Activating a collaborative innate-adaptive immune response to control breast and ovarian cancer metastasis
Bibliographic record
Abstract
Abstract Many cancers recruit monocytes/macrophages and polarize them into tumor-associated macrophages (TAMs). TAMs promote tumor growth and metastasis and inhibit cytotoxic T cells. Yet, macrophages can also kill cancer cells after polarization by e.g. , lipopolysaccharide (LPS, a bacteria-derived toll-like receptor 4 [TLR4] agonist) and interferon gamma (IFNγ). They do so via nitric oxide (NO), generated by inducible NO synthase (iNOS). Altering the polarization of macrophages could therefore be a strategy for controlling cancer. Here, we show that monophosphoryl lipid A (MPLA, a derivative of LPS) with IFNγ activated macrophages isolated from metastatic pleural effusions of breast cancer patients to kill the corresponding patients’ cancer cells in vitro . Importantly, intratumoral injection of MPLA with IFNγ not only controlled local tumor growth but also reduced metastasis in mouse models of luminal and triple negative breast cancers. Furthermore, intraperitoneal administration of MPLA with IFNγ reprogrammed peritoneal macrophages, suppressed metastasis, and enhanced the response to chemotherapy in the ID8-p53 −/− ovarian carcinoma mouse model. The combined MPLA+IFNγ treatment reprogrammed the immunosuppressive microenvironment to be immunostimulatory by recruiting leukocytes, stimulating type I interferon signaling, decreasing tumor-associated (CD206 + ) macrophages, increasing tumoricidal (iNOS + ) macrophages, and activating cytotoxic T cells through macrophage-secreted interleukin 12 (IL-12) and tumor necrosis factor α (TNFα). Both macrophages and T cells were critical for the anti-metastatic effects of MPLA+IFNγ. MPLA and IFNγ are already used individually in clinical practice, so our strategy to engage the anti-tumor immune response, which requires no knowledge of unique tumor antigens, may be ready for near-future clinical testing.
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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".