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A Novel Stratification Framework for Predicting Outcome in Patients with Prostate Cancer

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A Novel Stratification Framework for Predicting Outcome in Patients with Prostate Cancer. / Campbell, I C G; Luca, Bogdan; Moulton, Vincent; Ellis, Christopher ; Edwards, Dylan R.; Cooper, Rosalyn; Clark, Jeremy; Brewer, Daniel; Campbell, Colin.

In: British Journal of Cancer, 01.03.2020.

Research output: Contribution to journalArticle

Harvard

Campbell, ICG, Luca, B, Moulton, V, Ellis, C, Edwards, DR, Cooper, R, Clark, J, Brewer, D & Campbell, C 2020, 'A Novel Stratification Framework for Predicting Outcome in Patients with Prostate Cancer', British Journal of Cancer.

APA

Campbell, I. C. G., Luca, B., Moulton, V., Ellis, C., Edwards, D. R., Cooper, R., ... Campbell, C. (2020). A Novel Stratification Framework for Predicting Outcome in Patients with Prostate Cancer. British Journal of Cancer.

Vancouver

Campbell ICG, Luca B, Moulton V, Ellis C, Edwards DR, Cooper R et al. A Novel Stratification Framework for Predicting Outcome in Patients with Prostate Cancer. British Journal of Cancer. 2020 Mar 1.

Author

Campbell, I C G ; Luca, Bogdan ; Moulton, Vincent ; Ellis, Christopher ; Edwards, Dylan R. ; Cooper, Rosalyn ; Clark, Jeremy ; Brewer, Daniel ; Campbell, Colin. / A Novel Stratification Framework for Predicting Outcome in Patients with Prostate Cancer. In: British Journal of Cancer. 2020.

Bibtex

@article{5cb2a1048a2546ef9e9e84ef4ed54ff2,
title = "A Novel Stratification Framework for Predicting Outcome in Patients with Prostate Cancer",
abstract = "Unsupervised learning methods such as Hierarchical Cluster Analysis are commonly used for the analysis of genomic platform data. Unfortunately, such approaches ignore the well documented heterogeneous composition of cancer samples. Our aim is to use more sophisticated analytical approaches to deconvolute the structure of prostate cancer transciptome data providing novel clinically actionable information for human prostate cancer.Methods: We apply an unsupervised model called Latent Process Decomposition (LPD), which can handle heterogeneity within cancer samples, to genome-wide expression data from eight prostate cancer clinical series including 1,785 malignant samples with the clinical endpoints of PSA failure and metastasis.Results: We show that PSA failure is correlated with the level of an expression signature called DESNT (HR = 1.52, 95{\%} CI = [1.36, 1.7], P = 9.0x10-14, Cox model) and that patients with a majority DESNT signature have an increased metastasic risk (X2-test, P = 0.0017, and P = 0.0019). Additionally, we develop a stratification framework that incorporates DESNT and identifies three novel molecular subtypes of prostate cancer.",
author = "Campbell, {I C G} and Bogdan Luca and Vincent Moulton and Christopher Ellis and Edwards, {Dylan R.} and Rosalyn Cooper and Jeremy Clark and Daniel Brewer and Colin Campbell",
year = "2020",
month = "3",
day = "1",
language = "English",
journal = "British Journal of Cancer",
issn = "0007-0920",
publisher = "Springer Nature",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - A Novel Stratification Framework for Predicting Outcome in Patients with Prostate Cancer

AU - Campbell, I C G

AU - Luca, Bogdan

AU - Moulton, Vincent

AU - Ellis, Christopher

AU - Edwards, Dylan R.

AU - Cooper, Rosalyn

AU - Clark, Jeremy

AU - Brewer, Daniel

AU - Campbell, Colin

PY - 2020/3/1

Y1 - 2020/3/1

N2 - Unsupervised learning methods such as Hierarchical Cluster Analysis are commonly used for the analysis of genomic platform data. Unfortunately, such approaches ignore the well documented heterogeneous composition of cancer samples. Our aim is to use more sophisticated analytical approaches to deconvolute the structure of prostate cancer transciptome data providing novel clinically actionable information for human prostate cancer.Methods: We apply an unsupervised model called Latent Process Decomposition (LPD), which can handle heterogeneity within cancer samples, to genome-wide expression data from eight prostate cancer clinical series including 1,785 malignant samples with the clinical endpoints of PSA failure and metastasis.Results: We show that PSA failure is correlated with the level of an expression signature called DESNT (HR = 1.52, 95% CI = [1.36, 1.7], P = 9.0x10-14, Cox model) and that patients with a majority DESNT signature have an increased metastasic risk (X2-test, P = 0.0017, and P = 0.0019). Additionally, we develop a stratification framework that incorporates DESNT and identifies three novel molecular subtypes of prostate cancer.

AB - Unsupervised learning methods such as Hierarchical Cluster Analysis are commonly used for the analysis of genomic platform data. Unfortunately, such approaches ignore the well documented heterogeneous composition of cancer samples. Our aim is to use more sophisticated analytical approaches to deconvolute the structure of prostate cancer transciptome data providing novel clinically actionable information for human prostate cancer.Methods: We apply an unsupervised model called Latent Process Decomposition (LPD), which can handle heterogeneity within cancer samples, to genome-wide expression data from eight prostate cancer clinical series including 1,785 malignant samples with the clinical endpoints of PSA failure and metastasis.Results: We show that PSA failure is correlated with the level of an expression signature called DESNT (HR = 1.52, 95% CI = [1.36, 1.7], P = 9.0x10-14, Cox model) and that patients with a majority DESNT signature have an increased metastasic risk (X2-test, P = 0.0017, and P = 0.0019). Additionally, we develop a stratification framework that incorporates DESNT and identifies three novel molecular subtypes of prostate cancer.

M3 - Article

JO - British Journal of Cancer

JF - British Journal of Cancer

SN - 0007-0920

ER -