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A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients
Microscopy image data of human cancers provide detailed phenotypes of spatially and morphologically intact tissues at single-cell resolution, thus complementing large-scale molecular analyses, e.g., next generation sequencing or proteomic profiling. Here we describe a high-resolution tissue microarr...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5349242/ https://www.ncbi.nlm.nih.gov/pubmed/28291248 http://dx.doi.org/10.1038/sdata.2017.14 |
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author | Zhong, Qing Guo, Tiannan Rechsteiner, Markus Rüschoff, Jan H. Rupp, Niels Fankhauser, Christian Saba, Karim Mortezavi, Ashkan Poyet, Cédric Hermanns, Thomas Zhu, Yi Moch, Holger Aebersold, Ruedi Wild, Peter J. |
author_facet | Zhong, Qing Guo, Tiannan Rechsteiner, Markus Rüschoff, Jan H. Rupp, Niels Fankhauser, Christian Saba, Karim Mortezavi, Ashkan Poyet, Cédric Hermanns, Thomas Zhu, Yi Moch, Holger Aebersold, Ruedi Wild, Peter J. |
author_sort | Zhong, Qing |
collection | PubMed |
description | Microscopy image data of human cancers provide detailed phenotypes of spatially and morphologically intact tissues at single-cell resolution, thus complementing large-scale molecular analyses, e.g., next generation sequencing or proteomic profiling. Here we describe a high-resolution tissue microarray (TMA) image dataset from a cohort of 71 prostate tissue samples, which was hybridized with bright-field dual colour chromogenic and silver in situ hybridization probes for the tumour suppressor gene PTEN. These tissue samples were digitized and supplemented with expert annotations, clinical information, statistical models of PTEN genetic status, and computer source codes. For validation, we constructed an additional TMA dataset for 424 prostate tissues, hybridized with FISH probes for PTEN, and performed survival analysis on a subset of 339 radical prostatectomy specimens with overall, disease-specific and recurrence-free survival (maximum 167 months). For application, we further produced 6,036 image patches derived from two whole slides. Our curated collection of prostate cancer data sets provides reuse potential for both biomedical and computational studies. |
format | Online Article Text |
id | pubmed-5349242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53492422017-03-17 A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients Zhong, Qing Guo, Tiannan Rechsteiner, Markus Rüschoff, Jan H. Rupp, Niels Fankhauser, Christian Saba, Karim Mortezavi, Ashkan Poyet, Cédric Hermanns, Thomas Zhu, Yi Moch, Holger Aebersold, Ruedi Wild, Peter J. Sci Data Data Descriptor Microscopy image data of human cancers provide detailed phenotypes of spatially and morphologically intact tissues at single-cell resolution, thus complementing large-scale molecular analyses, e.g., next generation sequencing or proteomic profiling. Here we describe a high-resolution tissue microarray (TMA) image dataset from a cohort of 71 prostate tissue samples, which was hybridized with bright-field dual colour chromogenic and silver in situ hybridization probes for the tumour suppressor gene PTEN. These tissue samples were digitized and supplemented with expert annotations, clinical information, statistical models of PTEN genetic status, and computer source codes. For validation, we constructed an additional TMA dataset for 424 prostate tissues, hybridized with FISH probes for PTEN, and performed survival analysis on a subset of 339 radical prostatectomy specimens with overall, disease-specific and recurrence-free survival (maximum 167 months). For application, we further produced 6,036 image patches derived from two whole slides. Our curated collection of prostate cancer data sets provides reuse potential for both biomedical and computational studies. Nature Publishing Group 2017-03-14 /pmc/articles/PMC5349242/ /pubmed/28291248 http://dx.doi.org/10.1038/sdata.2017.14 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse. |
spellingShingle | Data Descriptor Zhong, Qing Guo, Tiannan Rechsteiner, Markus Rüschoff, Jan H. Rupp, Niels Fankhauser, Christian Saba, Karim Mortezavi, Ashkan Poyet, Cédric Hermanns, Thomas Zhu, Yi Moch, Holger Aebersold, Ruedi Wild, Peter J. A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients |
title | A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients |
title_full | A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients |
title_fullStr | A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients |
title_full_unstemmed | A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients |
title_short | A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients |
title_sort | curated collection of tissue microarray images and clinical outcome data of prostate cancer patients |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5349242/ https://www.ncbi.nlm.nih.gov/pubmed/28291248 http://dx.doi.org/10.1038/sdata.2017.14 |
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