Cell Surface Notch Ligand DLL3 is a Therapeutic Target in Isocitrate Dehydrogenase–mutant Glioma
Bibliographic record
Abstract
Abstract Purpose: Isocitrate dehydrogenase (IDH)-mutant glioma is a distinct glioma molecular subtype for which no effective molecularly directed therapy exists. Low-grade gliomas, which are 80%–90% IDH-mutant, have high RNA levels of the cell surface Notch ligand DLL3. We sought to determine DLL3 expression by IHC in glioma molecular subtypes and the potential efficacy of an anti-DLL3 antibody–drug conjugate (ADC), rovalpituzumab tesirine (Rova-T), in IDH-mutant glioma. Experimental Design: We evaluated DLL3 expression by RNA using TCGA data and by IHC in a discovery set of 63 gliomas and 20 nontumor brain tissues and a validation set of 62 known IDH wild-type and mutant gliomas using a monoclonal anti-DLL3 antibody. Genotype was determined using a DNA methylation array classifier or by sequencing. The effect of Rova-T on patient-derived endogenous IDH-mutant glioma tumorspheres was determined by cell viability assay. Results: Compared to IDH wild-type glioblastoma, IDH-mutant gliomas have significantly higher DLL3 RNA (P < 1 × 10−15) and protein by IHC (P = 0.0014 and P < 4.3 × 10−6 in the discovery and validation set, respectively). DLL3 immunostaining was intense and homogeneous in IDH-mutant gliomas, retained in all recurrent tumors, and detected in only 1 of 20 nontumor brains. Patient-derived IDH-mutant glioma tumorspheres overexpressed DLL3 and were potently sensitive to Rova-T in an antigen-dependent manner. Conclusions: DLL3 is selectively and homogeneously expressed in IDH-mutant gliomas and can be targeted with Rova-T in patient-derived IDH-mutant glioma tumorspheres. Our findings are potentially immediately translatable and have implications for therapeutic strategies that exploit cell surface tumor-associated antigens.
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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".