Dietary Genistein Reduces Metastasis in a Postsurgical Orthotopic Breast Cancer Model
Bibliographic record
Abstract
Metastatic spread, not primary tumor burden, is the leading cause of breast cancer deaths. For patient prognosis to improve, new systemic adjuvant therapies that are capable of effectively inhibiting the outgrowth of seeded tumor cells after surgical treatment of the primary breast tumor are needed. To facilitate the preclinical development of such therapies, relevant animal models of breast cancer metastasis that can mimic the postsurgical adjuvant setting are required. Here we developed a preclinical xenograft model of breast cancer metastasis where the primary tumor was removed by surgical resection before systemic adjuvant treatment. We used this model to assess the antimetastatic effect of postsurgical dietary intervention with the soy isoflavone genistein. The anticancer activity of genistein has been established in vitro and in vivo, however, few studies have tested the potential of genistein as an antimetastatic therapy. Using our model, we tested the efficacy of adjuvant treatment with genistein to inhibit the outgrowth of metastases postsurgery. To establish primary tumors, human breast carcinoma cells, MDA-MB-435/HAL, were implanted into the mammary fat pad of female nude mice. Primary tumors were left to grow for 5 weeks before being surgically removed. Mice were then randomized into two diet groups: control soy-free diet versus genistein-supplemented diet. Five weeks later, metastatic burden was assessed. Genistein reduced the percent metastatic burden in the lungs by 10-fold. These results indicate that dietary intervention following cancer surgery can affect the outgrowth of seeded tumor cells. The availability of well-characterized, clinically relevant animal models for studying factors that regulate metastatic outgrowth postsurgery will provide an important tool for developing new systemic adjuvant therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".