Surface and Global Proteome Analyses Identify ENPP1 and Other Surface Proteins as Actionable Immunotherapeutic Targets in Ewing Sarcoma
Bibliographic record
Abstract
PURPOSE: Ewing sarcoma is the second most common bone sarcoma in children, with 1 case per 1.5 million in the United States. Although the survival rate of patients diagnosed with localized disease is approximately 70%, this decreases to approximately 30% for patients with metastatic disease and only approximately 10% for treatment-refractory disease, which have not changed for decades. Therefore, new therapeutic strategies are urgently needed for metastatic and refractory Ewing sarcoma. EXPERIMENTAL DESIGN: This study analyzed 19 unique Ewing sarcoma patient- or cell line-derived xenografts (from 14 primary and 5 metastatic specimens) using proteomics to identify surface proteins for potential immunotherapeutic targeting. Plasma membranes were enriched using density gradient ultracentrifugation and compared with a reference standard of 12 immortalized non-Ewing sarcoma cell lines prepared in a similar manner. In parallel, global proteome analysis was carried out on each model to complement the surfaceome data. All models were analyzed by Tandem Mass Tags-based mass spectrometry to quantify identified proteins. RESULTS: The surfaceome and global proteome analyses identified 1,131 and 1,030 annotated surface proteins, respectively. Among surface proteins identified, both approaches identified known Ewing sarcoma-associated proteins, including IL1RAP, CD99, STEAP1, and ADGRG2, and many new cell surface targets, including ENPP1 and CDH11. Robust staining of ENPP1 was demonstrated in Ewing sarcoma tumors compared with other childhood sarcomas and normal tissues. CONCLUSIONS: Our comprehensive proteomic characterization of the Ewing sarcoma surfaceome provides a rich resource of surface-expressed proteins in Ewing sarcoma. This dataset provides the preclinical justification for exploration of targets such as ENPP1 for potential immunotherapeutic application in Ewing sarcoma. See related commentary by Bailey, p. 934.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".