Combining Molecular Subtypes with Multivariable Clinical Models Has the Potential to Improve Prediction of Treatment Outcomes in Prostate Cancer at Diagnosis.

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Cardenas, Ryan 
Gnanapragasam, Vincent J 
Cooper, Colin S 
Clark, Jeremy 

Clinical management of prostate cancer is challenging because of its highly variable natural history 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 expression 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 continuous 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 clinical prognostic tools has the potential to improve the ability to choose the optimal treatment pathway for a patient.


Peer reviewed: True

Funder: Bob Champion Cancer Trust

Funder: The Masonic Charitable Foundation

Funder: The King Family

Funder: The Hargrave Foundation

Funder: The University of East Anglia

Funder: Prostate Cancer Research, Movember, Prostate Cancer UK

Funder: The Big C Cancer Charity

Funder: Cancer Research UK

Funder: The Andy Ripley Memorial Fund

Article, prostate cancer, clinical models, predictive models, molecular subtypes, transcriptome, expression, statistical model
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Curr Oncol
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