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Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study.

Ross-Adams H, Lamb AD, Dunning MJ, Halim S, Lindberg J, Massie CM, Egevad LA, Russell R, Ramos-Montoya A, Vowler SL, Sharma NL, Kay J, Whitaker H, Clark J, Hurst R, Gnanapragasam VJ, Shah NC, Warren AY, Cooper CS, Lynch AG, Stark R, Mills IG, Grönberg H, Neal DE, CamCaP Study Gro - EBioMedicine (2015)

Bottom Line: We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses.We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue.A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001).

View Article: PubMed Central - PubMed

Affiliation: Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.

ABSTRACT

Background: Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome.

Methods: In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone.

Findings: We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions.

Interpretation: For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.

No MeSH data available.


Related in: MedlinePlus

Copy number and expression levels for 100 clustering genes in each integrated cluster.Mean mRNA expression levels are shown as a heatmap for each of the 100 genes used to differentiate the integrated clusters. Copy number is displayed as the number of men with a gain or loss in copies of that gene in that cluster. Chromosome location is also given (see Fig. 2). Scaling as shown.
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f0015: Copy number and expression levels for 100 clustering genes in each integrated cluster.Mean mRNA expression levels are shown as a heatmap for each of the 100 genes used to differentiate the integrated clusters. Copy number is displayed as the number of men with a gain or loss in copies of that gene in that cluster. Chromosome location is also given (see Fig. 2). Scaling as shown.

Mentions: We assessed transcript and copy number levels for these 100 classifying genes for both the discovery and validation cohorts (Fig. 3). There was clear consistency of expression and copy number aberrations in trained clusters with the exception of subsets of genes in iCluster 2 and 5, which displayed marked copy number amplification in the Stockholm cohort. There was consistent copy number loss and downregulation of expression of genes on chromosome 8 (e.g. MTMR9, LSM1 and ER1) in two particular subgroups iCluster 1 and 3, while iCluster 3 was characterised uniquely by copy number gain and upregulation of neighbouring genes on chromosome 8 (e.g. RIPK2, SPIDR and IMPA1). By contrast, iCluster 4 had consistent copy number loss and downregulation of genes on chromosome 13 (e.g. TRIM13, PHF11 and SUGT1).


Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study.

Ross-Adams H, Lamb AD, Dunning MJ, Halim S, Lindberg J, Massie CM, Egevad LA, Russell R, Ramos-Montoya A, Vowler SL, Sharma NL, Kay J, Whitaker H, Clark J, Hurst R, Gnanapragasam VJ, Shah NC, Warren AY, Cooper CS, Lynch AG, Stark R, Mills IG, Grönberg H, Neal DE, CamCaP Study Gro - EBioMedicine (2015)

Copy number and expression levels for 100 clustering genes in each integrated cluster.Mean mRNA expression levels are shown as a heatmap for each of the 100 genes used to differentiate the integrated clusters. Copy number is displayed as the number of men with a gain or loss in copies of that gene in that cluster. Chromosome location is also given (see Fig. 2). Scaling as shown.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4588396&req=5

f0015: Copy number and expression levels for 100 clustering genes in each integrated cluster.Mean mRNA expression levels are shown as a heatmap for each of the 100 genes used to differentiate the integrated clusters. Copy number is displayed as the number of men with a gain or loss in copies of that gene in that cluster. Chromosome location is also given (see Fig. 2). Scaling as shown.
Mentions: We assessed transcript and copy number levels for these 100 classifying genes for both the discovery and validation cohorts (Fig. 3). There was clear consistency of expression and copy number aberrations in trained clusters with the exception of subsets of genes in iCluster 2 and 5, which displayed marked copy number amplification in the Stockholm cohort. There was consistent copy number loss and downregulation of expression of genes on chromosome 8 (e.g. MTMR9, LSM1 and ER1) in two particular subgroups iCluster 1 and 3, while iCluster 3 was characterised uniquely by copy number gain and upregulation of neighbouring genes on chromosome 8 (e.g. RIPK2, SPIDR and IMPA1). By contrast, iCluster 4 had consistent copy number loss and downregulation of genes on chromosome 13 (e.g. TRIM13, PHF11 and SUGT1).

Bottom Line: We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses.We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue.A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001).

View Article: PubMed Central - PubMed

Affiliation: Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.

ABSTRACT

Background: Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome.

Methods: In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone.

Findings: We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions.

Interpretation: For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.

No MeSH data available.


Related in: MedlinePlus