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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".