Harnessing innate lung anti-cancer effector functions with a novel bacterial-derived immunotherapy
Bibliographic record
Abstract
Acute infection is known to induce strong anti-tumor immune responses, but clinical translation has been hindered by the lack of an effective strategy to safely and consistently provoke a therapeutic response. These limitations are overcome with a novel treatment approach involving repeated subcutaneous delivery of a Klebsiella-derived investigational immunotherapeutic, QBKPN. In preclinical models of lung cancer, QBKPN administration consistently showed anti-cancer efficacy, which was dependent on Klebsiella pre-exposure, but was independent of adaptive immunity. Rather, QBKPN induced anti-tumor innate immunity that required NK cells and NKG2D engagement. QBKPN increased NK cells and macrophages in the lungs, altered macrophage polarization, and augmented the production of cytotoxic molecules. An exploratory trial in patients with non-small cell lung cancer demonstrated QBKPN was well tolerated, safe, and induced peripheral immune changes suggestive of macrophage polarization and reduction of PD-1 and PD-L1 expression on leukocytes. These data demonstrate preclinical efficacy, and clinical safety and tolerability, for this cancer immunotherapy strategy that exploits innate anti-tumor immune mechanisms.
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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 | 0.001 |
| 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".