CfDNA NGS detection of fusions acquired as mechanisms of resistance in the post-EGFR setting across multiple solid tumors.
Notice bibliographique
Résumé
3053 Background: Oncogenic fusions are rare but have targetable, FDA-approved therapies across all solid tumor types. These fusions have been associated with resistance to EGFR-TKI (EGFRi) in advanced non-small cell lung cancer (NSCLC) and anti-EGFR antibodies in colorectal cancer (CRC), but the prevalence of acquired fusions and their associated clinical outcomes have not been well-studied. We examined fusions in >5,000 patients with advanced NSCLC or CRC who had a history of EGFR-directed therapy in the GuardantINFORM clinical-genomic database. Methods: Fusions in ALK, FGFR1-3, NTRK1-3, ROS1 and RET were examined in a cohort of 2,379 patients with NSCLC and 1,072 with CRC tested with Guardant360 or Guardant360 CDx (Guardant Health, Redwood City, CA) before approved EGFR-targeting therapies, and in 3,531 patients with NSCLC and 2,132 patients with CRC tested after EGFR therapy. Fusion clonality was calculated as the fusion variant allele fraction (VAF):max somatic VAF for each patient, with subclonal fusions defined as having clonality < 0.5. Clinical outcomes were compared between patients with and without fusions in the post-EGFR population, adjusting for age, gender, regimen/line of therapy, and max VAF. Comparisons of fusion prevalence pre- and post-EGFR therapy were performed using Fisher’s exact test. Results: Fusions were detected more frequently in patients with NSCLC and CRC after EGFR therapy relative to pretreatment (NSCLC: 3.2% vs. pre-EGFR 0.5%, p<0.05; CRC: 6.4% vs. pre-EGFR 1.1%, p<0.05). Fusions in RET were most enriched in the post-EGFR NSCLC population (1.0% vs. pre-EGFR 0.1%, OR=11.6, p<0.05), while fusions in the FGFR family were most enriched in the post-EGFR CRC population (3.0% vs. pre-EGFR 0.3%, OR=10.8, p<0.05). The majority of fusions post-EGFR were subclonal in patients with NSCLC (86%) and CRC (96%), consistent with these fusions emerging as mechanisms of resistance. In patients with NSCLC, clinical outcomes (as measured by time-to-next-treatment (TTNT)) were significantly shorter in patients who acquired fusions compared to those without fusions (median 5.9 vs. 9.5 months, log-rank p=0.01, adjusted HR=1.7, adjusted p=0.02). Patients with CRC showed no significant difference in TTNT between those with and without acquired fusions. In patients with NSCLC who acquired fusions, 45% received an EGFRi in the next line and 23% received a TKI targeted to their fusion; TTNT on these therapies was not significantly different (median 5.3 months vs. 6.4 months, respectively). Conclusions: Fusions in patients with NSCLC and CRC were enriched post-EGFR therapy and were associated with inferior clinical outcomes in NSCLC. Recognition of the subclonal prevalence of acquired fusions provides important context as oncologists consider the next line of therapy. This work highlights the potential utility of genomic profiling in patients over the course of treatment.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».