Recombinant Human Erythropoietin in Combination with Chemotherapy Increases Breast Cancer Metastasis in Preclinical Mouse Models
Bibliographic record
Abstract
PURPOSE: Erythropoiesis-stimulating agents (ESA) are used clinically for treating cancer-related anemia. Recent clinical trials have reported increased adverse events and reduced survival in ESA-treated breast cancer patients receiving chemotherapy, potentially related to erythropoietin (EPO)-induced cancer progression. However, minimal preclinical data are available about the impact of EPO on metastatic cell behavior and/or the metastatic process, and this was the goal of our study. EXPERIMENTAL DESIGN: Breast cancer cell lines were treated with recombinant human EPO (rHuEPO) and screened for expression of EPO receptors (EPOR). MDA-MB-231 and MDA-MB-435 cell lines were used for functional assays in vitro (two-dimensional/three-dimensional growth and survival) and in vivo (tumorigenicity and metastasis), in the presence or absence of EPO and/or cytotoxic agents. RESULTS: A large variation in EPOR expression across cell lines was observed. In vitro, rHuEPO had a protective effect on radiation-treated MDA-MB-435 cells (P < 0.05); however, rHuEPO treatment alone or combined with chemotherapy or hypoxia did not influence cell survival. In vivo, rHuEPO increased lung metastases in immunocompromised mice injected with MDA-MB-231 or MDA-MB-435 cells and treated with chemotherapy relative to mice treated with chemotherapy alone (P < 0.05). CONCLUSIONS: The lack of an in vitro effect of rHuEPO highlights the importance of in vivo studies to delineate the effects of EPO on the metastatic process. These studies may begin to uncover the underlying functional explanation for the observed EPO-related adverse events and decreased survival in ESA-treated metastatic breast cancer patients undergoing chemotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".