Rechallenge with first-generation RET inhibitors in <i>RET</i> -rearranged NSCLC pre-treated with selpercatinib or pralsetinib: Results from the RET MAP registry.
Bibliographic record
Abstract
8646 Background: RET fusions occur in 1-2% of patients with advanced non-small cell lung cancer (aNSCLC). First-generation RET inhibitors (RETi, selpercatinib and pralsetinib), have improved outcomes across treatment lines. Options at progression remain limited, especially in the absence of novel generation RETi. In real-world settings, rechallenge with the same class RETi is sometimes attempted, though efficacy and safety data are lacking. Methods: This multicenter retrospective analysis of the RET MAP registry included patients with RET -rearranged aNSCLC initially treated with selpercatinib or pralsetinib, followed by rechallenge with the same or a different first-generation RETi, as a single agent or in combination therapy. Clinical features, reasons for initial RETi discontinuation, treatment outcomes, and toxicity were assessed for both treatment courses. Results: Among 354 patients treated with first-generation RETi, same class RETi were re-administered in later lines in 33 (9.3 %) patients. At first RETi administration vs rechallenge, median prior lines were 2 (IQR 2–3) vs 4 (IQR 3–5), ECOG PS 0–1 was observed in 26 (78%) vs 25 (76%) patients, and brain metastases in 9 (27%) vs 13 (39%). Reasons for discontinuation of the first RETi were disease progression in 25 (76%) patients and toxicity in 8 (24%). RETi re-administration involved a change of first-generation RETi in 14 (42%) patients, monotherapy in 22 (67%), combination therapy in 11 (33%) (8 with other targeted agents for by-pass resistance, 3 with chemotherapy). It was given immediately after a prior RETi in 13 (39%) patients. At subsequent RETi treatment after progression on a prior RETi, ORR and median PFS were 18% and 2.17 months (95% CI 1.63–NR), respectively, with single-agent RETi (N=17), and 20% and 4 months (95% CI 3.55–NR), respectively, with RETi combined with other targeted agents (N=8). Patients who previously discontinued RETi due to toxicity (N=8) received a different RETi, with ORR and median PFS of 57% and 9.89 months (95% CI 5.33–NR), respectively. In this subgroup, 3 (37.5%) experienced serious side effects at re-administration of a different first-generation RETi. Conclusions: Rechallenge a different RETi of the same class is effective after initial discontinuation due to toxicity, though recurrent toxicity may occur in one-third of patients. In contrast, RETi rechallenge after progression demonstrates limited efficacy, primarily in selected cases treated with combination therapies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".