Single-molecule nanopore-based identification of methylome signatures in cfDNA for early colorectal cancer detection

The detection and characterization of cell-free DNA (cfDNA) is increasingly being used to detect cancer – this modality is frequently referred to as a liquid biopsy. Epigenetic characterization of cfDNA is an emerging approach for sensitive detection and quantification of cancer burden. Trends in cancer growth are evident from cfDNA among patients with advandced and metastatic cancer; however, there is a significant translational need for the development of early-stage cancer or even pre-cancer detection assays via liquid biopsy. Malignant tumor cells shed DNA into the bloodstream of cancer patients as cfDNA, commonly in the form of nucleosome-sized fragments. There is broad interest in cfDNA methylation as a cancer biomarker modality, ranging from targeted biomarker panels to whole-genome characterization of cfDNA methylomes. For detecting 5mC methylation, cfDNA is currently processed with bisulfite or enzymatic conversion of unmodified cytosines into uracils, which is detected by short-read sequencers. This approach introduces biases such as GC skews, oxidative DNA damage, PCR amplification bias, and alignment artifacts. Characterizing cfDNA methylomes from patients remains challenging, particularly with conventional sequencing approaches. We recently demonstrated a novel approach for single-molecule methylation analysis of cfDNA that overcomes these issues. We developed a nanopore-based sequencing approach for efficiently characterizing methylation profiles from the cfDNA of cancer patients. The passage of methylated DNA through the nanopore generates a unique electrical signal compared to unmodified DNA without cytosine conversion, which can then be classified with machine learning algorithms. We generated up to hundreds of millions of reads per cfDNA sample from colorectal cancer patients, with single nanograms or less of analyte per patient. In this project, we seek to extend our work to (1) characterize the early-stage colorectal cancer cfDNA methylome landscape, and (2) to develop a classification model for early detection of colorectal cancer. Using cfDNA samples from patients at the Stanford Cancer Institute (SCI) and the PLCO clinical trial, we will generate cfDNA methylomes that will be the basis of biomarker signatures for colorectal cancer detection. In Aim 1, we will derive cfDNA methylome profiles from confirmed and pre-diagnostic CRC, and characterize how cfDNA methylomes are affected by tumor subtype and stage. We will also deconvolute cfDNA using matched primary tumor and immune cell references. In Aim 2, using sequenced cfDNA we will build a machine learning model that will determine statistically significant changes in cfDNA methylation between cancer patients and healthy controls. The machine learning model will also quantify tumor burden and whether it is likely that a sample is indicative of cancer. We will use the SCI and PLCO patients as independent cohorts to perform training and validation.