Immune Profile of Exosomes in African American Breast Cancer Patients Is Mediated by Kaiso/THBS1/CD47 Signaling
Bibliographic record
Abstract
African American (AA) women with breast cancer are more likely to have higher inflammation and a stronger overall immune response, which correlate with poorer outcomes. In this report, we applied the nanostring immune panel to identify differences in inflammatory and immune gene expression by race. We observed a higher expression of multiple cytokines in AA patients compared to EA patients, with high expression of CD47, TGFB1, and NFKB1 associated with the transcriptional repressor Kaiso. To investigate the mechanism associated with this expression pattern, we observed that Kaiso depletion results in decreased expression of CD47, and its ligand SIRPA. Furthermore, Kaiso appears to directly bind to the methylated sequences of the THBS1 promotor and repress gene expression. Similarly, Kaiso depletion attenuated tumor formation in athymic nude mice, and these Kaiso-depleted xenograft tissues showed significantly higher phagocytosis and increased infiltration of M1 macrophages. In vitro validation using MCF7 and THP1 macrophages treated with Kaiso-depleted exosomes showed a reduced expression of immune-related markers (CD47 and SIRPA) and macrophage polarization towards the M1 phenotype compared to MCF7 cells treated with exosomes isolated from high-Kaiso cells. Lastly, analysis of TCGA breast cancer patient data demonstrates that this gene signature is most prominent in the basal-like subtype, which is more frequently observed in AA breast cancer patients.
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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) | 0.001 | 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".