A Maximum Likelihood Approach for Non-Invasive Cancer Diagnosis Using Methylation Profiling of Cell-Free DNA from Blood

• Activity Number: 36 - Diagnostic, Prognostic, and Predictive Genomic Biomarkers for Cancer
• Type: Contributed
• Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Carol Sun* and Wenyuan Li
• Companies: Oak Park High School and UCLA
• Keywords: maximum likelihood estimation ; classification ; simulation ; cancer ; methylation ; cell-free DNA

Abstract

Accurate cancer diagnosis is essential for the treatment and survival of the patient. Tumor DNA differs from normal DNA in their methylation patterns in many CpG sites or CpG-rich regions, and DNA from tumor cells can be released into the circulating blood. Thus, the tumor-derived cell-free DNA can be detected in the patient's blood and, therefore, it is possible to use the methylation data of the blood samples for cancer diagnosis. We design a maximum likelihood approach and a corresponding computational method to estimate the fraction of tumor-derived cell-free DNA in blood samples using methylation sequencing data. Through simulations, we study the effects of sequencing depth and fraction of tumor-derived cell-free DNA on the estimation accuracy. Applying our method to real blood samples of normal individuals and cancer patients, we show that the estimated fraction of tumor-derived cell-free DNA can separate normal and cancer patients well. Our method also shows the significant decrease of the fraction of tumor DNA after surgery. Overall, our method provides an effective approach for cancer diagnosis using blood samples.