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Record W2475497292 · doi:10.1172/jci.insight.87850

An imaging agent to detect androgen receptor and its active splice variants in prostate cancer

2016· article· en· W2475497292 on OpenAlexafffund
Yusuke Imamura, Amy H. Tien, Jinhe Pan, Jacky K. Leung, Carmen A. Bañuelos, Kunzhong Jian, Jun Wang, Nasrin R. Mawji, F. Fernandez, Kuo‐Shyan Lin, Raymond J. Andersen, Marianne D. Sadar

Bibliographic record

VenueJCI Insight · 2016
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersNational Cancer InstituteBC Cancer AgencyCanada's Michael Smith Genome Sciences Centre
KeywordsProstate cancerAndrogen receptorspliceCancer researchAndrogenProstateInternal medicineMedicineOncologyCancerBiologyHormoneGeneGenetics

Abstract

fetched live from OpenAlex

Constitutively active splice variants of androgen receptor (AR-Vs) lacking ligand-binding domain (LBD) are a mechanism of resistance to androgen receptor LBD–targeted (AR LBD–targeted) therapies for metastatic castration-resistant prostate cancer (CRPC). There is a strong unmet clinical need to identify prostate cancer patients with AR-V–positive lesions to determine whether they will benefit from further AR LBD–targeting therapies or should receive taxanes or investigational drugs like EPI-506 or galeterone. Both EPI-506 (NCT02606123) and galeterone (NCT02438007) are in clinical trials and are proposed to have efficacy against lesions that are positive for AR-Vs. AR activation function-1 (AF-1) is common to the N-terminal domains of full-length AR and AR-Vs. Here, we provide proof of concept for developing imaging compounds that directly bind AR AF-1 to detect both AR-Vs and full-length AR. 123 I-EPI-002 had specific binding to AR AF-1, which enabled direct visualization of CRPC xenografts that express full-length AR and AR-Vs. Our findings highlight the potential of 123 I-EPI-002 as an imaging agent for the detection of full-length AR and AR-Vs in CRPC.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.312
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.335
Teacher spread0.308 · 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 teacher head, 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

Citations23
Published2016
Admission routes2
Has abstractyes

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