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curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource
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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882125/ https://www.ncbi.nlm.nih.gov/pubmed/36711769 http://dx.doi.org/10.1101/2023.01.17.524403 |
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author | Laajala, Teemu D Sreekanth, Varsha Soupir, Alex Creed, Jordan Calboli, Federico CF Singaravelu, Kalaimathy Orman, Michael Colin-Leitzinger, Christelle Gerke, Travis Fidley, Brooke L. Tyekucheva, Svitlana Costello, James C |
author_facet | Laajala, Teemu D Sreekanth, Varsha Soupir, Alex Creed, Jordan Calboli, Federico CF Singaravelu, Kalaimathy Orman, Michael Colin-Leitzinger, Christelle Gerke, Travis Fidley, Brooke L. Tyekucheva, Svitlana Costello, James C |
author_sort | Laajala, Teemu D |
collection | PubMed |
description | 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 unstandardized. 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. |
format | Online Article Text |
id | pubmed-9882125 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-98821252023-01-28 curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource Laajala, Teemu D Sreekanth, Varsha Soupir, Alex Creed, Jordan Calboli, Federico CF Singaravelu, Kalaimathy Orman, Michael Colin-Leitzinger, Christelle Gerke, Travis Fidley, Brooke L. Tyekucheva, Svitlana Costello, James C bioRxiv Article 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 unstandardized. 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. Cold Spring Harbor Laboratory 2023-01-19 /pmc/articles/PMC9882125/ /pubmed/36711769 http://dx.doi.org/10.1101/2023.01.17.524403 Text en https://creativecommons.org/licenses/by-nd/4.0/This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Laajala, Teemu D Sreekanth, Varsha Soupir, Alex Creed, Jordan Calboli, Federico CF Singaravelu, Kalaimathy Orman, Michael Colin-Leitzinger, Christelle Gerke, Travis Fidley, Brooke L. Tyekucheva, Svitlana Costello, James C curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource |
title | curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource |
title_full | curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource |
title_fullStr | curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource |
title_full_unstemmed | curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource |
title_short | curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource |
title_sort | curatedpcadata: integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882125/ https://www.ncbi.nlm.nih.gov/pubmed/36711769 http://dx.doi.org/10.1101/2023.01.17.524403 |
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