Automated and Decentralized Genomic Profiling of Plasma Cell-Free DNA for Identification of Targetable and Resistance Alterations in Advanced Solid Tumors

Abstract

Background Existing circulating cell-free DNA (cfDNA) assays are primarily centralized, requiring specialized sample handling and transportation. Implementing a flexible, decentralized sequencing system at point of care, with minimal technical oversight, can enhance turnaround times and patient access to genomic profiling. In this study, we aimed to evaluate the clinical feasibility of an automated and decentralized cfDNA next-generation sequencing (NGS) assay for identifying actionable alterations in advanced solid tumors. Methods Genomic profiling of plasma cfDNA from 298 patients with advanced solid tumors was conducted using an automated NGS assay. We assessed concordance of tumor mutations detected in plasma cfDNA and patient-matched tumor tissues analyzed by an FDA-approved assay, investigating clinical factors influencing circulating tumor DNA (ctDNA) aberration (mut-ctDNA) detection sensitivity. Results Sequencing success rates for cfDNA genomic profiling was significantly higher than archived tumor tissue (99% vs 96%). Mut-ctDNA detection rates ranged from 20% to 67% across different solid tumors. Targetable or resistance alterations were found in 18% of the patients. About 72% of the patients showed concordant alterations from tissue and plasma. The level of concordance was associated with the cancer type, tumor burden, and metastatic location. Notably, 63 plasma-only alterations were identified in 18% of patients and were more frequently observed in those with prior targeted treatments (24%) compared to chemotherapy (10%). Conclusions This study underscores the clinical feasibility of an automated, decentralized cfDNA genomic profiling approach. It emphasizes the importance of considering confounding clinical factors when selecting plasma- or tissue-based profiling assay. Such an approach holds promise for enhancing patient access to timely genomic profiling and targeted therapy selection.