Myeloid cells in circulation and tumor microenvironment of breast cancer patients
Bibliographic record
Abstract
Pathological conditions including cancers lead to accumulation of a morphological mixture of highly immunosuppressive cells termed as myeloid-derived suppressor cells (MDSC). The lack of conclusive markers to identify human MDSC, due to their heterogeneous nature and close phenotypical and functional proximity with other cell subsets, made it challenging to identify these cells. Nevertheless, expansion of MDSC has been reported in periphery and tumor microenvironment of various cancers. The majority of studies on breast cancers were performed on murine models and hence limited literature is available on the relation of MDSC accumulation with clinical settings in breast cancer patients. The aim of this study was to investigate levels and phenotypes of myeloid cells in peripheral blood (n = 23) and tumor microenvironment of primary breast cancer patients (n = 7), compared with blood from healthy donors (n = 21) and paired non-tumor normal breast tissues from the same patients (n = 7). Using multicolor flow cytometric assays, we found that breast cancer patients had significantly higher levels of tumor-infiltrating myeloid cells, which comprised of granulocytes (P = 0.022) and immature cells that lack the expression of markers for fully differentiated monocytes or granulocytes (P = 0.016). Importantly, this expansion was not reflected in the peripheral blood. The immunosuppressive potential of these cells was confirmed by expression of Arginase 1 (ARG1), which is pivotal for T-cell suppression. These findings are important for developing therapeutic modalities to target mechanisms employed by immunosuppressive cells that generate an immune-permissive environment for the progression of cancer.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".