Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study
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 stud...
| Главные авторы: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Формат: | Journal article |
| Язык: | English |
| Опубликовано: |
Elsevier
2015
|
| _version_ | 1826257904435462144 |
|---|---|
| author | Ross-Adams, H Lamb, A Dunning, M Halim, S Lindberg, J Massie, C Egevad, L Russell, R Ramos-Montoya, A Vowler, S Sharma, N Kay, J Whitaker, H Clark, J Hurst, R Gnanapragasam, V Shah, N Warren, A Cooper, C Lynch, A Stark, R Mills, I Grönberg, H Neal, D |
| author_facet | Ross-Adams, H Lamb, A Dunning, M Halim, S Lindberg, J Massie, C Egevad, L Russell, R Ramos-Montoya, A Vowler, S Sharma, N Kay, J Whitaker, H Clark, J Hurst, R Gnanapragasam, V Shah, N Warren, A Cooper, C Lynch, A Stark, R Mills, I Grönberg, H Neal, D |
| author_sort | Ross-Adams, H |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T18:25:33Z |
| format | Journal article |
| id | oxford-uuid:07d4b66a-9de7-4ce2-85c3-71f0acd4c553 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:25:33Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:07d4b66a-9de7-4ce2-85c3-71f0acd4c5532022-03-26T09:09:43ZIntegration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort studyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:07d4b66a-9de7-4ce2-85c3-71f0acd4c553EnglishSymplectic Elements at OxfordElsevier2015Ross-Adams, HLamb, ADunning, MHalim, SLindberg, JMassie, CEgevad, LRussell, RRamos-Montoya, AVowler, SSharma, NKay, JWhitaker, HClark, JHurst, RGnanapragasam, VShah, NWarren, ACooper, CLynch, AStark, RMills, IGrönberg, HNeal, DBackground 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. |
| spellingShingle | Ross-Adams, H Lamb, A Dunning, M Halim, S Lindberg, J Massie, C Egevad, L Russell, R Ramos-Montoya, A Vowler, S Sharma, N Kay, J Whitaker, H Clark, J Hurst, R Gnanapragasam, V Shah, N Warren, A Cooper, C Lynch, A Stark, R Mills, I Grönberg, H Neal, D Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study |
| title | Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study |
| title_full | Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study |
| title_fullStr | Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study |
| title_full_unstemmed | Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study |
| title_short | Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study |
| title_sort | integration of copy number and transcriptomics provides risk stratification in prostate cancer a discovery and validation cohort study |
| work_keys_str_mv | AT rossadamsh integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT lamba integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT dunningm integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT halims integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT lindbergj integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT massiec integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT egevadl integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT russellr integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT ramosmontoyaa integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT vowlers integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT sharman integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT kayj integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT whitakerh integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT clarkj integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT hurstr integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT gnanapragasamv integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT shahn integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT warrena integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT cooperc integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT lyncha integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT starkr integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT millsi integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT gronbergh integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy AT neald integrationofcopynumberandtranscriptomicsprovidesriskstratificationinprostatecanceradiscoveryandvalidationcohortstudy |