CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA
We propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood samp...
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| Published in | Genome Biology Vol. 18; no. 1; p. 53 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
24.03.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1474-760X 1474-7596 1474-760X |
| DOI | 10.1186/s13059-017-1191-5 |
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| Abstract | We propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. CancerLocator outperforms two established multi-class classification methods on simulations and real data, even with the low proportion of tumor-derived DNA in the cell-free DNA scenarios. CancerLocator also achieves promising results on patient plasma samples with low DNA methylation sequencing coverage. |
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| AbstractList | We propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. CancerLocator outperforms two established multi-class classification methods on simulations and real data, even with the low proportion of tumor-derived DNA in the cell-free DNA scenarios. CancerLocator also achieves promising results on patient plasma samples with low DNA methylation sequencing coverage. We propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. CancerLocator outperforms two established multi-class classification methods on simulations and real data, even with the low proportion of tumor-derived DNA in the cell-free DNA scenarios. CancerLocator also achieves promising results on patient plasma samples with low DNA methylation sequencing coverage.We propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. CancerLocator outperforms two established multi-class classification methods on simulations and real data, even with the low proportion of tumor-derived DNA in the cell-free DNA scenarios. CancerLocator also achieves promising results on patient plasma samples with low DNA methylation sequencing coverage. |
| ArticleNumber | 53 |
| Author | Lee, Gina Krysan, Kostyantyn Wang, Wei Alber, Frank Sun, Fengzhu Zhou, Xianghong Jasmine Park, Stacy Yu, Min Zhou, Yonggang Li, Wenyuan Dubinett, Steven M. Li, Qingjiao Chen, Quan Grimes, Brandon Kang, Shuli |
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| SubjectTerms | Algorithms Animal Genetics and Genomics Bioinformatics Biomarkers Biomedical and Life Sciences blood sampling Breast cancer Cancer Computer Simulation CpG Islands Deoxyribonucleic acid DNA DNA Methylation DNA sequencing DNA, Neoplasm - blood DNA, Neoplasm - genetics Epigenesis, Genetic Epigenomics - methods Evolutionary Biology Gene expression Genomes Human Genetics Humans Life Sciences Medical diagnosis Medical screening Method Methods Microbial Genetics and Genomics Models, Statistical neoplasms Neoplasms - diagnosis Neoplasms - genetics Pancreatic cancer Patients Plant Genetics and Genomics prediction Reproducibility of Results Tumors Workflow |
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| Title | CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA |
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