DNA methylation signatures predicting liver metastasis of colorectal cancer: A proof-of-concept pilot study.

Abstract

e16080 Background: Liver metastasis (LIM) is the leading cause of death in colorectal cancer (CRC) patients. Early detection of LIM may improve outcome in CRC patients. The aim of this study was to evaluate the feasibility of predicting LIM of CRC using methylation profiles. Methods: We performed Roche targeted (~5.5 million methylation sites) bisulfite sequencing of matched primary, metastatic and their adjacent normal tissue samples from 5 CRC patients with LIM, 5 patients with lung metastasis (LUM) and 8 patients without metastasis in the training cohort (n = 48 samples). Differential methylation regions (DMR) of LUM were identified and a predictive model was developed. The model was further validated in primary tumor sample from nine patients (6 with LIM). Results: By comparing primary tumor vs adjacent normal tissues and metastatic tumor vs adjacent normal tissues in CRC patients with LIM, we identified 28954 common DMRs which indicating the methylation characteristic of CRC with LIM. Similarly, 16187 DMRs were identified in patients with LUM. 9179 DMRs are shared in both LIM and LUM comparisons which should be the common characteristic of CRC tumor tissue regardless of the location of metastasis. 7008 DMRs are LUM specific and 19775 DMRs are LIM specific. In order to predict LIM in primary, early changes in LIM specific DMRs should be identified. Hence, we further selected 4134 DMRs by chossing significantly differentically methylated regions between LIM primary tissues and LUM primary tissues. To increase the ability of distinguishing LIM from other normal tissues and non-matastasis CRC tumors, 1215 DMRs were finally selected which also showed increasing or decreasing trend of methylation level through the progression of CRC. The final 1215 biomarkers were used to construct a random forest model using methlylation profile of 5 CRC patients with LIM as positive training data and 5 CRC patients with LUM as well as 8 patients without metastasis as negative training data. Through the feature recursive elimination method, one methylation site (chr8.72468901-72469000) was identified with ROC of 0.9 in the training dataset. The predictive model was validated in an independent dataset which is composed of 6 patients with LIM and 3 patients without metastasis, and achieved an AUC of 0.87. Conclusions: Our findings demonstrate the utility of methylation biomarkers for the molecular characterization of metastatic precursors, with implications for prediction and early detection of liver metastasis in CRC.