Maximum efficiency sequencing using nuclease-based mutation enrichment and digital barcodes


Year of Award:
2017
Award Type:
R33
Project Number:
CA217652-01
RFA Number:
RFA-CA-16-002
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
MAKRIGIORGOS, G MIKE
Other PI or Project Leader:
Not Applicable
Institution:
DANA-FARBER CANCER INST
Project Summary Low-level tumor somatic DNA mutations can have profound implications for development of metastasis, prognosis, choice of treatment, follow-up or early cancer detection strategies. Unless they are effectively detected, these low-level mutations can misinform patient management decisions or become missed opportunities for personalized medicine. Next generation sequencing (NGS) technologies can effectively reveal prevalent somatic mutations, yet they 'lose steam' when it comes to detecting low-level DNA mutations in tumors with clonal heterogeneity, or in bodily fluids, and their integration with clinical practice is problematic. For mutations at allelic ratio of ~2-5% or less, NGS generates excessive false positives (‘noise’) independent of sequencing depth and hinders personalized clinical decisions based on mutational profiling. Recent adaptations of NGS to detect rare mutations using random barcoding strategies may overcome the noise but invariably diminish its high throughput capability and increase costs. We recently developed NaMe-PrO, a simple and powerful technology to eliminate wild-type sequences from large numbers of targets in genomic DNA. NaME-PrO utilizes a nuclease guided by probes to thousands of DNA targets, to render WT sequences non-amplifiable thereby allowing mutation–containing sequences to amplify and be sequenced as if they were clonal mutations. This R33 proposes to develop quantitative NaME- PrO (qNaME-PrO), which combines NaME-PrO with a novel use of molecular barcoding, to provide strict enumeration of original mutation abundance for all mutant sequences following their enrichment. Thereby converting rare mutations to high abundance mutations, boosting confidence in their detection and circumventing the need for repeated and wasteful sequence reads during NGS. The method creates the potential for massively parallel mutation enrichment prior to sequencing and engenders a new paradigm whereby rare mutations do not require deep sequencing for their detection. The R33 (Aims 1&2) will optimize and develop qNaME-PrO panels to cover all known mutation hotspots and full length exons in tumor suppressor genes and oncogenes relevant to lung cancer. In Aim 3 the method will be field-tested in a compilation of longitudinally collected plasma samples from patients undergoing radio-chemo-therapy. Being able to extract ‘the mutated portion of a large genomic target’ from a mixed clinical sample prior to downstream analysis will translate to a major boost in the speed, accuracy and cost of sequencing low- prevalence mutations in heterogeneous tumors and bodily fluids and will accelerate clinical application of NGS for cancer diagnosis, prognosis and management. Therefore relevance to Public Health is high.