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eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood

With the technology development on detecting circulating tumor cells (CTCs) and cell-free DNAs (cfDNAs) in blood, serum, and plasma, non-invasive diagnosis of cancer becomes promising. A few studies reported good correlations between signals from tumor tissues and CTCs or cfDNAs, making it possible...

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Detalles Bibliográficos
Autores principales: Zou, Jinfeng, Wang, Edwin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414714/
https://www.ncbi.nlm.nih.gov/pubmed/28389380
http://dx.doi.org/10.1016/j.gpb.2017.01.004
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author Zou, Jinfeng
Wang, Edwin
author_facet Zou, Jinfeng
Wang, Edwin
author_sort Zou, Jinfeng
collection PubMed
description With the technology development on detecting circulating tumor cells (CTCs) and cell-free DNAs (cfDNAs) in blood, serum, and plasma, non-invasive diagnosis of cancer becomes promising. A few studies reported good correlations between signals from tumor tissues and CTCs or cfDNAs, making it possible to detect cancers using CTCs and cfDNAs. However, the detection cannot tell which cancer types the person has. To meet these challenges, we developed an algorithm, eTumorType, to identify cancer types based on copy number variations (CNVs) of the cancer founding clone. eTumorType integrates cancer hallmark concepts and a few computational techniques such as stochastic gradient boosting, voting, centroid, and leading patterns. eTumorType has been trained and validated on a large dataset including 18 common cancer types and 5327 tumor samples. eTumorType produced high accuracies (0.86–0.96) and high recall rates (0.79–0.92) for predicting colon, brain, prostate, and kidney cancers. In addition, relatively high accuracies (0.78–0.92) and recall rates (0.58–0.95) have also been achieved for predicting ovarian, breast luminal, lung, endometrial, stomach, head and neck, leukemia, and skin cancers. These results suggest that eTumorType could be used for non-invasive diagnosis to determine cancer types based on CNVs of CTCs and cfDNAs.
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spelling pubmed-54147142017-05-10 eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood Zou, Jinfeng Wang, Edwin Genomics Proteomics Bioinformatics Method With the technology development on detecting circulating tumor cells (CTCs) and cell-free DNAs (cfDNAs) in blood, serum, and plasma, non-invasive diagnosis of cancer becomes promising. A few studies reported good correlations between signals from tumor tissues and CTCs or cfDNAs, making it possible to detect cancers using CTCs and cfDNAs. However, the detection cannot tell which cancer types the person has. To meet these challenges, we developed an algorithm, eTumorType, to identify cancer types based on copy number variations (CNVs) of the cancer founding clone. eTumorType integrates cancer hallmark concepts and a few computational techniques such as stochastic gradient boosting, voting, centroid, and leading patterns. eTumorType has been trained and validated on a large dataset including 18 common cancer types and 5327 tumor samples. eTumorType produced high accuracies (0.86–0.96) and high recall rates (0.79–0.92) for predicting colon, brain, prostate, and kidney cancers. In addition, relatively high accuracies (0.78–0.92) and recall rates (0.58–0.95) have also been achieved for predicting ovarian, breast luminal, lung, endometrial, stomach, head and neck, leukemia, and skin cancers. These results suggest that eTumorType could be used for non-invasive diagnosis to determine cancer types based on CNVs of CTCs and cfDNAs. Elsevier 2017-04 2017-04-04 /pmc/articles/PMC5414714/ /pubmed/28389380 http://dx.doi.org/10.1016/j.gpb.2017.01.004 Text en © 2017 Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Method
Zou, Jinfeng
Wang, Edwin
eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood
title eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood
title_full eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood
title_fullStr eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood
title_full_unstemmed eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood
title_short eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood
title_sort etumortype, an algorithm of discriminating cancer types for circulating tumor cells or cell-free dnas in blood
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414714/
https://www.ncbi.nlm.nih.gov/pubmed/28389380
http://dx.doi.org/10.1016/j.gpb.2017.01.004
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