Abstract A64: Functional genomics of metastatic Ewing sarcoma
Bibliographic record
Abstract
Abstract Ewing sarcoma (ES) is a poorly differentiated bone and soft tissue tumor of high metastatic potential. ES mainly affects children, adolescents, and young adults (AYAs) at a frequency of ~1.5 cases per million globally. Ewing neoplasms strikingly converge on a single recurrent initiating event, which is a chromosomal translocation that generates a fusion transcript between the EWSR1 gene and a gene of the ETS family of transcription factors, most commonly FLi1 (85%). After 20 years since the discovery of the EWS-FLi1 fusion, ES remains a clinical challenge with unacceptably low survival rates, primarily due to metastasis. The bulk of research conducted to date (>95%) being focused on the primary tumor has resulted in a critical knowledge gap. To address this issue and unravel the dysregulated pathways in ES tumor evolution and metastatic dissemination, we harnessed the genome-wide insertional mechanism of transposons and the transduction efficiency of lentiviruses to engineer ES cell models. Human mesenchymal stem cells (hMSC), the putative cells of origin of ES, were engineered to constitutively express EWS-Fli1 accompanied by the inducible expression of a highly active transposase. Upon activation of the former, transposon-mediated mutagenesis will activate oncogenes and inactivate tumor suppressor genes, to mediate transformation of the transduced that can now engraft when implanted in recipient mice. Xenografted tumors are resected and the mice observed for development of distant metastases. Matching primary and metastatic tumors are sequenced to uncover the genes commonly affected by transposition in the two compartments. Pathway-oriented bioinformatic analysis will further reveal candidate metastatic driver genes. Matching of the targeted pathways with drugs will be used to validate the candidates by in vitro and in vivo metastasis assays. Citation Format: Wajih Jawhar, Paul Waterhouse, Rama Khokha, Takeaki Ishii, Robert Turcotte, Nada Jabado, Livia Garzia. Functional genomics of metastatic Ewing sarcoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A64.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".