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Record W2976445677 · doi:10.1200/po.19.00123

Plasma Androgen Receptor Copy Number Status at Emergence of Metastatic Castration-Resistant Prostate Cancer: A Pooled Multicohort Analysis

2019· article· en· W2976445677 on OpenAlexafffund
Anuradha Jayaram, Anna Wingate, Daniel Wetterskog, Vincenza Conteduca, Daniel Khalaf, Mansour T. A. Sharabiani, Fabio Calabrò, Lorraine Barwell, Susan Feyerabend, Enrique Grande, A. Martínez-Carrasco, Albert Font, Alfredo Berruti, Cora N. Sternberg, Robert J. Jones, Florence Lefresne, Marjolein Lahaye, Shibu Thomas, Shilpy Joshi, Dong Shen, Deborah Ricci, Michael Gormley, Axel S. Merseburger, Bertrand Tombal, Matti Annala, Kim N., Ugo De Giorgi, Enrique González‐Billalabeitia, Alexander W. Wyatt, Gerhardt Attard

Bibliographic record

VenueJCO Precision Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchAstellas PharmaProstate Cancer CanadaEmil Aaltosen SäätiöProstate Cancer FoundationJane ja Aatos Erkon SäätiöDOD Prostate Cancer Research ProgramMedical Research CouncilMovember Foundation
KeywordsProstate cancerAndrogen receptorOncologyCastrationInternal medicineMedicineProstateBiologyCancerCancer researchHormone

Abstract

fetched live from OpenAlex

PURPOSE Increases in androgen receptor ( AR) copy number (CN) can be detected in plasma DNA when patients develop metastatic castration-resistant prostate cancer. We aim to evaluate the association between AR CN as a continuous variable and clinical outcome. PATIENTS AND METHODS PCR2023 was an international, multi-institution, open-label, phase II study of abiraterone acetate plus prednisolone (AAP) or abiraterone acetate plus dexamethasone that included plasma AR assessment as a predefined exploratory secondary end point. Plasma AR CN data (ClinicalTrials.gov identifier: NCT01867710 ) from this study (n = 133) were pooled with data from the following three other cohorts: cohort A, which was treated with either AAP or enzalutamide (n = 73); the PREMIERE trial (ClinicalTrials.gov identifier: NCT02288936 ) of biomarkers for enzalutamide (n = 94); and a phase II trial from British Columbia (ClinicalTrials.gov identifier: NCT02125357 ) that randomly assigned men to either AAP or enzalutamide (n = 201). The primary outcome measures for the biomarker analysis were overall survival and progression-free survival. RESULTS Using multivariable fractional polynomials analysis using Cox regression models, a nonlinear relationship between plasma AR CN and outcome was identified for overall survival, where initially for small incremental gains in CN there was a large added hazard ratio that plateaued at higher CN. The CN cut point associated with the highest local hazard ratio was 1.92. A similar nonlinear association was observed with progression-free survival. In an exploratory analysis of PCR2023, the time from start of long-term androgen-deprivation therapy to start of AAP or abiraterone acetate plus dexamethasone was significantly shorter in patients with plasma AR CN of 1.92 or greater than patients with plasma AR CN of less than 1.92 (43 v 130 weeks, respectively; P = .005). This was confirmed in cohort A ( P = .003), the PREMIERE cohort ( P = .03), and the British Colombia cohort ( P = .003). CONCLUSION Patients with metastatic castration-resistant prostate cancer can be dichotomized by a plasma AR CN cut point of 1.92. Plasma AR CN value of 1.92 or greater identifies aggressive disease that is poorly responsive to AR targeting and is associated with a prior short response to primary androgen-deprivation therapy.

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)0.011
metaresearch head score (Gemma)0.011
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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.382
Teacher spread0.352 · 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

Citations34
Published2019
Admission routes2
Has abstractyes

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