Amanda Le
Amanda Le
Helios Scholar
School: Arizona State University
Hometown: Goodyear, Arizona
Mentor: Daniel Lord
PI: Jeffrey Trent, PhD

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Assessing the impact of gDNA contamination and input range on the BESTseq technique for early cancer detection

Early detection of cancer can mean the difference between prolonged life and untimely death for a patient. It also assists patients in avoiding the financial and emotional burdens of drug therapies for advanced stage cancers. Bimodal Early Screening Technique (BESTseq) is a PCR-based assay that detects cancer early in a cost- and time-efficient manner by using a single blood draw to observe cell-free DNA (cfDNA) fragmentation. BESTseq can detect cancerous cell proliferation by comparing the distribution of two cfDNA amplicon frequencies on a bimodal distribution graph. Despite BESTseq’s potential to minimize patient suffering and risk of death, the presence of genomic DNA (gDNA) can “contaminate” cfDNA samples and lead to inconclusive results. To minimize the effect of gDNA contamination on BESTseq’s sensitivity, matched gDNA and cfDNA samples from healthy individuals were compared to identify and bioinformatically exclude amplicon regions of gDNA bias from the cfDNA profile. Each sample was assigned a score based on the observed ratio of the two amplicons in the matched sequencing data; those with a higher gDNA score were omitted from analysis to retain only those regions that are more informative in the cfDNA. After removal of gDNA-biased regions, all samples’ scores lowered as expected; this indicates that excluding these particular DNA regions from analysis can fine tune BESTseq’s cancer detection efficiency. In addition, quality control steps to standardize samples to a particular DNA concentration can reduce the speed and affordability of BESTseq. To produce optimal results, without compromising time and cost, a dilution series of predetermined cfDNA input concentrations was utilized to assess the need for a controlled input concentration for BESTseq. Results showed a gradual decrease in score as concentration increased; bioinformatic analysis is required to determine whether possible bias toward amplicon size or primer competition at higher cfDNA concentrations may influence score. Identifying and eliminating conserved amplicon regions within gDNA can fine tune analysis and determining the input range of the assay can reduce the steps it takes to make a cancer diagnosis.