTc time (4%), abdominal pain (4%), hypertension (4%), and fatigue/asthenia (4%). None of patients discontinued selpercatinib treatment for safety reasons. No new safety concerns were observed, nor where there any treatment-related death. Conclusions: In this real-world setting, the selective RET-inhibitor selpercatinib demonstrated durable systemic and intracranial antitumor activity in RET fusion-positive NSCLC and was well tolerated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".