B-244 DNA Library Amplification to Produce Archived Reference Samples for R&D and Clinical Assay Development, Optimization, and Validation

Abstract

Abstract Background Rapid advances in clinical diagnostic testing are constrained by finite specimen availability, technical and financial burdens incurred by patient recruitment, and sample collection. Expanding the availability of patient-derived analyte for experimentation could accelerate assay development and regulatory submissions, while reducing demand for additional resource-intensive clinical studies. We developed the AReS (Archived Reference Sample) platform to utilize genomic libraries as templates for PCR amplification. The resulting PCR product, referred to as an AReS library, serves as an alternative sample type from which development, optimization, and validation studies can be iteratively performed. Methods PCR conditions were optimized to maximize yield while minimizing amplification bias, generating approximately 50X more mass than the starting input. To evaluate the AReS process, aliquots of bisulfite converted original DNA libraries were further amplified under optimized conditions to produce AReS libraries. Both the original and AReS libraries were then hybrid captured using Harbinger Health’s proprietary 8.4 Mb panel and sequenced to ≥100X unique median target coverage (MTC) depth. Original libraries were compared to AReS libraries across Picard sequencing metrics by quantifying methylation across our regions of interest and by classification as determined by our cancer yes/no (CYN) determining algorithm. Harbinger Health’s CYN algorithm was developed using a multi-layered logistic regression-based machine learning approach trained on a separate patient cohort and locked prior to being used in this study. In total, we generated 528 AReS libraries from 321 unique patient-derived DNA samples, of which 124 were from patients diagnosed with cancer and 197 from patients with no cancer diagnosis. An additional sub-study was performed on 16 paired original and AReS libraries containing unique molecular identifiers (UMIs). The UMIs allowed for the comparison of individual cfDNA molecules between the two sample types. Results All AReS samples, including both intra- and inter- batch replicates, were highly concordant to the original library. There was no significant difference across sequencing metrics (e.g., conversion efficiency or %CC and MTC). All AReS libraries had similar methylation values to original libraries; Pearson correlation was greater than 98%. Our data also indicated that greater than 97% of AReS libraries were concordant with the original library by our CYN algorithm classification. Read-level UMI analysis identified that approximately 77% of reads were common between the original and AReS libraries. As both the UMI and sequence insert used were identical, these common reads were derived from the same cfDNA molecule. As a frame of reference, sequencing replicates of the original library similarly shared approximately 77% of common reads. In addition, the Pearson correlation of read frequency compared between AReS and original libraries were within 4% difference of the correlation between original library sequencing replicates. These results showed no indication of AReS-derived amplification bias. Conclusion Taken together, the AReS process produces excess libraries that highly reproducible. AReS libraries are functionally and analytically identical to original libraries and can be applied to both research and clinical use.