Abstract B023: Prospective clinical genomic profiling of ewing sarcoma: <i>ERF</i> and <i>FGFR1</i> mutations as recurrent secondary alterations of potential biological and therapeutic relevance
Notice bibliographique
Résumé
Abstract Background: Ewing Sarcoma (ES) is a primitive sarcoma defined by EWSR1–ETS fusions as the primary driver alteration. To expand our understanding of the genetic and molecular characterization of ES, we conducted a comprehensive analysis of clinical genomic profiling data on tumors from 113 patients using the MSK-IMPACT platform (Integrated Mutation Profiling of Actionable Cancer Targets). Methods: The dataset consisted of ES patients prospectively tested with the FDA-cleared MSK-IMPACT large panel, hybrid capture-based NGS assay. To assess the functional significance of ERF loss, we generated ES cell lines with increased expression of ERF as well as lines with knockdown of ERF. We assessed cell viability, clonogenic growth, and motility and performed transcriptomic and epigenetic analyses. Finally, we validated our findings in vivo using cell line xenografts. Results: Unlike previous ES genomic cohorts, ours included more adult patients (>18 years of age) and more patients with advanced stage at presentation. TP53, STAG2, and CDKN2A were the most common alterations and were associated with worse overall survival at 5-years. Notably, 3% had activating FGFR1 alterations (1 amplification and 2 hotspot activating kinase domain mutations). Mining data generated using a targeted RNAseq assay that includes FGFR1 based on the Archer Anchored Multiplex PCR technology, FGFR1 was highly expressed in the ES cohort (N=42). The 2 patients with activating FGFR1 mutations had relatively high expression of FGFR1. The second novel subset of patients in our cohort were defined by recurrent secondary alterations in ERF, which encodes an ETS domain transcriptional repressor, in 7% of patients (5 truncating mutations, 1 deep deletion, 2 missense mutations). ERF alterations were non-overlapping with STAG2 alterations, suggesting a potentially important biologic role in ES. As the functional significance of FGFR1 mutation in ES has been previously studied, we focused our functional studies on the role of ERF status in ES. In vitro, increased expression of ERF decreased tumor cell growth, colony formation, and motility in two ES cell lines, while ERF loss induced cellular proliferation and clonogenic growth. Transcriptomic analysis of cell lines with ERF loss revealed increased expression of genes and pathways associated with aggressive tumor biology, and epigenetic, chromatin-based studies revealed that ERF competes with EWSR1-FLI1 at ETS binding sites. Conclusion: Our study reveals a previously unexplored role of ERF loss-of-function in ES. Older age in our cohort, and a higher proportion of patients with advanced disease at presentation, could potentially explain the finding of ERF alterations which were associated with aggressive tumor biology in our preclinical studies. Our functional analyses of how ERF modulates EWSR1-FLI1 oncogenicity may open a new window into the pathobiology of ES. Moreover, our data suggest that 3% of ES patients harbor activating FGFR1 mutations, the first targetable kinase alteration in this sarcoma. Citation Format: Arielle Elkrief, Koichi Ogura, Anita S. Bowman, Richard P. Koche, Ryma Benayed, Audrey Mauguen, Marissa S. Mattar, Inna Khodos, Elisa de Stanchina, Paul A. Meyers, John H. Healey, William D. Tap, Neerav Shukla, Meera Hameed, Ahmet Zehir, Charles Sawyers, Rohit Bose, Emily Slotkin, Marc Ladanyi. Prospective clinical genomic profiling of ewing sarcoma: ERF and FGFR1 mutations as recurrent secondary alterations of potential biological and therapeutic relevance [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B023.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».