Activating Transcription Factor 3 as a Novel Regulator of Chemotherapy Response in Breast Cancer
Bibliographic record
Abstract
Anthracyclines, such as doxorubicin, are used as first-line chemotherapeutics, usually in combination therapies, for the treatment of advanced breast cancer. While these drugs have been successful therapeutic options, their use is limited due to serious drug related toxicities and acquired tumor resistance. Uncovering the molecular mechanisms that mediate doxorubicin's cytotoxic effect will lead to the identification of novel more efficacious combination therapies and allow for reduced doses of doxorubicin to be administered while maintaining efficacy. In our study, we demonstrate that activating transcription factor (ATF) 3 expression was upregulated by doxorubicin treatment in a representative panel of human breast cancer cell lines MCF7 and MDA-MB-231. We have also shown that doxorubicin treatment can induce ATF3 expression in ex vivo human breast and ovarian tumor samples. The upregulation of ATF3 in the cell lines was regulated by multiple cellular mechanisms including the activation of JNK and ATM signaling pathways. Importantly, loss of ATF3 expression resulted in reduced sensitivity to doxorubicin treatment in mouse embryonic fibroblasts. Through a 1200 FDA-approved compound library screen, we identified a number of agents whose cytotoxicity is dependent on ATF3 expression that also enhanced doxorubicin induced cytotoxicity. For example, the combination of the HDAC inhibitor vorinostat or the nucleoside analogue trifluridine could synergistically enhance doxorubicin cytotoxicity in the MCF7 cell line. Synergy in cell lines with the combination of ATF3 inducers and patients with elevated basal levels of ATF3 shows enhanced response to chemotherapy. Taken together, our results demonstrate a role for ATF3 in mediating doxorubicin cytotoxicity and provide rationale for the combination of ATF3-inducing agents with doxorubicin as a novel therapeutic approach.
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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".