Tissue of Origin Detection for Cancer Tumor Using Low-depth CfDNA Samples Through Combination of Tumor-specific Methylation Atlas and Genome-wide Methylation Density in Graph Convolutional Neural Networks

Overview

Journal J Transl Med

Publisher Biomed Central

Specialties Biomedical Engineering
General Medicine

Date 2024 Jul 3

PMID 38961476

Authors

Trong Hieu Nguyen

Nhu Nhat Tan Doan

Trung Hieu Tran

Le Anh Khoa Huynh

Phuoc Loc Doan

Thi Hue Hanh Nguyen

Van Thien Chi Nguyen

Giang Thi Huong Nguyen

Hoai-Nghia Nguyen

Hoa Giang

Le Son Tran

Minh Duy Phan

Affiliations

Soon will be listed here.

Abstract

Background: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples.

Methods: We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA.

Results: Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples.

Conclusions: In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

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