Bibliographic record
Abstract
Effective therapies typically target diseased tissues without significantly affecting healthy tissues. A tumor-related glycosphingolipid (GSL), such as ganglioside GD2, distinguishes neuroectoderm tumors from their healthy counterparts and is a validated tumor target. GD2 is clinically targeted for diagnosis and immunotherapy. GD2 plays an important functional role in tumor progression and in chemoresistance. It also plays an important functional role in pain; however, the mechanisms that make GD2 important in such phenomena remain unknown.Thus, understanding the structure-activity relationship of GD2, a GSL with two sialic acids, would be helpful. However, such studies on glycolipids are challenging. We employed a chemical biology approach to elucidate the structure and function of GD2 and to further direct the rational design of GD2 ligands and vaccines for GD2-related cancer treatment.The combined use of STD NMR spectroscopy, transferred NOE experiments, and molecular modeling furnished details on the molecular recognition of the ganglioside GD2 by the clinically used anti-GD2 monoclonal antibody 3F8 that can induce apoptosis of GD2 expressing tumours. A binding model that provided the basis for a rational development of GD2 ligands and vaccines was then established. Based on the structural information of GD2-3F8 interactions, small molecule monomeric peptide ligands binding to GD2 were developed. ELISA and NMR experiments demonstrated that peptides selectively bound to GD2 via an induced-fit mechanism. Furthermore, peptidic GD2 ligands, including 3F8, mediated similar biological functions in cell-based assays of activation of NMDA receptor via Src family kinase, calcium fluxe and cAMP. These can explain at least some of the mechanisms associated with tumor progression and pain, where GD2 plays a role. However, current GD2 peptide ligands did not demonstrate any treatment effect in vivo. Hence, we turned to the design of GD2 vaccines as a therapeutic approach. The rigid nature of GD2-oligosaccharides, discovered in our structural study, makes it perfectly suited to drive a structurally convergent immune response. A novel and homogenous tetra-GD2 dendrimer was designed to mimic a clustered GD2 lipid raft. Immunization of mice with tetra-GD2 dendrimer elicited a potent anti-GD2 humoral response. The antibodies (sera or mAbs) thus generated can kill GD2-expressing cells in culture, in the absence of a complement. Tumor growth was significantly delayed in vivo in prophylactic and in therapeutic paradigms. Our research strategy may be expanded to other clinically relevant glycolipids.
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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".