Harnessing innate anti-tumour immunity using a <i>Klebsiella</i>-derived therapeutic to reduce tumour burden and improve outcomes in mouse models of lung cancer
Bibliographic record
Abstract
Abstract Tumour regression and increased survival has been associated with certain acute microbial infections. Immune dysfunction contributes to the development and progression of lung cancer, and therapies that re-constitute anti-tumor immune responses provide an important means to effectively treat malignancies and improve health outcomes. We hypothesized that stimulating the innate immune system with bacterial-derived immunomodulators could induce protective anti-cancer immune responses. A Klebsiella-derived drug product, QBKPN (Qu Biologics), was used to specifically stimulate the innate immune niche in the lungs in established mouse models of lung cancer. Repeated subcutaneous administration with QBKPN significantly reduced lung tumor burden and increased survival. The protective action of QBKPN required prior exposure to Klebsiella through either environmental exposure or lung infection. However, this QBKPN-mediated anti-tumour response was independent of adaptive immunity, as the protective effect remained in RAG2-knockout mice. QBKPN intervention was characterized by a rapid, acute-like systemic immune response, including increased circulatory inflammatory cytokines and innate immune cells, leading to recruitment of immune effector cells into the lung tissue, including macrophages and natural killer (NK) cells. In addition to recruitment of innate immune cells, QBKPN increased markers of classically activated macrophages and increased production of NK cell effector molecules. Together, these data suggest that QBKPN, a Klebsiella-derived immunomodulator, causes activation and recruitment of macrophages and NK cells into the lungs, reducing cancer tumour burden and improving survival outcomes.
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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 | 0.001 |
| 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".