Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection

Abstract

Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.

Revolutionizing Cancer Research

A new platform, AlphaLiquid® Screening, offers simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns in a single assay for early cancer detection. The platform utilizes a multi-feature cancer signature ensemble classifier (CSE) that outperforms single-feature classifiers. At 95.2% specificity, the CSE achieved 88.9% sensitivity for cancer detection, a significant improvement over individual classifiers. The CSE model also demonstrated improved accuracy in predicting the tissue of origin for detected cancers. This study highlights the potential of integrating epigenetic and genetic information to enhance the accuracy of cancer detection using cell-free DNA sequencing data.

This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.