Abstract A26: Characterization and development of a GPC2 ADC for neuroblastoma and other cancers
Bibliographic record
Abstract
Abstract Introduction: We previously identified glypican 2 (GPC2) as a MYCN-driven neuroblastoma oncoprotein that is robustly differentially expressed, including expression of a tumor specific isoform (Cancer Cell 2017). We exploited this differential expression by developing an antibody-drug conjugate (ADC; D3-GPC2-PBD) that showed potent efficacy in neuroblastoma patient-derived xenografts (PDXs)/xenografts (n=4) (AACR Annual Meeting 2018). Methods: To validate GPC2 expression, we performed GPC2-directed immunohistochemistry (IHC) on a high-risk neuroblastoma tumor microarray (n=64 tumors) and flow cytometry on 8 neuroblastoma PDXs. To determine the GPC2 epitope bound by this ADC, we performed X-ray crystallography and mutational studies. To define mechanisms of cytotoxicity, we quantified DNA damage and apoptosis, immunogenic cell death (ICD), and bystander cell killing. Finally, we performed a pan-cancer analysis for GPC2 expression using RNA sequencing, flow cytometry and IHC. Results: We confirmed GPC2 expression in most high-risk neuroblastomas by IHC (60/64 tumors) and all neuroblastoma PDXs by flow cytometry (8/8 tumors). Additionally, we found a bimodal GPC2 expression pattern in neuroblastoma PDXs (GPC2-Hi and GPC2-UltraHi populations). GPC2-UltraHi cells coexpressed increased levels of the stem cell markers CD133, CD338 and CD117 (e.g., GPC2-UltraHi cell CD133 mean increase mean fluorescent intensity [MFI]=34-fold of GPC2-Hi cells). Forced GPC2 overexpression in SY5Y neuroblastoma cells was sufficient to induce increased stem cell marker expression (e.g., SY5Y-GPC2 CD133 mean increase MFI=3.4-fold of native SY5Y cells). Next, structural studies revealed that the D3-GPC2-Fab binds to a conformational and tumor-specific core GPC2 epitope, findings validated by binding kinetics experiments with GPC2 mutants, and with flow cytometry and ADC susceptibility studies. ADC treatment induced upregulation of γH2AX, cleaved PARP1 and caspase 3, indicative of DNA damage and apoptosis. In addition, we showed translocation of calreticulin to the cell surface and HMGB1 release, consistent with ICD. ADC treatment of co-incubated GPC2-high/low expressing cells induced 10-62% more cytotoxicity than expected, consistent with potent bystander cell killing. Finally, we identified several other cancers that express GPC2 and initially focused on small-cell lung cancers (SCLCs). For SCLCs, we show that GPC2 is also transcriptionally activated by MYCN, expressed at ultrahigh levels on stem cells, is integral in tumor growth and dictates comparable ADC susceptibility. Similar studies in GPC2-expressing pediatric brain tumors are ongoing. Conclusions: Neuroblastomas robustly express GPC2, including ultrahigh levels in the stem cell compartment. The D3-GPC2-PBD ADC targets a conformational and tumor-specific GPC2 epitope and is potently efficacious against a diverse panel of GPC2-expressing cancers, supporting the clinical development of GPC2-directed ADCs. Citation Format: Kristopher R. Bosse, Swetha Raman, Samantha Buongervino, Maria Lane, Kristen Upton, Hong Cui, Benjamin Martinez, Daniel Martinez, Doncho V. Zhelev, Bruce Pawel, Dimiter S. Dimitrov, Jean-Philippe Julien, John M. Maris. Characterization and development of a GPC2 ADC for neuroblastoma and other cancers [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A26.
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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 | 0.001 |
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; 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".