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Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer

BACKGROUND: Patients with newly diagnosed non-metastatic prostate adenocarcinoma are typically classified as at low, intermediate, or high risk of disease progression using blood prostate-specific antigen concentration, tumour T category, and tumour pathological Gleason score. Classification is used...

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Autores principales: Johnston, Wendy L., Catton, Charles N., Swallow, Carol J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6322345/
https://www.ncbi.nlm.nih.gov/pubmed/30616540
http://dx.doi.org/10.1186/s12894-018-0433-5
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author Johnston, Wendy L.
Catton, Charles N.
Swallow, Carol J.
author_facet Johnston, Wendy L.
Catton, Charles N.
Swallow, Carol J.
author_sort Johnston, Wendy L.
collection PubMed
description BACKGROUND: Patients with newly diagnosed non-metastatic prostate adenocarcinoma are typically classified as at low, intermediate, or high risk of disease progression using blood prostate-specific antigen concentration, tumour T category, and tumour pathological Gleason score. Classification is used to both predict clinical outcome and to inform initial management. However, significant heterogeneity is observed in outcome, particularly within the intermediate risk group, and there is an urgent need for additional markers to more accurately hone risk prediction. Recently developed web-based visualization and analysis tools have facilitated rapid interrogation of large transcriptome datasets, and querying broadly across multiple large datasets should identify predictors that are widely applicable. METHODS: We used camcAPP, cBioPortal, CRN, and NIH NCI GDC Data Portal to data mine publicly available large prostate cancer datasets. A test set of biomarkers was developed by identifying transcripts that had: 1) altered abundance in prostate cancer, 2) altered expression in patients with Gleason score 7 tumours and biochemical recurrence, 3) correlation of expression with time until biochemical recurrence across three datasets (Cambridge, Stockholm, MSKCC). Transcripts that met these criteria were then examined in a validation dataset (TCGA-PRAD) using univariate and multivariable models to predict biochemical recurrence in patients with Gleason score 7 tumours. RESULTS: Twenty transcripts met the test criteria, and 12 were validated in TCGA-PRAD Gleason score 7 patients. Ten of these transcripts remained prognostic in Gleason score 3 + 4 = 7, a sub-group of Gleason score 7 patients typically considered at a lower risk for poor outcome and often not targeted for aggressive management. All transcripts positively associated with recurrence encode or regulate mitosis and cell cycle-related proteins. The top performer was BUB1, one of four key MIR145-3P microRNA targets upregulated in hormone-sensitive as well as castration-resistant PCa. SRD5A2 converts testosterone to its more active form and was negatively associated with biochemical recurrence. CONCLUSIONS: Unbiased mining of large patient datasets identified 12 transcripts that independently predicted disease recurrence risk in Gleason score 7 prostate cancer. The mitosis and cell cycle proteins identified are also implicated in progression to castration-resistant prostate cancer, revealing a pivotal role for loss of cell cycle control in the latter. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12894-018-0433-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-63223452019-01-10 Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer Johnston, Wendy L. Catton, Charles N. Swallow, Carol J. BMC Urol Research Article BACKGROUND: Patients with newly diagnosed non-metastatic prostate adenocarcinoma are typically classified as at low, intermediate, or high risk of disease progression using blood prostate-specific antigen concentration, tumour T category, and tumour pathological Gleason score. Classification is used to both predict clinical outcome and to inform initial management. However, significant heterogeneity is observed in outcome, particularly within the intermediate risk group, and there is an urgent need for additional markers to more accurately hone risk prediction. Recently developed web-based visualization and analysis tools have facilitated rapid interrogation of large transcriptome datasets, and querying broadly across multiple large datasets should identify predictors that are widely applicable. METHODS: We used camcAPP, cBioPortal, CRN, and NIH NCI GDC Data Portal to data mine publicly available large prostate cancer datasets. A test set of biomarkers was developed by identifying transcripts that had: 1) altered abundance in prostate cancer, 2) altered expression in patients with Gleason score 7 tumours and biochemical recurrence, 3) correlation of expression with time until biochemical recurrence across three datasets (Cambridge, Stockholm, MSKCC). Transcripts that met these criteria were then examined in a validation dataset (TCGA-PRAD) using univariate and multivariable models to predict biochemical recurrence in patients with Gleason score 7 tumours. RESULTS: Twenty transcripts met the test criteria, and 12 were validated in TCGA-PRAD Gleason score 7 patients. Ten of these transcripts remained prognostic in Gleason score 3 + 4 = 7, a sub-group of Gleason score 7 patients typically considered at a lower risk for poor outcome and often not targeted for aggressive management. All transcripts positively associated with recurrence encode or regulate mitosis and cell cycle-related proteins. The top performer was BUB1, one of four key MIR145-3P microRNA targets upregulated in hormone-sensitive as well as castration-resistant PCa. SRD5A2 converts testosterone to its more active form and was negatively associated with biochemical recurrence. CONCLUSIONS: Unbiased mining of large patient datasets identified 12 transcripts that independently predicted disease recurrence risk in Gleason score 7 prostate cancer. The mitosis and cell cycle proteins identified are also implicated in progression to castration-resistant prostate cancer, revealing a pivotal role for loss of cell cycle control in the latter. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12894-018-0433-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-07 /pmc/articles/PMC6322345/ /pubmed/30616540 http://dx.doi.org/10.1186/s12894-018-0433-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Johnston, Wendy L.
Catton, Charles N.
Swallow, Carol J.
Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer
title Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer
title_full Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer
title_fullStr Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer
title_full_unstemmed Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer
title_short Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer
title_sort unbiased data mining identifies cell cycle transcripts that predict non-indolent gleason score 7 prostate cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6322345/
https://www.ncbi.nlm.nih.gov/pubmed/30616540
http://dx.doi.org/10.1186/s12894-018-0433-5
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