The Estrogen Receptor Cofactor SPEN Functions as a Tumor Suppressor and Candidate Biomarker of Drug Responsiveness in Hormone-Dependent Breast Cancers
Bibliographic record
Abstract
The treatment of breast cancer has benefitted tremendously from the generation of estrogen receptor-α (ERα)-targeted therapies, but disease relapse continues to pose a challenge due to intrinsic or acquired drug resistance. In an effort to delineate potential predictive biomarkers of therapy responsiveness, multiple groups have identified several uncharacterized cofactors and interacting partners of ERα, including Split Ends (SPEN), a transcriptional corepressor. Here, we demonstrate a role for SPEN in ERα-expressing breast cancers. SPEN nonsense mutations were detectable in the ERα-expressing breast cancer cell line T47D and corresponded to undetectable protein levels. Further analysis of 101 primary breast tumors revealed that 23% displayed loss of heterozygosity at the SPEN locus and that 3% to 4% harbored somatically acquired mutations. A combination of in vitro and in vivo functional assays with microarray-based pathway analyses showed that SPEN functions as a tumor suppressor to regulate cell proliferation, tumor growth, and survival. We also found that SPEN binds ERα in a ligand-independent manner and negatively regulates the transcription of ERα targets. Moreover, we demonstrate that SPEN overexpression sensitizes hormone receptor-positive breast cancer cells to the apoptotic effects of tamoxifen, but has no effect on responsiveness to fulvestrant. Consistent with these findings, two independent datasets revealed that high SPEN protein and RNA expression in ERα-positive breast tumors predicted favorable outcome in patients treated with tamoxifen alone. Together, our data suggest that SPEN is a novel tumor-suppressor gene that may be clinically useful as a predictive biomarker of tamoxifen response in ERα-positive breast cancers.
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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 | 0.000 |
| 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) | 0.000 | 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".