Abstract 2547: Discovery and characterization of late-stage breast cancer estrogen receptor alpha 1 bound long non-coding RNAs
Bibliographic record
Abstract
Abstract Breast cancer (BC) is the second most common newly diagnosed cancer and the second leading cause of cancer death among women in the United States. Despite the proven benefits of adjuvant endocrine therapy in women with hormone receptor positive BC, relapses still occur even after initial treatment with endocrine therapy for 5 years, referred to as late-stage relapse. Long non-coding RNAs (lncRNAs) have been shown to be dysregulated in breast cancer. Recent studies have also shown lncRNAs to function by interfacing with corresponding RNA binding proteins to play critical regulatory roles of diverse cellular processes. Therefore, we hypothesize that lncRNAs may interact with ER to regulate genes promoting late-stage relapse. To address this, we aimed to identify lncRNAs bound to the estrogen receptor alpha 1 protein (ESR1) that promote late-stage relapse breast cancer. We first used transcriptome sequencing to identify altered expression levels of lncRNAs between 72 primary tumors and 24 late-stage relapse breast cancer patients. We detected 1192 altered lncRNAs when comparing the metastatic to the primary samples (FDR <0.05). Next, to identify ESR1 bound lncRNAs associated with late-stage BC, we conducted RNA Immunoprecipitation Sequencing of all transcripts bound to ESR1 as compared to an IgG control in the ER+ T47D cell line. We identified 217 lncRNAs bound to ESR1 of which 50 were up-regulated in late-stage BC, termed Late-Stage Relapse BC ESR1-bound lncRNAs (LASERs). Next, we focused on characterizing the most up-regulated differentially expressed lncRNA, LASER1. We found that LASER1 has increased expression in ER+ breast cancer cell lines. Further, elevated expression of LASER1 was also detected in MCF7 long-term estrogen deprived cells lines that have amplified ER+, suggesting an ER dependent mechanism. Preliminary functional studies indicate LASER1 to promote proliferation and invasion in BC cell lines. Ongoing studies will decipher how LASERs interact with ESR1 to promote late-stage relapse. Overall, this is the first study to discover ESR1 bound lncRNAs that may be contributing to late-stage relapse in BC patients. Citation Format: Jessica Monique Silva-Fisher, Abdallah M. Eteleeb, Torsten Nielsen, Charles M. Perou, Jorge S. Reis-Filho, Mathew J. Ellis, Elaine R. Mardis, Christopher A. Maher. Discovery and characterization of late-stage breast cancer estrogen receptor alpha 1 bound long non-coding RNAs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2547. doi:10.1158/1538-7445.AM2017-2547
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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) | 0.001 | 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".