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Gene expression profiles define molecular subtypes of prostate cancer bone metastases with different outcomes and morphology traceable back to the primary tumor
Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
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2019 (English)In: Molecular Oncology, ISSN 1574-7891, E-ISSN 1878-0261, Vol. 13, no 8, p. 1763-1777Article in journal (Refereed) Published
Abstract [en]

Bone metastasis is the lethal end-stage of prostate cancer (PC), but the biology of bone metastases is poorly understood. The overall aim of this study was therefore to explore molecular variability in PC bone metastases of potential importance for therapy. Specifically, genome-wide expression profiles of bone metastases from untreated patients (n = 12) and patients treated with androgen-deprivation therapy (ADT, n = 60) were analyzed in relation to patient outcome and to morphological characteristics in metastases and paired primary tumors. Principal component analysis and unsupervised classification were used to identify sample clusters based on mRNA profiles. Clusters were characterized by gene set enrichment analysis and related to histological and clinical parameters using univariate and multivariate statistics. Selected proteins were analyzed by immunohistochemistry in metastases and matched primary tumors (n = 52) and in transurethral resected prostate (TUR-P) tissue of a separate cohort (n = 59). Three molecular subtypes of bone metastases (MetA-C) characterized by differences in gene expression pattern, morphology, and clinical behavior were identified. MetA (71% of the cases) showed increased expression of androgen receptor-regulated genes, including prostate-specific antigen (PSA), and glandular structures indicating a luminal cell phenotype. MetB (17%) showed expression profiles related to cell cycle activity and DNA damage, and a pronounced cellular atypia. MetC (12%) exhibited enriched stroma-epithelial cell interactions. MetB patients had the lowest serum PSA levels and the poorest prognosis after ADT. Combined analysis of PSA and Ki67 immunoreactivity (proliferation) in bone metastases, paired primary tumors, and TUR-P samples was able to differentiate MetA-like (high PSA, low Ki67) from MetB-like (low PSA, high Ki67) tumors and demonstrate their different prognosis. In conclusion, bone metastases from PC patients are separated based on gene expression profiles into molecular subtypes with different morphology, biology, and clinical outcome. These findings deserve further exploration with the purpose of improving treatment of metastatic PC.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 13, no 8, p. 1763-1777
Keywords [en]
bone metastasis, gene expression, gene set enrichment analysis, morphology, survival, unsupervised cluster analysis
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:umu:diva-162668DOI: 10.1002/1878-0261.12526ISI: 000478600200009PubMedID: 31162796OAI: oai:DiVA.org:umu-162668DiVA, id: diva2:1348763
Available from: 2019-09-05 Created: 2019-09-05 Last updated: 2020-01-14Bibliographically approved
In thesis
1. cancer subtype identification using cluster analysis on high-dimensional omics data
Open this publication in new window or tab >>cancer subtype identification using cluster analysis on high-dimensional omics data
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Identification and prediction of cancer subtypes are important parts in the development towards personalized medicine. By tailoring treatments, it is possible to decrease unnecessary suffering and reduce costs. Since the introduction of next generation sequencing techniques, the amount of data available for medical research has increased rapidly. The high dimensional omics data produced by various techniques requires statistical methods to transform data into information and knowledge.

All papers in this thesis are related to distinguishing of disease subtypes in patients with cancer using omics data. The high dimension and the complexity of sequencing data from tumor samples makes it necessary to pre—process the data.  We carry out comparisons of feature selection methods and clustering methods used for identification of cancer subtypes. In addition, we evaluate the effect that certain characteristics of the data have on the ability to identify cancer subtypes. The results show that no method outperforms the others in all cases and the relative ranking of methods is very dependent on the data. We also show that the benefit of receiving a more homogeneous data by analyzing genders separately can outweigh the possible drawbacks caused by smaller sample sizes. One of the major challenges when dealing with omics data from tumor samples is that the patients are generally a very heterogeneous group. Factors that lead to heterogeneity include age, gender, ethnicity and stage of disease. How big the effect size is for each of these factors might affect the ability to identify the subgroups of interest.

In omics data, the feature space is often large and how many of the features that are informative for the factors of interest will also affect the complexity of the problem. We present a novel clustering approach that can identify different clusters in different subsets of the feature space, which is applied on methylation data to create new potential biomarkers. It is shown that by combining clinical data with methylation data for patients with clear cell renal carcinoma, it is possible to improve the currently used prediction model for disease progression.  

Using unsupervised clustering techniques, we identify three molecular subtypes of prostate cancer bone metastases based on gene expression profiles. The robustness of the identified subtypes is confirmed by applying several clustering algorithms with very similar results.

 

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2020. p. 22
Series
Research report in mathematical statistics, ISSN 1653-0829 ; 70/20
Keywords
cluster analysis, cancer, classification
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-167275 (URN)978-91-7855-172-9 (ISBN)978-91-7855-173-6 (ISBN)
Public defence
2020-02-07, N460, Naturvetarhuset, Umeå, 09:15 (English)
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Supervisors
Available from: 2020-01-17 Created: 2020-01-14 Last updated: 2020-01-15Bibliographically approved

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Thysell, ElinVidman, LindaBovinder Ylitalo, ErikJernberg, EmmaCrnalic, SeadWidmark, AndersRydén, PatrikBergh, AndersWikström, Pernilla

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Thysell, ElinVidman, LindaBovinder Ylitalo, ErikJernberg, EmmaCrnalic, SeadWidmark, AndersRydén, PatrikBergh, AndersWikström, Pernilla
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PathologyDepartment of Mathematics and Mathematical StatisticsOrthopaedicsOncology
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