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

Wardale, Lewis, Cardenas, Ryan, Gnanapragasam, Vincent J., Cooper, Colin S. ORCID: https://orcid.org/0000-0003-2013-8042, Clark, Jeremy and Brewer, Daniel S. ORCID: https://orcid.org/0000-0003-4753-9794 (2023) Combining molecular subtypes with multivariable clinical models has the potential to improve prediction of treatment outcomes in prostate cancer at diagnosis. Current Oncology, 30 (1). pp. 157-170. ISSN 1198-0052

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Abstract

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.

Item Type:	Article
Additional Information:	Data Availability Statement: The datasets analysed during the current study are publicly available: MSKCC [18]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034 (accessed on 1 May 2016); CancerMap [12]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94767 (accessed on 1 May 2016); Stephenson [19]: Data available from the corresponding author of this paper. CamCap [20]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70768 (accessed on 1 May 2016) and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70769 (accessed on 1 May 2016). Funding: This work was funded by the Bob Champion Cancer Trust, The Masonic Charitable Foundation, The King Family, The Hargrave Foundation and The University of East Anglia. We acknowledge support from Prostate Cancer Research, Movember, Prostate Cancer UK, The Big C Cancer Charity, Cancer Research UK and The Andy Ripley Memorial Fund.
Uncontrolled Keywords:	clinical models,expression,molecular subtypes,predictive models,prostate cancer,statistical model,transcriptome,oncology,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700/2730
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Groups > Cancer Studies Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	09 Jan 2023 13:32
Last Modified:	09 Apr 2024 03:31
URI:	https://ueaeprints.uea.ac.uk/id/eprint/90478
DOI:	10.3390/curroncol30010013

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