A Bayesian model for identifying cancer subtypes from paired methylation profiles

Abstract

Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.

Keywords: Bayesian method; cancer subtyping; clustering; paired methylation profiles.

Figure 1

A flowchart to illustrate the BaySub algorithm. The methylation status of the reference normal cell is represented by a binary vector , and the rate of methylation is set to be . Based on this reference methylation status of normal tissue, a binary underlying methylation profile could be generated with mutation probability . Suppose there are different paths changing the methylation status from normal tissues to become tumor tissues, and the mutation rates is fixed at . After obtaining the membership of the cancer subtypes , the tumor methylation profile can be generated from normal methylation profile , the membership and the mutation paths . Therefore, the M-values of normal tissues follow normal distribution according to its methylation profile , and corresponding M-values of tumor tissues follow normal distribution according to its methylation profile .

Figure 2

The traceplots of key variables for simulation datasets. The whole MCMC samples are shown in the left figures, and the burn-in periods are illustrated in the right figures.

Figure 3

Heatmap of methylation signatures captured by BaySub algorithm on simulation datasets.

Figure 4

The plots of AIC (left) and BIC (right) for different numbers of subtypes in Experiment 2 (dashed line) and Experiment 3 (solid line). The horizontal axis represents the assumed value of the number of subtypes , and the vertical axis represents the corresponding values of AIC or BIC. The true value of for red dashed line is 3, and that for green solid line is 4.

Figure 5

The traceplots of key variables for real datasets. The whole MCMC samples are shown in the left figures, and the burn-in periods are illustrated in the right figures.