ArticleNature communications2023
Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- Immunoediting and precision biomarkers in prostate cancer: The emerging role of liquid biopsy, immune contexture and HLA genotype (Review).International journal of oncology · 2026Review
- Multi-modal circulating cell-free DNA profiling to predict response to docetaxel in metastatic castration-resistant prostate cancer.NPJ precision oncology · 2026Article
- Cross-resistance among novel androgen receptor signaling inhibitors in non-metastatic castration-resistant prostate cancer.International journal of clinical oncology · 2025Article
- Cell-free DNA aneuploidy score as a dynamic early response marker in prostate cancer.Molecular oncology · 2025Article
- Mechanisms and Therapeutic Strategies to Overcome Enzalutamide Resistance in Advanced Prostate Cancer.Serican journal of medicine · 2025Article
- Integrating Aggressive-Variant Prostate Cancer-Associated Tumor Suppressor Gene Status with Clinical Variables to Refine Prognosis and Predict Androgen Receptor Pathway Inhibitor Response in Metastatic Hormone-Sensitive Setting.International journal of molecular sciences · 2025Article
- Article
- Current Evidence on Cabazitaxel for Prostate Cancer Therapy: A Narrative Review.International journal of urology : official journal of the Japanese Urological Association · 2025Review
- Androgen receptor signalling in non-prostatic malignancies: challenges and opportunities.Nature reviews. Cancer · 2025Review
- Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach.JMIR medical informatics · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Response to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers are needed. We use whole-genomics (WGS; n = 155) with matching whole-transcriptomics (WTS; n = 113) from biopsies of ARSI-treated mCRPC patients for unbiased discovery of biomarkers and development of machine learning-based prediction models. Tumor mutational burden (q < 0.001), structural variants (q < 0.05), tandem duplications (q < 0.05) and deletions (q < 0.05) are enriched in poor responders, coupled with distinct transcriptomic expression profiles. Validating various classification models predicting treatment duration with ARSI on our internal and external mCRPC cohort reveals two best-performing models, based on the combination of prior treatment information with either the four combined enriched genomic markers or with overall transcriptomic profiles. In conclusion, predictive models combining genomic, transcriptomic, and clinical data can predict response to ARSI in mCRPC patients and, with additional optimization and prospective validation, could improve treatment guidance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.