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Record W4412931265 · doi:10.1038/s44321-025-00282-8

New frontiers in prostate cancer treatment from systemic therapy to targeted therapy

2025· review· en· W4412931265 on OpenAlexafffund
Shaghayegh Nouruzi, Maxim Kobelev, Nakisa Tabrizian, Martin Gleave, Amina Zoubeidi

Bibliographic record

VenueEMBO Molecular Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchTerry Fox Research InstituteProstate Cancer FoundationU.S. Department of Defense
KeywordsProstate cancerTargeted therapyMedicineDiseaseContext (archaeology)Precision medicineAndrogen receptorEpigeneticsBioinformaticsComputational biologyCancerBiologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Significant advances in prostate cancer (PCa) treatment have occurred through the integration of molecular biomarkers and imaging with targeted therapies. While androgen receptor pathway inhibition (ARPI) remains the cornerstone of PCa therapy, the current therapeutic landscape has expanded to include a broader range of targeted agents, alongside emerging approaches that leverage disease-specific vulnerabilities. Molecular profiling has enabled the exploration of diverse therapeutic modalities, including epigenetic regulators, immune-modulating agents, metabolic pathways, kinases, and cell surface proteins. Despite this progress, further research is needed to address tumour heterogeneity and treatment-resistant phenotypes. As ARPI use moves earlier in the disease course and novel agents are incorporated into standard care, prolonging disease control may also reshape emergent resistant phenotypes and disease progression trajectories. This evolving context underscores the need to revisit agents that may now show efficacy in new therapeutic settings or when paired with complementary strategies. Here, we review the current treatment framework in PCa and highlight novel approaches and targets poised to transform clinical care.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.381
Teacher spread0.344 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations5
Published2025
Admission routes2
Has abstractyes

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