Scholarlink

Background <p>The Prostatype® Test evaluates expression levels of three stem cell genes (IGFBP3, F3, and VGLL3), which are combined with PSA, stage, and grade to calculate P-score. Previous research found P-score accurately predicts prostate cancer (PC) specific mortality (PCSM) in patients with newly diagnosed clinically localized PC. We evaluated the performance of P-score to predict PCSM in a large, multiethnic cohort from the Veterans’ Administration (VA).</p> Methods <p>After pathologic review to ensure sufficient tumor tissue, formalin-fixed paraffin-embedded (FFPE) biopsy cores from patients with newly diagnosed PC at the Durham VA were sent to an academic medical center. There, cores were sectioned, RNA extracted, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests conducted for IGFBP3, F3, VGLL3, and GAPDH (control). Results were combined with clinical data to generate P-scores. The association between P-score and PCSM was evaluated using c-index, Cox and Fine-Gray models, and decision curve analysis (DCA).</p> Results <p>Higher P-scores were significantly associated with a higher risk of PCSM (HR = 1.48 per 1 unit increase in P-score, 95% CI: 1.20–1.84, <i>p</i> &lt;0.001) and accurately estimated PCSM (c-index = 0.87). Adding clinical variables to P-score only incrementally improved accuracy. The DCA indicated P-score provided net clinical benefit for patients with PCSM risk between 5% and ~50%. As P-score strongly correlated with risk group, we tested the value of P-score in intermediate-risk patients specifically, where it significantly predicted PCSM (HR 1.43, 95% CI: 1.09–1.86, <i>p</i> = 0.009).</p> Conclusion <p>In this American cohort of veterans, P-score significantly predicted PCSM. Adding clinical variables minimally improved accuracy. Accuracy remained high in intermediate-risk patients, wherein there is arguably the greatest need for better risk stratification. Given P-scores can be generated rapidly in-house using a standardized RT-qPCR assay, P-score represents a robust new tool to risk-stratify newly diagnosed patients for PC death, thereby minimizing mismatched treatments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Validation of the Prostatype® P-score for predicting prostate cancer specific mortality in a multiethnic U.S. veterans cohort

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

查看全文
  • Alexandra Mack,
  • Trung Duong Tran,
  • Emelie Berglund,
  • Gerald L. Andriole,
  • Christopher Alley,
  • Anthony E. Sisk,
  • Iveth Estrada-Reyes,
  • Kara Bissell,
  • Haleigh Bellerose,
  • Aubrey Jarman,
  • Anna Hoffmeyer,
  • Michael Burns,
  • Sergio Sanders,
  • Eric Vail,
  • Andy Pao,
  • Raja Khurram,
  • Amal Ahmed,
  • Stephen J. Freedland

摘要

Background

The Prostatype® Test evaluates expression levels of three stem cell genes (IGFBP3, F3, and VGLL3), which are combined with PSA, stage, and grade to calculate P-score. Previous research found P-score accurately predicts prostate cancer (PC) specific mortality (PCSM) in patients with newly diagnosed clinically localized PC. We evaluated the performance of P-score to predict PCSM in a large, multiethnic cohort from the Veterans’ Administration (VA).

Methods

After pathologic review to ensure sufficient tumor tissue, formalin-fixed paraffin-embedded (FFPE) biopsy cores from patients with newly diagnosed PC at the Durham VA were sent to an academic medical center. There, cores were sectioned, RNA extracted, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests conducted for IGFBP3, F3, VGLL3, and GAPDH (control). Results were combined with clinical data to generate P-scores. The association between P-score and PCSM was evaluated using c-index, Cox and Fine-Gray models, and decision curve analysis (DCA).

Results

Higher P-scores were significantly associated with a higher risk of PCSM (HR = 1.48 per 1 unit increase in P-score, 95% CI: 1.20–1.84, p <0.001) and accurately estimated PCSM (c-index = 0.87). Adding clinical variables to P-score only incrementally improved accuracy. The DCA indicated P-score provided net clinical benefit for patients with PCSM risk between 5% and ~50%. As P-score strongly correlated with risk group, we tested the value of P-score in intermediate-risk patients specifically, where it significantly predicted PCSM (HR 1.43, 95% CI: 1.09–1.86, p = 0.009).

Conclusion

In this American cohort of veterans, P-score significantly predicted PCSM. Adding clinical variables minimally improved accuracy. Accuracy remained high in intermediate-risk patients, wherein there is arguably the greatest need for better risk stratification. Given P-scores can be generated rapidly in-house using a standardized RT-qPCR assay, P-score represents a robust new tool to risk-stratify newly diagnosed patients for PC death, thereby minimizing mismatched treatments.