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Record W4414011186 · doi:10.1158/1535-7163.mct-25-0428

Advancing Preclinical Biology for Ewing Sarcoma: An International Effort

2025· article· en· W4414011186 on OpenAlexaff
Filemon S. Dela Cruz, Elizabeth Stewart, Didier Surdez, Jessica D. Daley, Alice Soragni, Eleni M. Tomazou, Jaime Alvarez-Perez, Tamar Y. Feinberg, James F. Amatruda, Shireen Ganapathi, Joyce E. Ohm, Christine M. Heske, Sarah Cohen‐Gogo, Dusan Pesic, Joshua O. Nash, Adam Shlien, Elizabeth A. Roundhill, Susan A. Burchill, Brian D. Crompton, Elizabeth R. Lawlor, David M. Loeb, Olivier Delattre, Jaume Mora, Katia Scotlandi, Damon R. Reed, Patrick J. Grohar, Thomas G. P. Grünewald, Heinrich Kovar, Kelly M. Bailey

Bibliographic record

VenueMolecular Cancer Therapeutics · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersNational Institutes of HealthAlan B. Slifka FoundationNational Cancer InstituteSarcoma UK
KeywordsSarcomaBiologyPreclinical testingEwing's sarcomaPreclinical researchCancer researchMedicineComputational biologyVirologyBioinformaticsPathology

Abstract

fetched live from OpenAlex

Ewing sarcoma is an aggressive bone and soft-tissue cancer affecting adolescents and young adults. In vitro and in vivo models of Ewing sarcoma have been instrumental in advancing our understanding of Ewing sarcoma biology and essential in evaluating potential therapies, particularly for metastatic or relapsed disease for which effective treatment options remain limited. Through an international collaborative effort between the Children's Oncology Group Bone Tumor Committee and the Euro Ewing Consortium, we review the current landscape of preclinical modeling used in Ewing sarcoma research encompassing both in vitro (cell lines and tumor organoids) and in vivo (mouse and nonmammalian xenografts) model systems. We discuss factors that can influence experimental results, provide testing considerations for both in vitro and in vivo studies, and descriptions of existing preclinical data repositories. We highlight current needs in Ewing sarcoma modeling and the importance of enhanced international cooperative research and patient advocacy efforts which will be critical in expanding our resources of biologically relevant Ewing sarcoma models to enable translation of preclinical findings into effective therapeutic strategies for patients with Ewing sarcoma.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.363
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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