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Record W2495445624 · doi:10.1158/1538-7445.am2016-993

Abstract 993: Identification of estrogen receptor alpha 1 bound lncRNAs in aggressive breast cancer

2016· article· en· W2495445624 on OpenAlexaff
Jessica Silva-Fisher, Abdallah M. Eteleeb, Torsten O. Nielsen, Charles M. Perou, Jorge S. Reis‐Filho, Mathew J. Ellis, Elaine R. Mardis, Christopher A. Maher

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsVancouver Hospital and Health Sciences Centre
Fundersnot available
KeywordsBreast cancerEstrogen receptor alphaEpigeneticsCancerEstrogen receptorTranscriptomeCancer researchBiologyEstrogenBioinformaticsMedicineInternal medicineGeneGene expressionGenetics

Abstract

fetched live from OpenAlex

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. Around 70% of diagnosed BCs are estrogen receptor positive (ER+). Despite the proven benefits of adjuvant endocrine therapy in women with hormone receptor positive breast cancer, relapses still occur even after initial treatment with endocrine therapy for 5 years, referred to as late stage relapse. While existing studies have focused on the role of protein-coding genes, long non-coding RNAs (lncRNAs) are an emerging and under-characterized class of transcripts that have been shown to be dysregulated in breast cancer. Recently, lncRNAs have been shown to function by interfacing with corresponding RNA binding proteins to play critical regulatory roles in chromatin remodeling and diverse cellular processes by acting as decoys, guides, and scaffolds. As estrogen receptor expression is controlled mostly by epigenetic and post-transcriptional mechanisms, and very rarely at the genomic level, we hypothesize that lncRNAs may interact with ER to promote aggressive disease. To address this, we aimed to identify lncRNAs bound to the estrogen receptor alpha 1 (ESR1) that promote late stage breast cancer. To accomplish this, we first used transcriptome sequencing to identify altered expression levels of lncRNAs between primary tumors and late-stage relapse breast cancer patients. We detected 2086 altered lncRNAs when comparing the metastatic to the primary samples with an FDR <0.05, of which 202 were novel. As expected, Gene Set Enrichment Analysis of differentially expressed protein-coding genes revealed an enrichment of biological concepts associated with breast cancer and metastasis. Next, in order to identify ESR1 bound lncRNAs associated with aggressive disease, we conducted RNA Immunoprecipitation Sequencing (RIP-Seq) of all transcripts bound to ESR1 as compared to an IgG control in the ER+ T47D cell line in triplicate. We identified 210 transcripts bound to ESR1, which we term ESR1 bound lncRNAs (ESRlncs). The identified ESRlncs consisted of both novel (unannotated) and known (annotated) lncRNAs. Interestingly we found ∼50% of ESRlncs had significantly altered expression in the metastatic samples, with 96% (105) having more than a two fold increase in expression. Further characterization of these ESRlncs is ongoing to decipher how they interact with ESR1 to promote aggressive and metastatic disease. Overall, this is the first study to discover ESR1 bound lncRNAs that may be contributing to late stage relapse in breast cancer patients. Citation Format: Jessica M. Silva-Fisher, Abdallah M. Eteleeb, Torsten O. Nielsen, Charles M. Perou, Jorge S. Reis-Filho, Mathew J. Ellis, Elaine R. Mardis, Christopher A. Maher. Identification of estrogen receptor alpha 1 bound lncRNAs in aggressive breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 993.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.030
GPT teacher head0.380
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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