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===Quantitatively estimation to the level of cancer DNA fractions in the plasma=== Recent studies have demonstrated that epigenetic information imbedded in cfDNA has the potential for predicting tumorโs tissue-of-origin. Consistently, we found that tissue-of-origin derived methylation haplotypes were the most abundant fraction in cancer plasma. Here we asked whether a MHL-based framework and a set of targets derived from whole genome data would allow us to predict tissue-of-origin with quantifiable sensitivity and specificity, which is crucial for future clinical applications. We compiled 43 WGBS and RRBS data sets for 10 human normal tissues that have high cancer incident rate, and identified a set of 2,880 tissue-specific MHBs as the candidates. We then used these tissue-specific MHBs or subsets to predict the tissue-of-origin for the cancer plasma sample. We adopted the approach by counting the total number of tissue-specific MHBs in the plasma samples and comparing with all other tissues, in order to infer the most probable tissue-of-origin. At the cutoff of minimal 10 tissue-specific methylated haplotypes per tissue type, we observed an average 90% accuracy for mapping a data set from the primary tissue to its tissue type (Figure 5, middle). We then applied this method to the full set of plasma data from 59 cancer patients and 75 normal individuals, and achieved an average prediction accuracy of 82.8%, 88.5%, 91.2% for the plasma from colorectal cancer, lung cancer, and control plasma samples respectively with 5-fold cross-validation. For the incorrectly classified samples, we noticed that 4 out of 5 colorectal cancer plasma were from metastatic colorectal cancer patients while the fifth was in fact tubular adenoma. In the case of lung cancer, one misclassified sample came from a patient with benign fibrous tissue. Our invention demonstrate these biomarkers could be useful for tissue-of-origin mapping and other related applications.
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