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Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer

Krzyzanowska, Agnieszka LU ; Barron, Stephen ; Higgins, Debra F. ; Loughman, Tony ; O'Neill, Amanda ; Sheehan, Katherine M. ; Wang, Chan Ju Angel ; Fender, Bozena ; McGuire, Leah and Fay, Joanna , et al. (2023) In European Urology Focus 9(6). p.983-991
Abstract

Background: Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease. Objective: To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management. Design, setting, and participants: Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk... (More)

Background: Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease. Objective: To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management. Design, setting, and participants: Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203). Outcome measurements and statistical analysis: The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models. Results and limitations: The six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study. Conclusions: The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP. Patient summary: We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

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@article{46aeac07-d8ef-439b-95f3-a367bec2370a,
  abstract     = {{<p>Background: Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease. Objective: To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management. Design, setting, and participants: Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203). Outcome measurements and statistical analysis: The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models. Results and limitations: The six-gene signature was significantly (p &lt; 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study. Conclusions: The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP. Patient summary: We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.</p>}},
  author       = {{Krzyzanowska, Agnieszka and Barron, Stephen and Higgins, Debra F. and Loughman, Tony and O'Neill, Amanda and Sheehan, Katherine M. and Wang, Chan Ju Angel and Fender, Bozena and McGuire, Leah and Fay, Joanna and O'Grady, Anthony and O'Leary, Des and Watson, R. William and Bjartell, Anders and Gallagher, William M.}},
  issn         = {{2405-4569}},
  keywords     = {{Molecular gene expression signature; Prognosis; Prostate cancer clinical patient management; Risk stratification; Treatment decisions}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{983--991}},
  publisher    = {{Elsevier}},
  series       = {{European Urology Focus}},
  title        = {{Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer}},
  url          = {{http://dx.doi.org/10.1016/j.euf.2023.04.004}},
  doi          = {{10.1016/j.euf.2023.04.004}},
  volume       = {{9}},
  year         = {{2023}},
}