IgE-Based Therapeutic Combination Enhances Antitumor Response in Preclinical Models of Pancreatic Cancer
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) represents 3% of all cancer cases and 7% of all cancer deaths in the United States. Late diagnosis and inadequate response to standard chemotherapies contribute to an unfavorable prognosis and an overall 5-year survival rate of less than 10% in PDAC. Despite recent advances in tumor immunology, tumor-induced immunosuppression attenuates the immunotherapy response in PDAC. To date, studies have focused on IgG-based therapeutic strategies in PDAC. With the recent interest in IgE-based therapies in multiple solid tumors, we explored the MUC1-targeted IgE potential against pancreatic cancer. Our study demonstrates the notable expression of FceRI (receptor for IgE antibody) in tumors from PDAC patients. Our study showed that administration of MUC1 targeted-IgE (mouse/human chimeric anti-MUC1.IgE) antibody at intermittent levels in combination with checkpoint inhibitor (anti-PD-L1) and TLR3 agonist (PolyICLC) induces a robust antitumor response that is dependent on NK and CD8 T cells in pancreatic tumor-bearing mice. Subsequently, our study showed that the antigen specificity of the IgE antibody plays a vital role in executing the antitumor response as nonspecific IgE, induced by ovalbumin (OVA), failed to restrict tumor growth in pancreatic tumor-bearing mice. Utilizing the OVA-induced allergic asthma-PDAC model, we demonstrate that allergic phenotype induced by OVA cannot restrain pancreatic tumor growth in orthotopic tumor-bearing mice. Together, our data demonstrate the novel tumor protective benefits of tumor antigen-specific IgE-based therapeutics in a preclinical model of pancreatic cancer, which can open new avenues for future clinical interventions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".