The goal of this application is to further develop, optimize, and validate a rapid, cost-effective, sensitive, and specific method to detect measurable residual disease (MRD) in the research and clinical settings. The ability to quickly and accurately monitor cancer is of obvious clinical utility and could be used to predict early disease recurrence and monitor treatment efficacy. DNA mutations present in tumor cells provide a unique genetic fingerprint that distinguishes cancer cells from normal cells. Current sequencing assays are able to detect tumor burden based on the abundance of a variant allele (i.e., mutation) present in a pool of cells. However, current sequencing approaches have a low sensitivity to detect rare variants largely due to sequencing errors, low coverage, and insufficient sampling of tumor genomes during sequencing (i.e., low library efficiency). Incorporation of molecular barcodes or universal molecular identifiers (short degenerate oligonucleotide sequences used to track individual DNA molecules during sequencing) can overcome some of these limitations, but typically require large amounts of input DNA or cannot efficiently target a large number of genes. We developed a sequencing assay that incorporates molecular barcodes and allows for selective targeting of specific regions/genes, achieves high on-target capture efficiency, and increases the fraction of genomes sequenced per sample, resulting in a lower cost of sequencing and increased sensitivity. This method permits sensitive and specific detection of low-frequency variants is ideal or MRD monitoring the clinical laboratory with limited DNA quantities (e.g., cell free DNA). We will validate and optimize this assay using DNA from cell lines, creating a gold-standard method, and then determine its performance using clinical samples in the following Aims.
Specific Aim 1. We will optimize the analytic performance of our targeted molecular barcode-based assay and validate its performance using reference samples. The innovations in our assay allow for specific targeting of both DNA strands in any gene with high on-target and input efficiencies. We will maximize the performance of this assay and library efficiency through iterative improvements in reaction conditions and will test its performance using well-characterized human cell lines containing known SNPs to simulate low- allele frequency mutations present in as few as 1 cell in 5,000.
Specific Aim 2. We will determine the clinical performance of our assay for use as a cancer Measurable Residual Disease (MRD) biomarker. To demonstrate clinical utility, we will determine the performance of our assay using commonly encountered clinical sample types (e.g., fresh-frozen, formalin-fixed tissue, and plasma) harvested from patients with hematopoietic and solid tumors. Finally, we will determine the sensitivity of sequencing DNA derived from blood cells or plasma to identify somatic mutations in cancer patients, potentially allowing for less invasive cancer monitoring.
In order to fully implement precision medicine, accurate methods are needed to monitor the level of cancer cells during and after treatment. Knowledge of the number of cancer cells present after treatment may provide an early indicator of overall response. We propose to rigorously validate an ultra-sensitive sequencing assay capable of monitoring the level of cancer cells present in the blood across all cancer types. The results of this study are of immediate use in the clinical laboratory.
| Duncavage, Eric J; Jacoby, Meagan A; Chang, Gue Su et al. (2018) Mutation Clearance after Transplantation for Myelodysplastic Syndrome. N Engl J Med 379:1028-1041 |
| Wong, Terrence N; Miller, Christopher A; Jotte, Matthew R M et al. (2018) Cellular stressors contribute to the expansion of hematopoietic clones of varying leukemic potential. Nat Commun 9:455 |
| Jacoby, Meagan A; Duncavage, Eric J; Chang, Gue Su et al. (2018) Subclones dominate at MDS progression following allogeneic hematopoietic cell transplant. JCI Insight 3: |
