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Abstract P4-07-06: MicroRNAs associated with acquired taxane resistance in a breast cancer cell line model

2017· article· en· W2594859485 on OpenAlexaff
Karen J. Taylor, Chong Tm, Alister D’Costa, Ching‐Fa Yao, Cheryl Gourley, David Cameron, JMS Bartlett, Melissa Spears

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsInstitute of Cancer ResearchUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsmicroRNAPaclitaxelTaxaneFold changeBreast cancerDrug resistanceCancer researchBiologyCancerDownregulation and upregulationGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Background: Acquired chemoresistance remains the major cause of therapeutic failure in the treatment of breast cancer. Improved knowledge of the transition from drug sensitive to resistant breast cancer will pave the way for novel preventative and therapeutic strategies. MicroRNAs (miRNA) are endogenous, small non-coding RNAs that regulate gene expression by targeting the 3'UTR region of messenger RNAs. There is a growing body of evidence to suggest miRNAs may be involved in the development of chemoresistance and may play a role in the regulation of drug resistance pathways. Methods: An in vitro model of paclitaxel resistance was developed through the generation of resistant MDA-MB-231 cell lines by serial culture in escalating doses of taxane until resistance was achieved. The chemoresistant model was used to compare differential miRNA expression with the sensitive, parental line using the Nanostring® platform, analysing 800 human miRNAs. Confirmation of differential expression was performed by QRT-PCR. Results: This analysis resulted in 30 significantly altered miRNA (1.5 fold, p value < 0.05) at 25nM paclitaxel and 48 significantly altered miRNA at 50nM paclitaxel. The top up-regulated miRNA cluster in MDA-MB-231 25PACR is hsa-miR-548l (fold change: 2.89, p value: 0.016) and top down-regulated was hsa-miR-449a (fold change: -4.1. p value: 0.001). In MDA-MB-231 50PACR the top up-regulated miRNA cluster is hsa-miR-193a-5p (fold change: 3.746, p value: 0.008) and the top down-regulated miRNA cluster is hsa-miR-135a (fold change: -4.085, p value: 0.001). To explore the molecular mechanisms of the differentially expressed miRNAs in paclitaxel resistance, targets were predicted by in silico analysis. Pathways and networks designated by miRNA targets included the cell cycle, PI3K/Akt pathways and focal adhesion. Conclusion: In this study we identified candidate resistance-associated miRNAs which were differentially expressed between in vitro derived paclitaxel resistant MDA-MB-231 and the sensitive parental line. Further validation to ascertain their role in the transition to a chemoresistant phenotype is currently ongoing. Citation Format: Taylor KJ, Chong T, D'Costa A, Yao C, Gourley C, Cameron DA, Bartlett JMS, Spears M. MicroRNAs associated with acquired taxane resistance in a breast cancer cell line model [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-07-06.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.355
Teacher spread0.313 · 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
Published2017
Admission routes1
Has abstractyes

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