Engineering the Human Fc Region Enables Direct Cell Killing by Cancer Glycan–Targeting Antibodies without the Need for Immune Effector Cells or Complement
Bibliographic record
Abstract
Abstract Murine IgG3 glycan-targeting mAb often induces direct cell killing in the absence of immune effector cells or complement via a proinflammatory mechanism resembling oncotic necrosis. This cancer cell killing is due to noncovalent association between Fc regions of neighboring antibodies, resulting in enhanced avidity. Human isotypes do not contain the residues underlying this cooperative binding mode; consequently, the direct cell killing of mouse IgG3 mAb is lost upon chimerization or humanization. Using the Lewisa/c/x -targeting 88mAb, we identified the murine IgG3 residues underlying the direct cell killing and increased avidity via a series of constant region shuffling and subdomain swapping approaches to create improved (“i”) chimeric mAb with enhanced tumor killing in vitro and in vivo. Constant region shuffling identified a major CH3 and a minor CH2 contribution, which was further mapped to discontinuous regions among residues 286–306 and 339–378 that, when introduced in 88hIgG1, recapitulated the direct cell killing and avidity of 88mIgG3. Of greater interest was the creation of a sialyl-di-Lewisa–targeting i129G1 mAb via introduction of these selected residues into 129hIgG1, converting it into a direct cell killing mAb with enhanced avidity and significant in vivo tumor control. The human iG1 mAb, termed Avidimabs, retained effector functions, paving the way for the proinflammatory direct cell killing to promote antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity through relief of immunosuppression. Ultimately, Fc engineering of human glycan-targeting IgG1 mAb confers proinflammatory direct cell killing and enhanced avidity, an approach that could be used to improve the avidity of other mAb with therapeutic potential. Significance: Fc engineering enhances avidity and direct cell killing of cancer-targeting anti-glycan antibodies to create superior clinical candidates for cancer immunotherapy.
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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".