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AcceleRET Lung: A phase 3 study of first-line pralsetinib in patients with <i>RET</i> fusion–positive advanced/metastatic NSCLC.

2022· article· en· W4281695484 on OpenAlexaff
Sanjay Popat, Enriqueta Felip, Edward S. Kim, Filippo de Marinis, Byoung Chul Cho, Martin Wermke, Adrianus J. de Langen, Roberto Ferrara, Stephan Kanzler, Fabiana Letizia Cecere, Domenico Galetta, Ki Hyeong Lee, Vanesa Gregorc, Ana Rodrigues, Christian Britschgi, Ahmadur Rahman, Diana Ndunda, Johannes Noé, Danny Lu, Benjamin Besse

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsRoche (Canada)
Fundersnot available
KeywordsMedicinePembrolizumabPemetrexedInternal medicineLung cancerOncologyCrizotinibResponse Evaluation Criteria in Solid TumorsPopulationGemcitabineAdverse effectPhases of clinical researchClinical trialCancerChemotherapyImmunotherapyMalignant pleural effusion

Abstract

fetched live from OpenAlex

TPS9159 Background: RET gene fusions have been identified as oncogenic drivers in multiple tumor types, including 1–2% of non-small cell lung cancer (NSCLC). Pralsetinib is a potent, selective RET inhibitor, approved in the US and EU for the treatment of metastatic RET fusion–positive NSCLC based on the phase 1/2 ARROW study (NCT03037385). In the ARROW study (data cutoff: Nov 6, 2020), patients who initiated 400 mg once daily (QD) of pralsetinib after platinum-based chemotherapy achieved an overall response rate (ORR) of 62%, per independent central review. In the treatment-naïve group, the ORR was 79%. Most treatment-related adverse events were grade 1–2 across the entire safety population treated at 400 mg QD (n=471). AcceleRET Lung, an international, open-label, randomized, phase 3 study (NCT04222972), will evaluate the efficacy and safety of pralsetinib versus standard of care (SOC) for first-line treatment of advanced/metastatic RET fusion–positive NSCLC. Abstracts for this study were previously submitted to the European Lung Cancer Congress 2020, the American Society of Clinical Oncology 2020 annual meeting, and 2020 World Conference on Lung Cancer. Methods: Approximately 226 patients with metastatic RET fusion–positive NSCLC will be randomized 1:1 to oral pralsetinib (400 mg QD) or SOC (non-squamous histology: platinum/pemetrexed ± pembrolizumab followed by maintenance pemetrexed ± pembrolizumab [at investigator’s discretion]; squamous histology: platinum/gemcitabine or platinum + pembrolizumab + paclitaxel/nab-paclitaxel followed by maintenance pembrolizumab). Stratification factors include intended use of pembrolizumab, history of brain metastases, and Eastern Cooperative Oncology Group Performance Status. Key eligibility criteria include no prior systemic treatment for advanced/metastatic NSCLC; RET fusion–positive tumor by local or central assessment; no additional actionable oncogenic drivers; no prior selective RET inhibitor; measurable disease per Response Evaluation Criteria in Solid Tumors (RECIST) v1.1. Patients with central nervous system metastases were permitted if asymptomatic and on a stable dose of corticosteroids. Cross-over to receive pralsetinib upon disease progression will be permitted for patients randomized to SOC. The primary endpoint is progression-free survival (blinded independent central review; RECIST v1.1). Secondary endpoints include ORR, overall survival, duration of response, disease control rate, clinical benefit rate, time to intracranial progression, intracranial ORR, safety/tolerability and quality of life evaluations. Identification of potential biomarkers of antineoplastic activity and resistance was an exploratory endpoint. Recruitment has begun with sites (active or planned) in North America, Central America, Europe and Asia. Clinical trial information: NCT04222972.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.064
GPT teacher head0.497
Teacher spread0.433 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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