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Record W2886013149 · doi:10.1158/1538-7445.am2018-5468

Abstract 5468: Neratinib significantly inhibits responses to androgen in human prostate cancer cells

2018· article· en· W2886013149 on OpenAlexaboutno aff
D. Alwyn Dart, Alshad S. Lalani, Francesca Avogadri-Connors, Richard Bryce, Wen G. Jiang

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsProstate cancerAndrogen receptorLNCaPCancer researchNeratinibAndrogenBicalutamideSignal transductionEndocrinologyInternal medicineMedicineBiologyCancerBreast cancerCell biologyHormoneTrastuzumab

Abstract

fetched live from OpenAlex

Abstract Background: Prostate cancer (PCa) is the most commonly diagnosed male cancer in the Western world. Tumor growth is initially androgen dependent - and driven by the androgen receptor (AR). The mainstays of prostate cancer treatment are androgen ablation and antiandrogen treatment, which block AR signalling. However, PCa often relapses to an androgen- independent disease. Androgens can transactivate genes directly via the AR-mediated transcription factor and indirectly via less well understood signal transduction pathways. These signal transduction pathways become increasingly relevant as prostate cancer cells progress to anti-androgen resistance, with HER2 being associated with higher relapse rates. Neratinib is an orally available tyrosine kinase inhibitor that irreversibly binds and inhibits EGFR, HER2 and HER4 receptor tyrosine kinases. This study aimed to examine the effect of neratinib on androgen signalling and on the expression of androgen-regulated genes in prostate cancer cells. Methods: Changes in protein phosphorylation after androgen treatment of hormonally starved prostate cancer cells (LNCaP) was assessed using protein microarrays (Kinexus, Canada). Changes in gene expression after neratinib or androgen treatment were ascertained using AmpliSseq® technology or standard qPCR, and analysed via IPA Ingenuity software. Results: Androgen treatment of hormonally starved prostate cancer cells (LNCaP) caused phosphorylation of several members of the signal transduction cascade including HER2 and Src within 2 hours, indicating a role for HER2 in rapid androgen signalling. Additionally, from over 1000 genes upregulated by androgen treatment (>2fold within 2 hours), 87% showed downregulation with neratinib treatment. Ingenuity pathway analysis indicated that STAT3, ETS-family and NF-κB transcription factors may be responsible for the rapid androgen-induced gene upregulation observed, and that these pathways were inhibited by neratinib treatment. Q-Quantitative PCR analysis of PSA expression in LNCaP cells stimulated with androgen in the presence of increasing concentrations of neratinib resulted in a dose-dependent inhibition of androgen activity. Conclusions: These results show that neratinib is able to inhibit the responses of prostate cancer cells to androgens, and that a strong potential signaling cross-talk exists between the androgen receptor and the certain signal transduction pathways - pathways known to be involved in the progression of androgen-independent prostate cancer. Citation Format: Dafydd A. Dart, Alshad S. Lalani, Francesca Avogadri-Connors, Richard Bryce, Wen G. Jiang. Neratinib significantly inhibits responses to androgen in human prostate cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5468.

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.056
GPT teacher head0.404
Teacher spread0.349 · 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
Published2018
Admission routes1
Has abstractyes

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