The circulating immune cell landscape stratifies metastatic burden in breast cancer patients
Bibliographic record
Abstract
Abstract Advanced breast cancers show varying degrees of metastasis; however, reliable biomarkers of metastatic disease progression remain unknown. In circulation, immune cells are the first line of defence against tumour cells. Herein, using >109,591 peripheral blood mononuclear cells from healthy individuals and breast cancer patients, we tested whether molecular traits of the circulating immune cells, probed with single-cell transcriptomics, can be used to segregate metastatic profiles. Our analyses revealed significant compositional and transcriptional differences in PBMCs of patients with restricted or high metastatic burden versus healthy subjects. The abundance of T cell and monocyte subtypes segregated cancer patients from healthy individuals, while memory and unconventional T cells were enriched in low metastatic burden disease. The cell communication axes were also found to be tightly associated with the extent of metastatic burden. Additionally, we identified a PBMC-derived metastatic gene signature capable of discerning metastatic condition from a healthy state. Our study provides unique molecular insights into the peripheral immune system operating in metastatic breast cancer, revealing potential new biomarkers of the extent of the metastatic state. Tracking such immune traits associated with metastatic spread could complement existing diagnostic tools.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".