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Updated: Aug 5, 2026
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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
NGS-Based Genomic Profiling Identifies Independent Predictors of Time to Castration Resistance in Hormone-Sensitive
Merve Turan1, Merve Çırak Balta2
1Department of Medical Oncology, Faculty of Medicine, Adnan Menderes University, Aydın 09010, Türkiye.
Current Oncology (Toronto, Ont.)
|July 27, 2026
Summary
Next-generation sequencing (NGS) in hormone-sensitive prostate cancer reveals key gene alterations like KMT2C and TP53 that predict faster progression to castration-resistant prostate cancer (CRPC) and shorter survival. This supports using NGS for early risk stratification.
Area of Science:
- Oncology
- Genomics
- Prostate Cancer Research
Background:
- Prognostic significance of next-generation sequencing (NGS) in hormone-sensitive prostate cancer (HSPC) is not well understood.
- Early identification of progression risk is crucial for treatment planning.
Purpose of the Study:
- To investigate the prognostic value of NGS findings in HSPC.
- To identify genomic predictors of progression to castration-resistant prostate cancer (CRPC) and survival outcomes.
Main Methods:
- Retrospective cohort study of 92 patients with HSPC undergoing tumor tissue NGS.
- Analysis of time to CRPC, overall survival from androgen deprivation therapy (ADT) initiation (OS-ADT), and overall survival from diagnosis.
- Kaplan-Meier and Cox regression analyses were used.
Main Results:
- Frequent gene alterations included ATR, PTEN, TP53, and BRCA2.
- KMT2C alteration was the strongest independent predictor of shorter time to CRPC and OS-ADT.
- High genomic burden independently predicted shorter time to CRPC.
- TP53 alteration independently predicted shorter OS-ADT.
Conclusions:
- NGS during the hormone-sensitive phase identifies independent predictors of CRPC.
- Genomic alterations like KMT2C and TP53, and high genomic burden, are associated with poorer outcomes.
- NGS integration at ADT initiation can aid risk stratification and personalized treatment.
