PolyI:C and CpG Synergize with Anti-ErbB2 mAb for Treatment of Breast Tumors Resistant to Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Abstract Innate and adaptive immune cells play an important role in the therapeutic activity of anti-ErbB2 mAbs, such as trastuzumab. In the clinic, breast tumors poorly infiltrated with immune cells are more resistant to trastuzumab, and patients have a worse prognosis. Because type I and II IFNs are critical to the immune-mediated activity of anti-ErbB2 mAb, we investigated the effect of combining polyI:C and CpG with trastuzumab-like therapy in immunocompetent mouse models of ErbB2+ breast cancer. We demonstrated that in situ delivery of polyI:C and CpG combined to systemic anti-ErbB2 mAb triggered a potent inflammatory response in breast tumors able to induce long-lasting CD8+ T cell–dependent antitumor immunity. Remarkably, polyI:C and CpG was superior to combined PD-1/CTLA-4 blockade in sensitizing tumors to anti-ErbB2 mAb therapy. Local injection of CpG and polyI:C in a primary tumor significantly enhanced the activity of systemic anti-ErbB2 mAb against a distant untreated tumor. Type I and II IFNs, as well as natural killer cells and CD8+ T cells, were indispensible to the synergistic activity of the combination treatment. Because synthetic RNA analogues and CpG oligodeoxynucleotides have been safely used in clinical trials, our study supports combination treatments with anti-ErbB2 mAbs. Cancer Res; 77(2); 312–9. ©2016 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".