Kang Shuli, Li Qingjiao, Chen Quan, Zhou Yonggang, Park Stacy, Lee Gina, Grimes Brandon, Krysan Kostyantyn, Yu Min, Wang Wei, Alber Frank, Sun Fengzhu, Dubinett Steven M, Li Wenyuan, Zhou Xianghong Jasmine
Molecular and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA.
Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Genome Biol. 2017 Mar 24;18(1):53. doi: 10.1186/s13059-017-1191-5.
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.
我们提出了一种概率方法——癌症定位器（CancerLocator），它不仅通过确定肿瘤的存在，还通过确定肿瘤的位置来利用游离DNA的诊断潜力。癌症定位器利用全基因组DNA甲基化数据，同时推断血液样本中肿瘤来源的游离DNA的比例和组织来源。在模拟数据和真实数据上，癌症定位器的表现优于两种既定的多类分类方法，即使在游离DNA情况下肿瘤来源DNA比例较低的情况下也是如此。在DNA甲基化测序覆盖率较低的患者血浆样本上，癌症定位器也取得了有前景的结果。
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