Microglia orchestrate tumor suppressive activity and upregulate antigen presentation machinery in the early-stage response to breast cancer brain metastasis.
Bibliographic record
Abstract
Breast cancer brain metastasis (BCBM) is an increasingly prevalent clinical problem, due largely to improvements in treatment of primary tumors. Incidence of BCBM is rapidly fatal and difficult to treat, owing to the relative impermeability of the blood-brain barrier (BBB) to standard-of-care treatments and restrictions on the influx of peripheral immune cells during tumor initiation. As the resident macrophage and predominant immune cell of the central nervous system (CNS), microglia are poised to be the first responders to metastatic tumor infiltration, however an accumulating body of literature implies that microglia facilitate tumor growth in the CNS and brain parenchyma. In the following studies, we employ single-cell RNA sequencing (scRNAseq), spatial immunophenotying and flow cytometry on murine models of BCBM to interrogate the CNS immune microenvironment during tumor initiation to evaluate the tumoricidal potential of microglia and evaluate modalities for treatment of intracranial metastases. We identify the emergence of a pro-inflammatory program in microglia upon incidence of BCBM consistent with the canonical activity of a tumoricidal macrophage. We subsequently demonstrate that microglia upregulate antigen presentation (AP) machinery, and this activity is largely dependent on the infiltration of lymphocytes from the periphery. Finally, we evaluate the impact of agonistic anti-CD40 antibody on AP activity and the capacity to clear BCBM lesions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".