Data-based clustering in prediction of cervical cancer DNA methylation using pan-cancer genetic and clinical data

TL;DR

This paper introduces a new method to predict DNA methylation in cervical cancer by clustering patients across cancers and races, using genetic and clinical data.

Contribution

The novel framework uses data-based clustering to improve DNA methylation prediction accuracy across cancer types and races.

Findings

The proposed framework outperforms regression and naive estimates in predicting DNA methylation mixed effects.

Clustering patterns across cancers and races were identified using The Cancer Genome Atlas data.

The method leverages shared random effects to enhance prediction accuracy for underrepresented populations.

Abstract

Understanding the role of DNA methylation in oncogenesis, diagnosis, and treatment requires data sufficient in size and accuracy, but current epigenetic data is limited, especially for population groups underrepresented in research. We propose a framework for generating highly accurate DNA methylation predictions using classified mixed model prediction, incorporating a step to cluster patients into cross-cancer and cross-race groups.

Simulations show our framework more accurately predicts underlying mixed effects compared to regression prediction and naive estimates, extending previous work to the case where clusters are estimated from the data. We illustrate this framework using data from The Cancer Genome Atlas, uncovering clustering patterns and generating DNA methylation predictions for further analysis. Our work demonstrates how shared random effects can be leveraged to borrow strength across observations with similar methylation patterns.

The methods are implemented in R and available at: https://github.com/nidhipai/dnam_cmmp.

Figures