322. Clinical-Grade Metagenomics in Urinary Tract Infections: Improving Performance of Next-Generation Sequencing Assays Using Internal Controls and Machine Learning. (15th December 2022)

• Record Type: Journal Article
• Authors: Couto-Rodriguez, Mara; Danko, David C; Jirau Serrano, Xavier O; Paisie, Taylor; Papciak, John C; Szollosi, Eszter; Mason, Christopher E; Otto, Caitlin; O'Hara, Niamh B; Nagy-Szakal, Dorottya
• Is Part Of: Open forum infectious diseases. Volume 9:(2022)Supplement 2
• Publication Date: 2022-12-15
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofac492.400
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Shotgun sequencing-based metagenomics is a useful approach to profiling microbiomes in environmental and patient samples. In a clinical setting, metagenomic techniques have the advantage of identifying organisms, which cannot be readily cultured or confirmed by other techniques. We have developed a clinical-grade, streamlined metagenomics-based pipeline that includes regulatory compliant method considerations, such as an internal control followed by a machine-based learning (ML) process to identify pathogens in urine samples. Methods: We built an optimized novel end-to-end NGS assay pipeline that harnesses pathogen-specific genome data to detect bacterial species. We processed de-identified clinical urine specimens, collected from patients symptomatic for urinary tract infection (UTI). This workflow includes an IPC, QIACube-MDx extraction, library preparation and Illumina NextSeq 550 sequencing and a novel interpretable ML based analytic approach, Biotia-DX. Clinical culture results and qPCR were used as a baseline for the assay to train the ML model and to establish accuracy relative to the clinical standard of care. Results: We clinically validated over 40 key uropathogens and conducted clinical studies of specificity, intra/inter reproducibility, accuracy in urine specimens (n=300), and limit of detection in E. coli, K. pneumoniae, P. mirabilis, S. aureus, E. faecalis and Candida. Additionally, the implementation of an internal control coupled with our Biotia-DX software provides an accurate (F1 score 94.3%) and highly sensitive clinical grade diagnostic tool. Conclusion: Urine has historically presented a challenge for diagnostics via culturing, with a high rate of culture-negative results (∼30% on average). We improved the clinical utility of an NGS urine assay by leveraging an IPC and ML software. This decreased the rate of false positive species called in a sample relative to other NGS techniques and allows for greater sensitivity and taxonomic specificity. This assay may be especially useful for low colony-count or negative-culture samples to diagnose and guide patient treatment. Disclosures: Mara Couto-Rodriguez, MS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company David C. Danko, PhD, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company Xavier O. Jirau Serrano, MS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company Taylor Paisie, MS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company John C. Papciak, BS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company Eszter Szollosi, BS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company Christopher E. Mason, PhD, Biotia Inc.: Advisor/Consultant|Biotia Inc.: Board Member|Biotia Inc.: Ownership Interest Caitlin Otto, PhD, D(ABMM), Biotia Inc.: Advisor/Consultant Niamh B. O'Hara, PhD, Biotia Inc.: Board Member|Biotia Inc.: Ownership Interest Dorottya Nagy-Szakal, MD PhD, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company|Biotia Inc.: Stocks/Bonds.