Developer

This pipeline is released by the BMAP team.

Benchmark Data

A set of human prostate cancer cell line (LNCa, PrEC) datasets that were detected using both microarray (Infinium MethylationEPIC BeadChip) and WGBS (Illumina HiSeq 2500) was used to assess the accuracy of the analysis pipeline. These original datasets were downloaded from NCBI-SRA/GEO (SRP089722, GSE86833).

Workflow

Introduction

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is growing in incidence but treatment options remain limited, particularly for late stage disease. As liver cirrhosis is the principal risk state for HCC development, markers to detect early HCC within this patient population are urgently needed. Perturbation of epigenetic marks, such as DNA methylation (5mC), is a hallmark of human cancers, including HCC. Identification of regions with consistently altered 5mC levels in circulating cell free DNA (cfDNA) during progression from cirrhosis to HCC could therefore serve as markers for development of minimally-invasive screens of early HCC diagnosis and surveillance.

Methods

To discover DNA methylation derived biomarkers of HCC in the background of liver cirrhosis, we profiled genome-wide 5mC landscapes in patient cfDNA using the Infinium HumanMethylation450k BeadChip Array. We further linked these findings to primary tissue data available from TCGA and other public sources. Using biological and statistical frameworks, we selected CpGs that robustly differentiated cirrhosis from HCC in primary tissue and cfDNA followed by validation in an additional independent cohort.

Results

We identified CpGs that segregate patients with cirrhosis, from patients with HCC within a cirrhotic liver background, through genome-wide analysis of cfDNA 5mC landscapes. Lasso regression analysis pinpointed a panel of probes in our discovery cohort that were validated in two independent datasets. A panel of five CpGs (cg04645914, cg06215569, cg23663760, cg13781744, and cg07610777) yielded area under the receiver operating characteristic (AUROC) curves of 0.9525, 0.9714, and 0.9528 in cfDNA discovery and tissue validation cohorts 1 and 2, respectively. Validation of a 5-marker panel created from combining hypermethylated and hypomethylated CpGs in an independent cfDNA set by bisulfite pyrosequencing yielded an AUROC of 0.956, compared to the discovery AUROC of 0.996.

Reference

Hlady R A, Zhao X, Pan X, et al. Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA[J]. Theranostics, 2019, 9(24): 7239.

V1.0 The code and Docker image associated with this pipeline are identified by CRS1078729588181577728 and SRS1078731771471998976, respectively.

Reproducibility

The pipeline utilizes code CRS1048606034756907008 and Docker image SRS1048608424692297728, both of which are stored in the BMAP repositories.

Usage

1. Select the application 'DNA Methylation Array Data Analysis'
2. Click on the 'Analysis' button
3. Click on the 'Raw data or Beta value'
4. Fill in the request parameters

Note: the file names should include the subfolder name and the extension name (eg. data/sample.idat).
5. Upload the compressed data in gz or rar format.

You need to compress the file which filled in “Data” into the corresponding format according to the following requirements.
a. If the data to be analyzed is not stored on our platform, only upload the data temporarily (Web) to use the analysis function. Please compress all the data to be analyzed into a single file, supported compression formats: rar, zip, gz, tar.gz.
b. If the data to be analyzed has been stored on our platform, including Data Repository Storage (DRS), or stored in the HPC@SJTU through the administrator (Local), please upload any content compressed file that conforms to these formats: rar, zip, gz, tar.gz.

An Example of Input Data (.idat):

The input data are IDAT files, representing two different color channels (red and green) prior to normalization. The basename of each IDAT file consists of three parts: slide (sentrix_ID), array (Sentrix_Position), and channel.

NOTE: The format of the input file should be strictly in accordance with the example data.

An Example of Input Data (.csv)：

① beta_matrix.csv
row = cg ID;
column = samples;
no missing values

② pheno_data.csv
column 1 = “Sample_Name”;
column 2 = “Sample_Group” (at least 2 groups);
no missing value

6. View the results