Analyzing imbalance of short tandem repeats for pancreatic cancer detection.

Abstract

e16359 Background: Short tandem repeats (STRs), alternatively known as microsatellites, encompass repeat units of 1 to 6 base pairs (bp) in the genome and function as regulators of gene expression, impacting plasticity and complex traits. STRs are prone to hypermutation with alterations correlated to pathogenicity in multiple Mendelian and acquired disorders. STR variant classes include insertions, mobile element insertions (MEIs), deletions, and multi-allelic copy number variants (mCNVs). These can lead to repeat expansions, allelic imbalance (AI), and microsatellite instability (MSI) observed in diseases such as Huntington's disease, hereditary ataxia, and multiple cancers. In pancreatic cancer, AI and associated loss-of-heterozygosity (LOH) at tumor suppressor loci flanked by hypervariable STR regions correspond to a pathogenic phenotype with a risk of aggressive disease. Consequently, it is critical to develop accurate and robust methods to precisely identify and genotype pancreatic samples with varying specimen quality in a clinical setting to aid in diagnosis. Methods: Capillary electrophoresis (CE) and Sanger sequencing have been used to analyze STRs. The availability of newer tools such as 2nd generation short-read (NGS) and 3rd generation long-read (TGS) sequencers could allow for high throughput scaling, however, informatics challenges associated with analyzing repetitive regions remain. Here we developed amplification and enrichment methods for targeted analysis for detection by CE, NGS and TGS. Results: Amplification and enrichment strategies for detecting 17 short tandem repeat regions that play a role in pancreatic carcinogenesis were developed. Methods were optimized for detecting STR alterations in cell-free nucleic acids isolated from endoscope-guided pancreatic fine needle aspirate biopsies. Furthermore, informatics tools were developed to analyze short-read and long-read sequencing data to accurately analyze repeat regions. Conclusions: Here we compared 3 platforms (CE, NGS, and TGS) and found that CE remains the gold standard for analyzing AI/LOH with data comparable to long-read TGS, while short-read NGS is not always amenable to accurate analysis dependent on the region of interest. Additionally, we found that artifacts of amplification persist, irrespective of the 3 platforms analyzed.