Abstract 1912: A versatile computational pipeline for the preprocessing of cell-free DNA fragmentation data

Cell-free DNA (cfDNA) emerges as a promising liquid biopsy biomarker for cancer diagnosis and patient monitoring. Complementing mutation-based assays, cfDNA carries information about epigenetic modifications from decaying cells. This information is encoded in the shape of the cfDNA fragments. Specifically, fragments from cancer tend to be shorter than those originating from other adult cells, enabling a distinction between cancer patients and healthy individuals. Additional cfDNA features such as fragment end motifs and information on nucleosome positioning provide further insight into cancer biology. These cfDNA measures are typically inferred from low-pass whole genome sequencing and subsequent bioinformatics processing. A key bioinformatics step is the alignment of DNA sequencing reads to the reference genome, which critically depends on preprocessing steps such as read trimming and alignment filters. A good understanding of preprocessing settings is thus crucial to derive accurate information from cfDNA. We therefore investigated to what extent preprocessing choices affect cfDNA analysis. To this end, we have built a robust bioinformatics pipeline that evaluates a range of possible preprocessing settings. The pipeline is implemented in a bioinformatics workflow engine, enabling a scalable and reproducible workflow. We are currently evaluating the effect of preprocessing on global and regional fragmentation patterns, detection of nucleosome positioning and identification of fragment end motifs. The different preprocessing settings are also evaluated for their ability to distinguish cancer from control samples using cfDNA fragmentation information. We are benchmarking the pipeline using cfDNA datasets from multiple centres to ensure our findings are generalizable over different platforms and experimental procedures. Our investigations will allow us to build a versatile bioinformatics preprocessing pipeline for the analysis of cell-free DNA fragmentation data. Citation Format: Ivna Ivankovic, Zsolt Balázs, Todor Gitchev, Daniel Trejo Banos, Norbert Moldovan, Panagiotis Balermpas, Jonas Willmann, Nicolaus Andratschke, Florent Moulière, Michael Krauthammer. A versatile computational pipeline for the preprocessing of cell-free DNA fragmentation data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1912.