Antagonistic activity of acolbifene, fulvestrant, tamoxifen, and raloxifene on cancer-associated genes in the mouse mammary gland.
Bibliographic record
Abstract
10583 Background: The efficacy and exceptionally good tolerance of estrogen blockade in the treatment of breast cancer is well recognized. Acolbifene (ACOL) is a novel and unique SERM completely free of estrogen-like activity in both the mammary gland and uterus. To better understand the specificity of ACOL, we have investigated its effect on the expression of a set of genes modulated by estradiol (E2) in the mouse mammary gland. Methods: ACOL, tamoxifen (TAM), raloxifene (RALOX) and fulvestrant (FULV) were administered (0.01 mg/mouse; sc) to ovariectomized (OVX) mice or to OVX mice simultaneously treated with E2 (0.05 µg/mouse; single sc injection). Microarray screening followed by Q_RTPCR was used to identify a reproducible set of E2 responsive genes. Results: From 128 genes significantly modulated by E2, 108 genes were up-regulated and 20 were down-regulated. Forty-nine of these genes were associated with tumorigenesis while 22 are known to be associated with breast cancer. This set of 49 genes were used to determine the specificity of ACOL compared to another pure antiestrogen in the mammary gland and uterus, namely FULV, as well as to the mixed estrogen antagonists/agonists TAM and RALOX, in their ability to block the effect of E2. Efficacy of reversal of the effect of E2 was 94%, 63%, 45% and 90% for ACOL, FULV, TAM and RALOX, respectively. The overlap between all treatments was 30.6% (15/49). ACOL reversed the effect of E2 on 42 of the 49 (85.7%) cancer-related genes. Between the genes up-regulated by E2 and reversed by ACOL, seven are considered as prognostic markers in breast cancer, namely Fgfr3, Fos12, Junb, Jdp2, Gdf15, Greb1 and Tgm2. On the other hand, two genes down‑regulated by E2, namely Foxa1 and Fgfr2, were restored by ACOL. Conclusions: Taken together, these data offer new information for a better understanding of the previously demonstrated potent tumoricidal action of ACOL in human breast cancer xenografts. The data also suggest, under the tested conditions, superiority of ACOL over TAM and the other compounds to reverse the effect of E2 on specific gene expression, thus supporting the interest of this new 3rd generation SERM for the hormonal therapy and prevention of breast cancer.
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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 | 0.001 | 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 | 0.000 |
| 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".