A proteogenomic surfaceome study identifies DLK1 as an immunotherapeutic target in neuroblastoma
Bibliographic record
Abstract
Cancer immunotherapies produce remarkable results in B cell malignancies; however, optimal cell surface targets for many solid cancers remain elusive. Here, we present an integrative proteomic, transcriptomic, and epigenomic analysis of tumor and normal tissues to identify biologically relevant cell surface immunotherapeutic targets for neuroblastoma, an often-fatal childhood cancer. Proteogenomic analyses reveal sixty high-confidence candidate immunotherapeutic targets, and we prioritize delta-like canonical notch ligand 1 (DLK1) for further study. High expression of DLK1 directly correlates with a super-enhancer. Immunofluorescence, flow cytometry, and immunohistochemistry show robust cell surface expression of DLK1. Short hairpin RNA mediated silencing of DLK1 in neuroblastoma cells results in increased cellular differentiation. ADCT-701, a DLK1-targeting antibody-drug conjugate (ADC), shows potent and specific cytotoxicity in DLK1-expressing neuroblastoma xenograft models. Since high DLK1 expression is found in several adult and pediatric cancers, our study demonstrates the utility of a proteogenomic approach and credentials DLK1 as an immunotherapeutic target.
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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) | 0.001 | 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".