Abstract 3705: <i>In silico</i> modeling of ctDNA shedding using a novel computational pipeline which models tumorigenesis, progression, and metastasis

Abstract Introduction: Liquid biopsies are shifting the paradigm for diagnosis and treatment of many types of cancer. Circulating tumor DNA (ctDNA), defined as small fragments of tumor DNA shed into circulation from tumor cells, represents a major analyte for liquid biopsy approaches. ctDNA has potentially wide translational applications, including early detection of cancer, monitoring disease response, detection of minimal residual disease (MRD), and biological-sampling of tumor biology noninvasively. Although there is significant interest and a growing body of work in applying ctDNA to the clinic, existing studies are generally limited by the access to patient plasma samples. Importantly, computational models are often useful to simulate data in contexts where actual experiments require such precious resources; however, there are currently no existing computational models of tumor ctDNA shedding. In that context, the current study overcomes this limitation and describes a novel bioinformatic pipeline which is able to model ctDNA shedding during tumor initiation, growth, progression, and metastasis. Methods: We developed a novel method for generating simulated ctDNA next-gen sequencing (NGS) data in a variety of tumor scenarios. To accomplish this, we enhanced a previously-characterized spatial model of tumor growth which generates simulated whole exome sequencing data in silico to also generate ctDNA data. Outputs from this spatial model include a list of variants called from the simulated exome data. To generate the simulated ctDNA data, the distribution of these variant allele frequencies (VAF) were split into quantile bins with an associated scale factor. All variants had their mean tumor VAFs divided by their respective scale factors to determine corresponding ctDNA VAFs utilizing a validated “downsampling” technique. Results: First we utilized the novel pipeline to generate simulated ctDNA in the setting of both a non-metastatic primary tumor and a metastatic tumor. We next validated the fidelity of simulated ctDNA data to real-world biologic data using published datasets of ctDNA from both metastatic and nonmetastatic sarcomas. In that validation analysis, we demonstrated a high degree of similarity between the simulated and published ctDNA data. Conclusions: Here we demonstrate a novel computational approach to model the paradigm-shifting technology of liquid biopsy completely in silico. Our ability to generate simulated data with validated biologic fidelity has potential to augment discovery-based ctDNA studies. Furthermore, the potential of this technology is underscored by the emerging potential of AI-based approaches, digital twin technologies, and simulated clinical trial data as viable synergistic approaches to conventional clinical studies. Citation Format: Jasmine R. Alvarez, Ajay Subramanian, Everett J. Moding, Erik S. Blomain. In silico modeling of ctDNA shedding using a novel computational pipeline which models tumorigenesis, progression, and metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3705.