MétaCan
Menu
← Back to cohort
Record W4221085387 · doi:10.1101/2022.03.10.22271799

Assessment of Androgen Receptor splice variant-7 as a biomarker of clinical response in castration-sensitive prostate cancer

2022· preprint· en· W4221085387 on OpenAlexfundno aff
Adam G. Sowalsky, Ines Figueiredo, Rosina T. Lis, Ilsa Coleman, Bora Gürel, Denisa Bogdan, Wei Yuan, Joshua W. Russo, John R. Bright, Nichelle C. Whitlock, Shana Y. Trostel, Anson T. Ku, Radhika A. Patel, Lawrence D. True, Jonathan Welti, Juan M. Jiménez‐Vacas, Daniel Nava Rodrigues, Ruth Riisnaes, Antje Neeb, Cynthia T. Sprenger, Amanda Swain, Scott Wilkinson, Fatima Karzai, William L. Dahut, Steven P. Balk, Eva Corey, Peter S. Nelson, Michael C. Haffner, Stephen R. Plymate, Johann S. de Bono, Adam Sharp

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
FundersFoghorn TherapeuticsGilead SciencesNational Cancer InstituteGenentechJanssen Research and DevelopmentNational Institutes of HealthSierra OncologyV Foundation for Cancer ResearchAstellas PharmaEisaiProstate Cancer UKNational Institute for Health and Care ResearchMacroGenicsDOD Prostate Cancer Research ProgramVertex PharmaceuticalsBristol-Myers SquibbAstraZenecaMovember FoundationSanofiPfizerWellcome TrustCancer Research UKDoris Duke Charitable FoundationU.S. Department of Veterans Affairs
KeywordsProstate cancerAndrogen receptorBiomarkerAndrogen deprivation therapyImmunohistochemistryOncologyMedicineCancer researchProstatespliceInternal medicineAndrogenMessenger RNACancerBiologyHormoneGene

Abstract

fetched live from OpenAlex

Abstract Background Therapies targeting the androgen receptor (AR) have improved the outcome for patients with castration-sensitive prostate cancer (CSPC). Expression of the constitutively active AR splice variant-7 (AR-V7) has shown clinical utility as a predictive biomarker of AR-targeted therapy resistance in castration-resistant prostate cancer (CRPC), but its importance as predictive biomarker in CSPC remains understudied. Methods We explored multiple approaches to quantify AR-V7 mRNA and protein in prostate cancer cell lines and patient-derived xenograft (PDX) models, in both publicly available and independent institutional clinical cohorts, to identify reliable approaches for detecting AR-V7 mRNA and protein, and its association with clinical outcome. Results In publicly available benign prostate, CSPC and CRPC cohorts, AR-V7 mRNA was much less abundant when detected using reads across splice boundaries than when considering isoform-specific exonic reads. The RM7 AR-V7 antibody had increased sensitivity and specificity for AR-V7 protein detection by immunohistochemistry (IHC) in CRPC cohorts and identified AR-V7 protein reactivity very rarely in CSPC cohorts, when compared to the EPR15656 AR-V7 antibody. Using multiple CRPC PDX models, we demonstrated that AR-V7 expression was exquisitely sensitive to hormonal manipulation. In CSPC cohorts, AR-V7 protein quantification by either assay did not associate with time to development of castration-resistance or overall survival, and intense neoadjuvant androgen-deprivation therapy did not lead to significant detectable AR-V7 mRNA or staining following treatment, and neither pre- nor post-treatment AR-V7 levels associated with the volume of residual disease after therapy. Conclusion This study demonstrates that further analytical validation and clinical qualification is required before AR-V7 can be considered for clinical use in CSPC as a predictive biomarker.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.459
Teacher spread0.373 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuemedRxiv→Same topicProstate Cancer Treatment and Research→French-language works237,207→