Prolactin and DNA damage trigger an anti-breast cancer cell immune response
Bibliographic record
Abstract
Abstract There are conflicting reports on the role of prolactin (PRL) in breast cancer, and its role within the context of the tumour microenvironment is not well understood. In our previous study, we demonstrated a cross-talk between the ataxia telangiectasia-mutated (ATM) DNA damage response pathway and the PRL-Janus-kinase-2 (JAK2)-signal transducer and activator of transcription-5 (STAT5)-heat shock protein-90 (HSP90) pathway. To investigate the role of PRL in tumour initiation and the effect of DNA damage in vivo, we used a model of breast cancer initiation that assesses the ability of breast cancer cells to initiate orthotopic xenograft tumour formation after DNA damage. Breast cancer cells engineered to secrete human PRL or the control cells, were treated with the DNA damaging agent doxorubicin or vehicle and injected into the mammary fat pad of immune-deficient SCID mice. PRL secretion from human breast cancer cells did not change the tumour latency compared to controls, although combined doxorubicin and PRL treatment increased tumour latency. Depletion of glycolipid asialo ganglioside-GM1 positive immune cells using anti-asialo GM1 antibody resulted in faster tumour formation only in the PRL-secreting breast cancer cells that were pre-treated with doxorubicin, and not in the PRL-only or empty vector controls. Additionally, doxorubicin plus PRL treatment of breast cancer cells were shown in vitro to attract cytotoxic NK cells compared to controls, and that this was dependent on the PRLR. These results may shed light on the conflicting reports of PRL in breast cancer and demonstrate that combined breast cancer cell DNA damage and PRL exposure results in anti-tumour activity of asialo-GM1-positive immune cells.
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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 | 0.001 |
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".