Combining molecular subtypes with multivariable clinical models has the potential to improve prediction of treatment outcomes in prostate cancer at diagnosis

Lewis Wardale, Ryan Cardenas, Vincent J. Gnanapragasam, Colin S. Cooper, Jeremy Clark, Daniel S. Brewer

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Clinical management of prostate cancer is challenging because of its highly variable natural his-tory and so there is a need for improved predictors of outcome in non-metastatic men at the time of diagnosis. In this study we calculated the model score from the leading clinical multivariable model, PREDICT prostate, and the poor prognosis DESNT molecular subtype, in a combined ex-pression and clinical dataset that were taken from malignant tissue at prostatectomy (n = 359). Both PREDICT score (p < 0.0001, IQR HR = 1.59) and DESNT score (p < 0.0001, IQR HR = 2.08) were significant predictors for time to biochemical recurrence. A joint model combining the con-tinuous PREDICT and DESNT score (p < 0.0001, IQR HR = 1.53 and 1.79, respectively) produced a significantly improved predictor than either model alone (p < 0.001). An increased probability of mortality after diagnosis, as estimated by PREDICT, was characterised by upregulation of cell-cycle related pathways and the downregulation of metabolism and cholesterol biosynthesis. The DESNT molecular subtype has distinct biological characteristics to those associated with the PREDICT model. We conclude that the inclusion of biological information alongside current clin-ical prognostic tools has the potential to improve the ability to choose the optimal treatment pathway for a patient.
Original languageEnglish
Pages (from-to)157-170
Number of pages14
JournalCurrent Oncology
Issue number1
Early online date22 Dec 2022
Publication statusPublished - Jan 2023


  • clinical models
  • expression
  • molecular subtypes
  • predictive models
  • prostate cancer
  • statistical model
  • transcriptome

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