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.
Highlights
Cancer cells often display aberrant DNA methylation patterns, such as hypermethylation of the promoter regions of tumor suppressor genes and pervasive hypomethylation of intergenic regions [1,2,3,4,5]
The results show that CancerLocator can achieve a Pearson’s correlation coefficient (PCC) of 0.975 between the predicted and true proportions of circulating tumor DNA (ctDNA), and an error rate of 0.078 for the classification of non-cancer and tumor types
We chose to focus on seven cancer types from the five organs that are generally regarded as having a high level of blood circulation
Summary
Cancer cells often display aberrant DNA methylation patterns, such as hypermethylation of the promoter regions of tumor suppressor genes and pervasive hypomethylation of intergenic regions [1,2,3,4,5]. The non-invasive nature of cfDNA methylation profiling makes it a promising strategy for general cancer screening. Current research on cfDNA-based, non-invasive cancer detection approaches falls into two classes: the development of biomarkers for a single specific cancer type; and the characterization of circulating tumor DNA (ctDNA) for general cancer detection, without trying to predict specific cancer types. Several studies have reported plasma methylation biomarkers for different types of cancers [9,10,11,12,13,14,15].
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