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Prediction of Primary Tumors in Cancers of Unknown Primary

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to identify the location of the primary tumor. CUPs account for 3–5% of cancer cases. Using molecular data to determine the location of the primary tumor in such cases can help doctors make the right tr...

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Detalles Bibliográficos
Autores principales: Søndergaard, Dan, Nielsen, Svend, Pedersen, Christian N.S., Besenbacher, Søren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042823/
https://www.ncbi.nlm.nih.gov/pubmed/28686574
http://dx.doi.org/10.1515/jib-2017-0013
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author Søndergaard, Dan
Nielsen, Svend
Pedersen, Christian N.S.
Besenbacher, Søren
author_facet Søndergaard, Dan
Nielsen, Svend
Pedersen, Christian N.S.
Besenbacher, Søren
author_sort Søndergaard, Dan
collection PubMed
description A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to identify the location of the primary tumor. CUPs account for 3–5% of cancer cases. Using molecular data to determine the location of the primary tumor in such cases can help doctors make the right treatment choice and thus improve the clinical outcome. In this paper, we present a new method for predicting the location of the primary tumor using gene expression data: locating cancers of unknown primary (LoCUP). The method models the data as a mixture of normal and tumor cells and thus allows correct classification even in impure samples, where the tumor biopsy is contaminated by a large fraction of normal cells. We find that our method provides a significant increase in classification accuracy (95.8% over 90.8%) on simulated low-purity metastatic samples and shows potential on a small dataset of real metastasis samples with known origin.
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spelling pubmed-60428232019-01-28 Prediction of Primary Tumors in Cancers of Unknown Primary Søndergaard, Dan Nielsen, Svend Pedersen, Christian N.S. Besenbacher, Søren J Integr Bioinform Research Articles A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to identify the location of the primary tumor. CUPs account for 3–5% of cancer cases. Using molecular data to determine the location of the primary tumor in such cases can help doctors make the right treatment choice and thus improve the clinical outcome. In this paper, we present a new method for predicting the location of the primary tumor using gene expression data: locating cancers of unknown primary (LoCUP). The method models the data as a mixture of normal and tumor cells and thus allows correct classification even in impure samples, where the tumor biopsy is contaminated by a large fraction of normal cells. We find that our method provides a significant increase in classification accuracy (95.8% over 90.8%) on simulated low-purity metastatic samples and shows potential on a small dataset of real metastasis samples with known origin. De Gruyter 2017-07-07 /pmc/articles/PMC6042823/ /pubmed/28686574 http://dx.doi.org/10.1515/jib-2017-0013 Text en ©2017, Dan Søndergaard, et al., published by De Gruyter, Berlin/Boston http://creativecommons.org/licenses/by-nc-nd/3.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
spellingShingle Research Articles
Søndergaard, Dan
Nielsen, Svend
Pedersen, Christian N.S.
Besenbacher, Søren
Prediction of Primary Tumors in Cancers of Unknown Primary
title Prediction of Primary Tumors in Cancers of Unknown Primary
title_full Prediction of Primary Tumors in Cancers of Unknown Primary
title_fullStr Prediction of Primary Tumors in Cancers of Unknown Primary
title_full_unstemmed Prediction of Primary Tumors in Cancers of Unknown Primary
title_short Prediction of Primary Tumors in Cancers of Unknown Primary
title_sort prediction of primary tumors in cancers of unknown primary
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042823/
https://www.ncbi.nlm.nih.gov/pubmed/28686574
http://dx.doi.org/10.1515/jib-2017-0013
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