ATM Is Required for the Prolactin-Induced HSP90-Mediated Increase in Cellular Viability and Clonogenic Growth After DNA Damage
Bibliographic record
Abstract
Prolactin (PRL) acts as a survival factor for breast cancer cells, but the PRL signaling pathway and the mechanism are unknown. Previously, we identified the master chaperone, heat shock protein 90 (HSP90) α, as a prolactin-Janus kinase 2 (JAK2)-signal transducer and activator of transcription 5 (STAT5) target gene involved in survival, and here we investigated the role of HSP90 in the mechanism of PRL-induced viability in response to DNA damage. The ataxia-telangiectasia mutated kinase (ATM) protein plays a critical role in the cellular response to double-strand DNA damage. We observed that PRL increased viability of breast cancer cells treated with doxorubicin or etoposide. The increase in cellular resistance is specific to the PRL receptor, because the PRL receptor antagonist, Δ1-9-G129R-hPRL, prevented the increase in viability. Two different HSP90 inhibitors, 17-allylamino-17-demethoxygeldanamycin and BIIB021, reduced the PRL-mediated increase in cell viability of doxorubicin-treated cells and led to a decrease in JAK2, ATM, and phosphorylated ATM protein levels. Inhibitors of JAK2 (G6) and ATM (KU55933) abolished the PRL-mediated increase in cell viability of DNA-damaged cells, supporting the involvement of each, as well as the crosstalk of ATM with the PRL pathway in the context of DNA damage. Drug synergism was detected between the ATM inhibitor (KU55933) and doxorubicin and between the HSP90 inhibitor (BIIB021) and doxorubicin. Short interfering RNA directed against ATM prevented the PRL-mediated increase in cell survival in two-dimensional cell culture, three-dimensional collagen gel cultures, and clonogenic cell survival, after doxorubicin treatment. Our results indicate that ATM contributes to the PRL-JAK2-STAT5-HSP90 pathway in mediating cellular resistance to DNA-damaging agents.
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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".