Efficient, cost-effective, and ultrasensitive sequencing of somatic mutations and methylation

Next-generation sequencing (NGS) has become increasingly integral to the practice of clinical oncology, where its ability to scalably examine hundreds to thousands of targets now routinely enables identification of prognostic and therapeutically actionable markers that support the practice of precision medicine. There are many applications for which it would be useful to detect and quantitate genetic and epigenetic cancer-associated variants at ultra-low levels (<1 in 10,000 or more), such as identifying drug-resistance mutations in tumors, detecting residual cancer cells after therapy, or early cancer detection. Nevertheless, standard NGS technologies are hampered by a relatively high error rate (~1 in 100bp), below which true biological variation cannot be distinguished from noise. Various methods have been proposed to bypass this issue by allowing error correction of NGS sequence reads, but such techniques are limited by technical and economic considerations, and have consequently seen little uptake in clinical use due to issues of cost-effectiveness, scalability, and practicality. Separately, there remains an unmet need for approaches that are able to sensitively assess both cancer-specific mutations and methylation profiles in a specimen using a single library preparation and sequencing run. We have recently developed a new experimental paradigm that addresses the limitations currently presented by error corrected sequencing techniques: we join the two strands of DNA from an initial template fragment into a single, covalently linked molecule. Error correction of the duplex can be performed by comparing separate reads from the two linked strands, thereby eliminating the need for redundant sequencing of template molecules. This provides robust error correction with scalability, cost-effectiveness, efficiency, and quantitative precision, and is compatible with low-to-mid output short read sequencing platforms (ie, Illumina) that are already in widespread clinical use. The current proposal will expand functionality of our approach to simultaneously detect DNA methylation patterns and cancer-associated mutations with ultrasensitivity. In our first Aim, we will develop methods and supportive bioinformatic analysis pipelines in support of this technology, and will characterize the cardinal performance metrics of the approach using reference material. In our second Aim, we will apply our technique to detect disease-associated mutations and methylation markers that identify residual tumor cells after leukemia therapy, the key prognostic variable predicting relapse. In the third Aim, we will identify cancer-associated methylation and mutation signatures in cell-free DNA from prostate cancer patients to improve cancer detection and monitoring. This work will provide information and deliverables having immediate, direct, and transformative benefit to cancer patients by improving the quality of oncology sequencing assays while imbuing them with enhanced diagnostic capabilities for the ultrasensitive detection of cancer associated mutations and epigenetic changes through the use of a single assay.