Molecular evolution and clinical trajectories of prostate cancer identifies novel markers for risk stratification

Study ID	Alternative Stable ID	Type
EGAS00001002923		Other

Study ID

Alternative Stable ID

Type

EGAS00001002923

Other

Study Description

Prostate cancer is a highly heterogeneous disease that is thought to develop over many years. Identifying the earliest somatic changes can give important insights into the tumour evolution and aid in stratifying high- from low-risk diseases. Here we pursued integrative whole-genome, transcriptome and methylome-based analysis of early-onset prostate cancer patients. Characterisation of genomic alterations across 270 PCa tumours revealed age-related genomic alterations and mutation signatures including BRCAness and APOBEC. We used methylation- and expression-data to identify four molecular subgroups, which included a highly aggressive tumour subgroup that frequently involved recurrent duplications and increased expression of ESRP1. Analysis of 12,000 tissue-microarray tumour samples demonstrated that ESRP1 is a candidate biomarker associated with faster proliferation rate and shorter time to relapse. We combine the patterns of molecular co-occurrence, risk-stratification and subgroup information to deconvolute tumor heterogeneity, which reveal complex but recurrent clinical ... (Show More)

Dataset ID	Description	Technology	Samples
EGAD00001004791	The dataset contains RNA-seq data of 96 EOPC patients and 9 controls. For some patients multiple tissue samples were sequenced ("multi-area" samples). The RNA extraction and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013. The dataset contains RNA-seq data of 96 EOPC patients and 9 controls. For some patients multiple tissue samples were sequenced ("multi-area" samples). The RNA extraction and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.	Illumina HiSeq 2000	1
EGAD00001007669	This dataset contains 139 Tumor and Control WGS files for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013. This dataset contains 139 Tumor and Control WGS files for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.	HiSeq X Ten,Illumina HiSeq 2000	141
EGAD00001007861	This dataset contains 318 Tumor and Control WGS files submitted in another EGA box for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013. This dataset contains 318 Tumor and Control WGS files submitted in another EGA box for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.	Illumina HiSeq 2000	320

Dataset ID

Description

Technology

Samples

EGAD00001004791

The dataset contains RNA-seq data of 96 EOPC patients and 9 controls. For some patients multiple tissue samples were sequenced ("multi-area" samples). The RNA extraction and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

Illumina HiSeq 2000

EGAD00001007669

This dataset contains 139 Tumor and Control WGS files for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

HiSeq X Ten,Illumina HiSeq 2000

141

EGAD00001007861

This dataset contains 318 Tumor and Control WGS files submitted in another EGA box for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

Illumina HiSeq 2000

320

Study

Study Description

Study Datasets 3 datasets.

Who archives the data?

Publications

Citations

The European Genome-phenome Archive (EGA) is part of the ELIXIR infrastructure.