A harmonized resource of integrated prostate cancer clinical, -omic, and signature features
Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, der...
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Published in | Scientific data Vol. 10; no. 1; p. 430 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Nature Publishing Group
05.07.2023
Nature Publishing Group UK Nature Portfolio |
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Abstract | Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility. |
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AbstractList | Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or not standardized. Here, we present the
curatedPCaData
R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights.
curatedPCaData
is an open and accessible community resource with code made available for reproducibility. Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility. Abstract Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility. Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility. Abstract Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility. |
ArticleNumber | 430 |
Author | Soupir, Alex C Gerke, Travis Orman, Michael V Sreekanth, Varsha Creed, Jordan H Calboli, Federico C F Singaravelu, Kalaimathy Tyekucheva, Svitlana Fridley, Brooke L Colin-Leitzinger, Christelle Halkola, Anni S Costello, James C Laajala, Teemu D |
Author_xml | – sequence: 1 givenname: Teemu D orcidid: 0000-0002-7016-7354 surname: Laajala fullname: Laajala, Teemu D email: teelaa@utu.fi, teelaa@utu.fi organization: Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. teelaa@utu.fi – sequence: 2 givenname: Varsha surname: Sreekanth fullname: Sreekanth, Varsha organization: Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA – sequence: 3 givenname: Alex C orcidid: 0000-0003-1251-9179 surname: Soupir fullname: Soupir, Alex C organization: Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA – sequence: 4 givenname: Jordan H surname: Creed fullname: Creed, Jordan H organization: Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA – sequence: 5 givenname: Anni S surname: Halkola fullname: Halkola, Anni S organization: Department of Mathematics and Statistics, University of Turku, Turku, Finland – sequence: 6 givenname: Federico C F surname: Calboli fullname: Calboli, Federico C F organization: Natural Resources Institute Finland (Luke), F-31600, Jokioinen, Finland – sequence: 7 givenname: Kalaimathy surname: Singaravelu fullname: Singaravelu, Kalaimathy organization: Department of Mathematics and Statistics, University of Turku, Turku, Finland – sequence: 8 givenname: Michael V orcidid: 0000-0003-2681-0063 surname: Orman fullname: Orman, Michael V organization: Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA – sequence: 9 givenname: Christelle orcidid: 0000-0002-8118-9591 surname: Colin-Leitzinger fullname: Colin-Leitzinger, Christelle organization: Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA – sequence: 10 givenname: Travis surname: Gerke fullname: Gerke, Travis organization: Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA – sequence: 11 givenname: Brooke L orcidid: 0000-0001-7739-7956 surname: Fridley fullname: Fridley, Brooke L organization: Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA – sequence: 12 givenname: Svitlana orcidid: 0000-0002-3119-6507 surname: Tyekucheva fullname: Tyekucheva, Svitlana email: svitlana@jimmy.harvard.edu organization: Department of Data Science, Dana-Farber Cancer Institute; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. svitlana@jimmy.harvard.edu – sequence: 13 givenname: James C orcidid: 0000-0003-3158-9682 surname: Costello fullname: Costello, James C email: james.costello@cuanschutz.edu, james.costello@cuanschutz.edu organization: University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. james.costello@cuanschutz.edu |
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Snippet | Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize... Abstract Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better... Abstract Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better... |
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SubjectTerms | Androgen receptors Datasets Datasets as Topic Gene Expression Profiling Genomics Humans Hypotheses Male Meta-Analysis as Topic Metastases Prostate cancer Prostatic Neoplasms - genetics Prostatic Neoplasms - pathology Reproducibility of Results Transcriptome Transcriptomics |
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Title | A harmonized resource of integrated prostate cancer clinical, -omic, and signature features |
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