Immune Effector Functions of Human IgG2 Antibodies against EGFR
Bibliographic record
Abstract
Three FDA-approved epidermal growth factor receptor (EGFR) antibodies (cetuximab, panitumumab, necitumumab) are clinically available to treat patients with different types of cancers. Interestingly, panitumumab is of human IgG2 isotype, which is often considered to have limited immune effector functions. Unexpectedly, our studies unraveled that human IgG2 antibodies against EGFR mediated effective CDC when combined with another noncross-blocking EGFR antibody. This second antibody could be of human IgG1 or IgG2 isotype. Furthermore, EGFR antibodies of human IgG2 isotype were highly potent in recruiting myeloid effector cells such as M1 macrophages and PMN for tumor cell killing by ADCC. Tumor cell killing by PMN was more effective with IgG2 than with IgG1 antibodies if tumor cells expressed lower levels of EGFR. Additionally, lower expression levels of the "don't eat me" molecule CD47 on tumor cells enabled ADCC also by M2 macrophages, and improved PMN and macrophage-mediated ADCC. A TCGA enquiry revealed broadly varying CD47 expression levels across different solid tumor types. Together, these results demonstrate that human IgG2 antibodies against EGFR can promote significant Fc-mediated effector functions, which may contribute to their clinical efficacy. The future challenge will be to identify clinical situations in which myeloid effector cells can optimally contribute to antibody efficacy.
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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".