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PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery
We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for r...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026194/ https://www.ncbi.nlm.nih.gov/pubmed/35475145 http://dx.doi.org/10.1093/narcan/zcac014 |
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author | Koc, Soner Lloyd, Michael W Grover, Jeffrey W Xiao, Nan Seepo, Sara Subramanian, Sai Lakshmi Ray, Manisha Frech, Christian DiGiovanna, John Webster, Phillip Neuhauser, Steven Srivastava, Anuj Woo, Xing Yi Sanderson, Brian J White, Brian Lott, Paul Dobrolecki, Lacey E Dowst, Heidi Evrard, Yvonne A Wallace, Tiffany A Moscow, Jeffrey A Doroshow, James H Mitsiades, Nicholas Kaochar, Salma Pan, Chong-xian Chen, Moon S Carvajal-Carmona, Luis Welm, Alana L Welm, Bryan E Lewis, Michael T Govindan, Ramaswamy Ding, Li Li, Shunqiang Herlyn, Meenhard Davies, Michael A Roth, Jack Meric-Bernstam, Funda Robinson, Peter N Bult, Carol J Davis-Dusenbery, Brandi Dean, Dennis A Chuang, Jeffrey H |
author_facet | Koc, Soner Lloyd, Michael W Grover, Jeffrey W Xiao, Nan Seepo, Sara Subramanian, Sai Lakshmi Ray, Manisha Frech, Christian DiGiovanna, John Webster, Phillip Neuhauser, Steven Srivastava, Anuj Woo, Xing Yi Sanderson, Brian J White, Brian Lott, Paul Dobrolecki, Lacey E Dowst, Heidi Evrard, Yvonne A Wallace, Tiffany A Moscow, Jeffrey A Doroshow, James H Mitsiades, Nicholas Kaochar, Salma Pan, Chong-xian Chen, Moon S Carvajal-Carmona, Luis Welm, Alana L Welm, Bryan E Lewis, Michael T Govindan, Ramaswamy Ding, Li Li, Shunqiang Herlyn, Meenhard Davies, Michael A Roth, Jack Meric-Bernstam, Funda Robinson, Peter N Bult, Carol J Davis-Dusenbery, Brandi Dean, Dennis A Chuang, Jeffrey H |
author_sort | Koc, Soner |
collection | PubMed |
description | We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI’s Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. |
format | Online Article Text |
id | pubmed-9026194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-90261942022-04-25 PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery Koc, Soner Lloyd, Michael W Grover, Jeffrey W Xiao, Nan Seepo, Sara Subramanian, Sai Lakshmi Ray, Manisha Frech, Christian DiGiovanna, John Webster, Phillip Neuhauser, Steven Srivastava, Anuj Woo, Xing Yi Sanderson, Brian J White, Brian Lott, Paul Dobrolecki, Lacey E Dowst, Heidi Evrard, Yvonne A Wallace, Tiffany A Moscow, Jeffrey A Doroshow, James H Mitsiades, Nicholas Kaochar, Salma Pan, Chong-xian Chen, Moon S Carvajal-Carmona, Luis Welm, Alana L Welm, Bryan E Lewis, Michael T Govindan, Ramaswamy Ding, Li Li, Shunqiang Herlyn, Meenhard Davies, Michael A Roth, Jack Meric-Bernstam, Funda Robinson, Peter N Bult, Carol J Davis-Dusenbery, Brandi Dean, Dennis A Chuang, Jeffrey H NAR Cancer Cancer Data Resource We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI’s Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. Oxford University Press 2022-04-22 /pmc/articles/PMC9026194/ /pubmed/35475145 http://dx.doi.org/10.1093/narcan/zcac014 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of NAR Cancer. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Cancer Data Resource Koc, Soner Lloyd, Michael W Grover, Jeffrey W Xiao, Nan Seepo, Sara Subramanian, Sai Lakshmi Ray, Manisha Frech, Christian DiGiovanna, John Webster, Phillip Neuhauser, Steven Srivastava, Anuj Woo, Xing Yi Sanderson, Brian J White, Brian Lott, Paul Dobrolecki, Lacey E Dowst, Heidi Evrard, Yvonne A Wallace, Tiffany A Moscow, Jeffrey A Doroshow, James H Mitsiades, Nicholas Kaochar, Salma Pan, Chong-xian Chen, Moon S Carvajal-Carmona, Luis Welm, Alana L Welm, Bryan E Lewis, Michael T Govindan, Ramaswamy Ding, Li Li, Shunqiang Herlyn, Meenhard Davies, Michael A Roth, Jack Meric-Bernstam, Funda Robinson, Peter N Bult, Carol J Davis-Dusenbery, Brandi Dean, Dennis A Chuang, Jeffrey H PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery |
title | PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery |
title_full | PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery |
title_fullStr | PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery |
title_full_unstemmed | PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery |
title_short | PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery |
title_sort | pdxnet portal: patient-derived xenograft model, data, workflow and tool discovery |
topic | Cancer Data Resource |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026194/ https://www.ncbi.nlm.nih.gov/pubmed/35475145 http://dx.doi.org/10.1093/narcan/zcac014 |
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