Reducing Epitope Spread during Affinity Maturation of an Anti-Ganglioside GD2 Antibody
Bibliographic record
Abstract
Ab affinity maturation in vivo is always accompanied by negative selection to maintain Ag specificity. In contrast, in vitro affinity maturation can lead to epitope spread, resulting in loss of specificity. Anti-ganglioside-GD2 mAbs are clinically effective against neuroblastoma; pain and neuropathy are major side effects. We used structural relatives of GD2 to define epitope spread during in vitro affinity maturation of an anti-GD2 single-chain variable fragment (scFv) called 5F11-scFv. Clonal dominance identified by polyclonal sequencing was confirmed by analyzing individual clones. Affinity-matured mutations were introduced into scFv-streptavidin for functional studies. Without a negative selector, 19-fold affinity improvement (clone Q, where Q is the symbol for glutamine) was associated with strong cross-reactivity with GM2 and GD1b and moderate cross-reactivity with GD3, resulting in positive immunohistochemical staining of all 13 non-neural normal human tissues, in contrast to none of 13 tissues with parental clone P. With GM2 as a negative selector, clone Y (where Y is the symbol for tyrosine) was generated with only weak cross-reactivity with GD1b, adrenal and thyroid glands, and no staining of other non-neural normal tissues. Even though there was only a 3-fold affinity improvement, clone Y showed significantly higher tumor uptake over parental clone P (134%, p = 0.04), whereas clone Q was inferior (54% of clone P; p = 0.05) as confirmed by tumor-to-normal tissue ratios across 16 organs (41% of clone P; p < 0.0001). Using the less efficient negative selector GD3, a clone mixture (Q, V, and Y, where V is the symbol for valine) emerged. We conclude that epitope spread during affinity maturation can be reduced by negative selection. Furthermore, efficiency of the negative selector depends on its cross-reactive affinity with the matured scFv.
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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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".