Limits...
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

A copy number profile of the prostate cancer genome.The percentage of samples containing copy number aberrations (CNA) at each locus is shown by gain/loss (red/blue); left hand y-axis. Established prostate cancer risk genes commonly disrupted by CNAs (from Williams et al. (2014) meta-analysis) are indicated in grey (gene name and frequency altered in this cohort are shown, see also Suppl. Table 3); only those affected in > 10% samples are annotated. Novel CN changes identified in this cohort (> 10% samples) also in our 100-gene set are indicated in black type. MAP3K7 is highlighted in purple as the only previously known CN-altered risk gene included in our 100-gene signature.Data were generated on high-density Illumina OMNI2.5 M arrays and analysed using OncoSNP (Yau et al., 2010); only highly stringent calls are shown (see Methods). Chromosome ends are delineated by grey, vertical stripes. Representative genes with large average fold changes (tumours versus matched benign) are shown by red (up-regulation) and green spots (down-regulation); right-hand y-axis. With the exception of OLFM4 (19%, chr13q14.3), these do not coincide with CN alterations.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4588396&req=5

f0005: A copy number profile of the prostate cancer genome.The percentage of samples containing copy number aberrations (CNA) at each locus is shown by gain/loss (red/blue); left hand y-axis. Established prostate cancer risk genes commonly disrupted by CNAs (from Williams et al. (2014) meta-analysis) are indicated in grey (gene name and frequency altered in this cohort are shown, see also Suppl. Table 3); only those affected in > 10% samples are annotated. Novel CN changes identified in this cohort (> 10% samples) also in our 100-gene set are indicated in black type. MAP3K7 is highlighted in purple as the only previously known CN-altered risk gene included in our 100-gene signature.Data were generated on high-density Illumina OMNI2.5 M arrays and analysed using OncoSNP (Yau et al., 2010); only highly stringent calls are shown (see Methods). Chromosome ends are delineated by grey, vertical stripes. Representative genes with large average fold changes (tumours versus matched benign) are shown by red (up-regulation) and green spots (down-regulation); right-hand y-axis. With the exception of OLFM4 (19%, chr13q14.3), these do not coincide with CN alterations.

Mentions: The overall genome-wide copy number (CN) profile of the Cambridge (discovery) cohort is consistent with previous findings, with chromosome 8p loss/8q gain evident (Fig. 1). The detection of known prostate cancer risk CN changes (Williams et al., 2014; Sun et al., 2007) in our cohort is broadly consistent with other studies (Suppl. Table 3), with key tumour suppressor NKX3-1 (8p21.2) deleted in 40% of samples, and RB1 (24%), PTEN (18%), and TP53 (11%) deletions also easily detected even with very stringent call ranking selection criteria applied (see Methods). A similar overall profile is evident in the Stockholm validation data set (Suppl. Fig. 1), despite being assayed and analysed on different platforms (see Methods). These findings also show that well-established molecular changes in prostate cancer are detectable even at relatively low percentage tumour core content (Table 1).


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)

A copy number profile of the prostate cancer genome.The percentage of samples containing copy number aberrations (CNA) at each locus is shown by gain/loss (red/blue); left hand y-axis. Established prostate cancer risk genes commonly disrupted by CNAs (from Williams et al. (2014) meta-analysis) are indicated in grey (gene name and frequency altered in this cohort are shown, see also Suppl. Table 3); only those affected in > 10% samples are annotated. Novel CN changes identified in this cohort (> 10% samples) also in our 100-gene set are indicated in black type. MAP3K7 is highlighted in purple as the only previously known CN-altered risk gene included in our 100-gene signature.Data were generated on high-density Illumina OMNI2.5 M arrays and analysed using OncoSNP (Yau et al., 2010); only highly stringent calls are shown (see Methods). Chromosome ends are delineated by grey, vertical stripes. Representative genes with large average fold changes (tumours versus matched benign) are shown by red (up-regulation) and green spots (down-regulation); right-hand y-axis. With the exception of OLFM4 (19%, chr13q14.3), these do not coincide with CN alterations.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0005: A copy number profile of the prostate cancer genome.The percentage of samples containing copy number aberrations (CNA) at each locus is shown by gain/loss (red/blue); left hand y-axis. Established prostate cancer risk genes commonly disrupted by CNAs (from Williams et al. (2014) meta-analysis) are indicated in grey (gene name and frequency altered in this cohort are shown, see also Suppl. Table 3); only those affected in > 10% samples are annotated. Novel CN changes identified in this cohort (> 10% samples) also in our 100-gene set are indicated in black type. MAP3K7 is highlighted in purple as the only previously known CN-altered risk gene included in our 100-gene signature.Data were generated on high-density Illumina OMNI2.5 M arrays and analysed using OncoSNP (Yau et al., 2010); only highly stringent calls are shown (see Methods). Chromosome ends are delineated by grey, vertical stripes. Representative genes with large average fold changes (tumours versus matched benign) are shown by red (up-regulation) and green spots (down-regulation); right-hand y-axis. With the exception of OLFM4 (19%, chr13q14.3), these do not coincide with CN alterations.
Mentions: The overall genome-wide copy number (CN) profile of the Cambridge (discovery) cohort is consistent with previous findings, with chromosome 8p loss/8q gain evident (Fig. 1). The detection of known prostate cancer risk CN changes (Williams et al., 2014; Sun et al., 2007) in our cohort is broadly consistent with other studies (Suppl. Table 3), with key tumour suppressor NKX3-1 (8p21.2) deleted in 40% of samples, and RB1 (24%), PTEN (18%), and TP53 (11%) deletions also easily detected even with very stringent call ranking selection criteria applied (see Methods). A similar overall profile is evident in the Stockholm validation data set (Suppl. Fig. 1), despite being assayed and analysed on different platforms (see Methods). These findings also show that well-established molecular changes in prostate cancer are detectable even at relatively low percentage tumour core content (Table 1).

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