A novel stratification framework for predicting outcome in patients with prostate cancer.

ABSTRACT:

Background

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 prostate cancer samples. Our aim is to use more sophisticated analytical approaches to deconvolute the structure of prostate cancer transcriptome data, providing novel clinically actionable information for this disease.

Methods

We apply an unsupervised model called Latent Process Decomposition (LPD), which can handle heterogeneity within individual 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 sho

SUBMITTER: Luca BA 

PROVIDER: S-EPMC7217762 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature