Dataset Information

Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA.

ABSTRACT: Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets covering 2125 patient samples of 9 cancer types and 1241 normal control samples, and also a reference dataset for background variant filtering based on 20,529 low-depth healthy samples. An external cfDNA dataset consisting of 208 cancer and 214 normal control samples is used for additional evaluation. Accuracy for cancer detection and tissue-of-origin localization is achieved using our algorithm, which incorporates cancer type-specific profiles of mutation distribution and chromatin organization in tumor tissues as model references. Our integrative model detects early-stage cancers, including those of pancreatic origin, with high sensitivity that is comparable to that of late-stage detection. Model interpretation reveals the contribution of cancer type-specific genomic and epigenomic features. Our methodologies may lay the groundwork for accurate cfDNA-based cancer diagnosis, especially at early stages.

SUBMITTER: Bae M

PROVIDER: S-EPMC10085982 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA.

Bae Mingyun M Kim Gyuhee G Lee Tae-Rim TR Ahn Jin Mo JM Park Hyunwook H Park Sook Ryun SR Song Ki Byung KB Jun Eunsung E Oh Dongryul D Lee Jeong-Won JW Park Young Sik YS Song Ki-Won KW Byeon Jeong-Sik JS Kim Bo Hyun BH Sohn Joo Hyuk JH Kim Min Hwan MH Kim Gun Min GM Chie Eui Kyu EK Kang Hyun-Cheol HC Kong Sun-Young SY Woo Sang Myung SM Lee Jeong Eon JE Ryu Jai Min JM Lee Junnam J Kim Dasom D Ki Chang-Seok CS Cho Eun-Hae EH Choi Jung Kyoon JK

Nature communications 20230410 1

Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets covering 2125 patient samples of 9 cancer types and 1241 normal control samples, and also a reference dataset for background variant filtering based on 20,529 low-depth healthy samples. An external cfDNA dataset consisting of 208 cancer and 214 normal control samples is used for additional evaluation. Accuracy for cancer d ...[more]

PMID: 37037826

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Project description:BackgroundLung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.MethodsTo address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.ResultsCancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.ConclusionsWith feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.KeypointsOur study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

Dataset Information

Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA.

Publications

Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA.

Similar Datasets

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