Targeting Macrophages as a Novel Therapy to Treat Triple-Negative Breast Cancer: A Literature Review
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a highly aggressive form of cancer which lacks the traditional cellular targets of other types of breast cancer. As such, it is increasingly important to find alternative targets to treat this deadly disease. Recently, tumour-associated macrophages (TAMs) have become an exciting area of focus for cancer research and may provide a source of treatment options for TNBC. Macrophages are an important part of the innate immune response and also play a crucial role in tumour progression, inflammation, and metastasis. TAMs fall along a spectrum and are generally presented as either M1 type or M2 type. M1 macrophages are considered anti-tumorigenic whereas M2 macrophages are considered pro-tumorigenic, promoting tumour growth and inhibiting T-cell response. A search of the University of Alberta’s online library database was carried out with a specific emphasis on clinical research. Search efforts focused on the effects of macrophages on the progression of breast cancer. Further searches were performed to determine the efficacy of targeting macrophages to treat cancer. Increasing the relative ratio of M1 to M2 macrophages or depletion of macrophages may lead to a better prognosis in TNBC. TAMs may be repolarized to M1 phenotype using metformin, inhibition of SerpinE2, YAP/STAT3, or MED1/PPARy. Macrophage recruitment to the tumour microenvironment may be inhibited by targeting chemokines such as CCL2. Current methods could not efficiently deplete macrophages at such a high abundance. The abundance of macrophages and their phagocytic properties could instead be exploited to increase tumour cell phagocytosis by targeting the CD47-SIRPɑ axis. Exciting opportunities have been revealed regarding inhibition of macrophage recruitment, repolarization of M2 to M1 type, and through exploitation of phagocytic properties of macrophages. However, conflicting results can be found for all these emerging treatment strategies. Worsening matters is the lack of knowledge regarding macrophage functions and the confusing landscape of macrophages current naming conventions. As such, further research is required to determine the efficacy of macrophages as a target in breast cancer treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".