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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".