Larotrectinib resistance in TRK fusion cancers: Analysis of a tumor-agnostic, global clinical trial dataset.
Notice bibliographique
Résumé
3066 Background: Larotrectinib (laro) is the first-in-class, highly selective, TRK inhibitor approved for tumor- and age-agnostic use in TRK fusion cancers. This is the seminal report of primary and secondary laro resistance based on an analysis of the regulatory dataset that supported drug approval across multiple countries. Methods: Genomic data from patients (pts) with non-primary CNS TRK fusion cancer enrolled in a global, prospective, multicenter database of three laro clinical trials including adult and pediatric pts were analyzed. Tumor DNA (Illumina TruSight Oncology [TSO] Comprehensive, TSO 500, or FoundationOne CDx) or circulating tumor DNA (Guardant360 or GuardantOMNI) NGS was performed pre-laro (baseline; BL) and post-laro initiation. On-target NTRK (solvent front [SF], gatekeeper [GK], xDFG) mutations and COSMIC-classified tier 1/2 off-target alterations were identified. Primary laro resistance analysis set included pts with no meaningful clinical benefit (PD/SD < 4 months). Secondary (acquired) laro resistance analysis set included pts who developed resistance after meaningful clinical benefit (CR/PR/SD ≥4 months). Data cutoff: July 20, 2024. Results: Of 304 adult and pediatric pts enrolled, 216 had BL genomic data. Primary laro resistance was observed in 24 pts. Only 1 pt had an on-target mutation ( NTRK3 G623R), likely attributable to prior crizotinib; 9 pts (38%) had off-target alterations involving AKT, BRAF, FGFR1, GNAS, KRAS, NRAS, and PIK3CA . Secondary laro resistance was observed in 55 pts with valid post-BL ctDNA (the most common of these TRK fusion cancers were infantile fibrosarcoma [22%], other soft tissue sarcoma [18%], thyroid [11%], lung and salivary gland [9% each]); acquired alterations were identified in 16 of these pts. On-target resistance alone was observed in 5 of 16 pts (31%) and were mainly SF or GK single or double mutation-mediated ( NTRK1 F589L, NTRK1 G595R, NTRK3 G623R [n = 2], NTRK3 G623R/G696A). One xDFG mutation was identified. Off-target resistance alone was observed in 7 of 16 pts (44%) and included hotspot KRAS G12D/A/S/V or G13D, PIK3CA E545K or E542A, BRAF V600E, and GNAS R844H/C mutations. Complex, combined on-target and off-target resistance was observed in 4 of 16 pts (25%): on-target SF or GK alterations ( NTRK1 G595R, NTRK1 F589L/G595R, NTRK3 G623R, NTRK3 G623R/F617L) co-occurred with KRAS G12D or G12D/G13D, and NRAS G12D or Q61H. An analysis of resistance profiles by cancer type and age will be presented. Conclusions: In this analysis, on-target resistance to laro, including potential double NTRK resistance mutations, was commonly observed. Off-target, largely MAPK or PI3K/AKT pathway reactivating resistance, also occurred. In select cases, complex and likely polyclonal resistance including both on-target and off-target alterations were identified. These observations impact novel therapy development for TRK fusion cancers. Clinical trial information: NCT02637687 , NCT02576431 , NCT02122913 .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».