Anti-Folate Receptor-α IgE but not IgG Recruits Macrophages to Attack Tumors via TNFα/MCP-1 Signaling
Bibliographic record
Abstract
Abstract IgE antibodies are key mediators of antiparasitic immune responses, but their potential for cancer treatment via antibody-dependent cell-mediated cytotoxicity (ADCC) has been little studied. Recently, tumor antigen–specific IgEs were reported to restrict cancer cell growth by engaging high-affinity Fc receptors on monocytes and macrophages; however, the underlying therapeutic mechanisms were undefined and in vivo proof of concept was limited. Here, an immunocompetent rat model was designed to recapitulate the human IgE-Fcϵ receptor system for cancer studies. We also generated rat IgE and IgG mAbs specific for the folate receptor (FRα), which is expressed widely on human ovarian tumors, along with a syngeneic rat tumor model expressing human FRα. Compared with IgG, anti-FRα IgE reduced lung metastases. This effect was associated with increased intratumoral infiltration by TNFα+ and CD80+ macrophages plus elevated TNFα and the macrophage chemoattractant MCP-1 in lung bronchoalveolar lavage fluid. Increased levels of TNFα and MCP-1 correlated with IgE-mediated tumor cytotoxicity by human monocytes and with longer patient survival in clinical specimens of ovarian cancer. Monocytes responded to IgE but not IgG exposure by upregulating TNFα, which in turn induced MCP-1 production by monocytes and tumor cells to promote a monocyte chemotactic response. Conversely, blocking TNFα receptor signaling abrogated induction of MCP-1, implicating it in the antitumor effects of IgE. Overall, these findings show how antitumor IgE reprograms monocytes and macrophages in the tumor microenvironment, encouraging the clinical use of IgE antibody technology to attack cancer beyond the present exclusive reliance on IgG. Cancer Res; 77(5); 1127–41. ©2017 AACR.
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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".