Abstract LB-47: SPEN is a novel candidate tumor suppressor gene that regulates response to tamoxifen in estrogen receptor positive breast cancers.
Bibliographic record
Abstract
Abstract The majority of breast cancers are hormone-responsive and are treated with anti-estrogens, such as tamoxifen. However, most of the 30 and 50% of estrogen receptor positive (ER+) patients that initially respond to tamoxifen eventually become resistant to the drug. Although there are several mechanisms responsible for resistance of breast cancers to tamoxifen, no predictive biomarkers for tamoxifen resistance are in clinical use besides the estrogen receptor (ER) and the progesterone receptors (PR). Using a novel integrative genomic method based on the discovery of nonsense mutations in deleted chromosomal fragments, we identified a nonsense mutation in the SPEN gene in the T47D breast cancer cell line. SPEN is a transcriptional repressor of the estrogen-signaling pathway, which is recruited to estrogen-responsive elements upon activation of the ER. We found 4 somatic mutations (2 nonsense and 2 missense) in 23 breast tumors showing loss of heterozygosity at the SPEN locus. Moreover, tissue microarrays showed that SPEN was frequently over-expressed in the nucleus of normal breast epithelial cells, but in only 10% of breast tumor cells, suggesting that inactivation of SPEN in breast cancer contributes to disease progression. In vitro, overexpression of SPEN in the T47D breast cancer cell line, in which SPEN is mutated and endogenous levels of the protein are very low, resulted in significantly decreased cell proliferation and anchorage-independent growth, decreased PR expression as well as increased sensitivity to tamoxifen. Remarkably, tamoxifen treatment induced 5-fold higher levels of apoptosis in SPEN-overexpressing compared to control T47D cells. In addition, using a tissue microarray of 100 tumor samples from ER+ breast cancer patients treated with tamoxifen only, we found that patients whose tumors express high levels of SPEN had a much better prognosis than patients whose tumors express low or no levels of the protein. To identify transcriptional targets of SPEN besides the PR, we performed gene expression profiling on a panel of breast cancer cell lines in which SPEN was either overexpressed or knocked-down. We found an inverse relationship between SPEN and Apolipoprotein D (APOD) expression, suggesting that SPEN potently repressed transcription of the APOD gene. APOD encodes a glycoprotein from the lipocalin family, which can chelate multiple molecules including progesterone, arachidonic acid as well as tamoxifen itself. Hence, our analysis shows that the loss or mutation of SPEN in ER+ breast cancers has the potential to affect tumor growth as well as sensitivity to tamoxifen, in part through upregulation of APOD expression. Together, our results highlight the role of SPEN as a novel putative tumor suppressor gene in breast cancer and suggest that SPEN is a candidate predictive biomarker of tamoxifen resistance in ER+ breast cancer patients. Citation Format: Stéphanie Légaré, Luca Cavallone, Aline Mamo, Catherine Chabot, Dana Keilty, Anthony Magliocco, Alexander Klimowicz, Patricia Tonin, Mark Basik. SPEN is a novel candidate tumor suppressor gene that regulates response to tamoxifen in estrogen receptor positive breast cancers. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-47. doi:10.1158/1538-7445.AM2013-LB-47
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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) | 0.002 | 0.001 |
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".