The Immunomodulatory Role of Gemcitabine in Triple Negative Breast Cancer
Bibliographic record
Abstract
Triple negative breast cancer (TNBC), defined for its lack of expression/amplification of three major receptors, makes up ~15% of all BC cases but a majority of all BC deaths. TNBC has been found to be the most immune-rich among BC subtypes, and progress has been made in the development of immunotherapies; however, not all patients are eligible, and response can be limited. Therefore, there is a significant clinical need to enhance the response to these treatments. Given chemotherapy is the core component of TNBC treatment, and is given in combination with immunotherapy, its potential immunomodulatory impact warrants exploration. Gemcitabine, currently used for the treatment of metastatic TNBC, has been reported to have potential immunomodulatory properties that create a more immune-favourable TME for combination with immunotherapies and/or improved outcome. We therefore investigated the use of gemcitabine as an immunomodulator in a primary 4T1 TNBC mouse model. Gemcitabine was able to reduce pro-tumour immune cells including macrophages and MDSCs while increasing T-cell abundance, therefore resulting in a less immunosuppressive TME. We demonstrated that this immune response was both temporal and dose-dependent, which has impact for planning and scheduling combination treatments. In conclusion, we have demonstrated that gemcitabine modulates the TME in ways that could not only enhance the direct anti-tumour effects of gemcitabine itself but also potentially enhance responsiveness to immunotherapy. This work has laid the foundation for further studies investigating combination therapy for the treatment of TNBC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".