Efficacy of selpercatinib after prior systemic therapy in patients with <i>RET</i> mutant medullary thyroid cancer.
Bibliographic record
Abstract
6074 Background: Selpercatinib is a first-in-class, CNS active, highly selective, and potent RET kinase inhibitor which has demonstrated durable antitumor activity in patients (pts) with RET altered thyroid cancer and is approved in multiple countries for the treatment of RET fusion+ lung or thyroid cancers. As response rates to cancer therapy usually decline on subsequent lines of therapy, the efficacy of selpercatinib was examined in the context of the last prior therapy received before trial enrollment. Methods: Pts with RET mutant medullary thyroid cancer (MTC) previously treated with multikinase inhibitors (cabozantinib and/or vandetanib) were enrolled in the global LIBRETTO-001 trial (NCT03157128). This post-hoc exploratory intrapatient analysis, based on March 30, 2020 data cutoff date, was performed to compare the retrospective physician-reported objective response rate (ORR) from the last systemic therapy prior to enrollment, as reported in pts case reports, to ORR by independent review committee per RECIST 1.1 with selpercatinib treatment, with each patient serving as his/her own control. Results: Efficacy-evaluable pts, 64% male, 90% white with a median age of 58 years, received prior therapy for MTC (n = 143). Pts had a median of 2 (range 1-8) prior systemic regimens. The ORR on selpercatinib (69%) was markedly higher than for the last prior therapy (10%) received before enrollment. ORR improvements with selpercatinib were observed regardless of prior therapy: cabozantinib (66% vs 14%) or vandetanib (71% vs 12%). Fewer pts had progressive disease as their best overall response with selpercatinib (2/143; 1.4%) compared to last prior therapy (33/143; 23.1%). Notably selpercatinib achieved 62% ORR in pts that did not respond to their previous line of therapy prior to enrolment. This shift from non-responder to responder on selpercatinib therapy was consistent regardless of prior cabozantinib or vandetanib treatment, where pts achieved 57% and 61% ORR respectively when subsequently treated with selpercatinib. In contrast, only 3% of patients did not respond to selpercatinib after a previous response to the immediate prior therapy. Similarly, 5% and 2% of patients were non-responders on selpercatinib after a prior response with cabozantinib and vandetanib therapy respectively. Conclusions: Prior to selpercatinib, response with previous multikinase therapy was rare. By contrast, selpercatinib demonstrated robust efficacy regardless of response to or specific prior therapy in pts with RET mutant MTC. Clinical trial information: NCT03157128.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".