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Growth recovery in patients with <i>BRAF</i> altered pediatric low-grade gliomas (LGG) after discontinuation of tovorafenib.

2025· article· en· W4410803160 on OpenAlexaff
Cassie Kline, Karen Wright, Lindsay Kilburn, Susan Chi, Daniel Landi, Jasper van der Lugt, Sarah Leary, Mohamed S Abdelbaki, Simon Bailey, Karsten Nysom, Dong‐Anh Khuong‐Quang, Elias Sayour, Olaf Witt, Pablo Hernáiz Driever, Valérie Larouche, Ashley Bailey-Torres, Lindsey Ott, Jiaheng Qiu, Lisa McLeod, Sabine Mueller

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineDiscontinuationGliomaOncologyInternal medicinePediatricsCancer research

Abstract

fetched live from OpenAlex

10029 Background: Tovorafenib is a selective, CNS-penetrant, type II RAF inhibitor that targets BRAF and CRAF. Based on preclinical data, CRAF plays an essential role in chondrocyte maturation, a required step in linear bone growth. Children treated with tovorafenib in early phase studies demonstrate a reversible decrease in growth velocity consistent with CRAF inhibition with no signs of premature closure of growth plates or adverse effects on bone such as fractures or treatment emergent osteopenia. Here we report a combined analysis of off-treatment growth recovery in patients treated with tovorafenib in 3 clinical studies. Methods: Patients aged < 18 years with BRAF altered relapsed/refractory LGG treated with tovorafenib in the Phase 1 PNOC014 study (NCT03429803), Phase 2 FIREFLY-1 study (NCT04775485), or Expanded Access Program (EAP) for patients (NCT05760586) were included. Relevant medical history, neuroendocrine medications, growth parameters, and tovorafenib dosing were collected. Pre- and post-treatment annualized growth velocity (AGV) was calculated for all patients with growth data available ≥90 days post-discontinuation of tovorafenib. Results: As of 17-Jan-2025,38 / 167 (23%) patients were evaluable for growth recovery. Among these evaluable patients, median age at start of treatment was 9.5 yrs (range 3.5 -16.5). Eighteen (47%) patients had a tumor associated endocrinopathy or comorbidity that may affect growth including growth hormone deficiency (8), thyroid disease (8), precocious puberty (6) and panhypopituitarism (4) at baseline. Four (11%) were receiving a gonadotropin-releasing hormone analogue for precocious puberty and 2 (5%) were receiving growth hormone replacement concurrent with tovorafenib. Median baseline height Z-score was -0.13 (range -2.57, 2.64) with 4 patients having Z-score > 2 or < -2. Median on-treatment AGV was 1.7 cm/yr [n = 36, interquartile range (IQR) 0.4 - 2.2] at 12 mo and 2.3 cm/yr (n = 25, IQR 0 - 3.3) at 24 mo. Median age at end of treatment was 11 yrs (range 4.4 - 17.5), and median off-treatment follow up was 10.3 mo (range 3.2 - 37.2). Median off-treatment AGV was 4.3 cm/yr (n = 38; IQR 1.8 - 7.6) at 3 mo, 10.2 cm/yr (n = 26, IQR 2.3 - 13.8) at 6 mo and 7.7cm/yr (n = 5, IQR 4.1 - 13.9) at 12 mo. Thirty-four (89%) patients had recovery of AGV, and 28 (74%) had an increase in Z-score towards baseline indicating catch-up growth. Patients with slow AGV recovery tended to be > 15 years, younger females with precocious puberty/Tanner stage 4, or have only 3 months of off-treatment follow up. Conclusions: Decreases in growth velocity were common during tovorafenib treatment. Majority of patients to date demonstrate AGV recovery as early as 3 months with signs of catchup within 6-12 months after stopping tovorafenib. Preliminary findings indicate tumor-associated precocious puberty/Tanner stage 4 in females may be a risk factor for slow AGV recovery.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.367
Teacher spread0.341 · 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 designObservational
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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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