Implementing whole genome and transcriptome sequencing for cancer patients in routine healthcare: a comprehensive guide to costing.
Summary
This study conducted a detailed micro-costing analysis of implementing whole genome sequencing (WGS) and whole transcriptome sequencing (WTS) in routine clinical care for cancer patients. The primary goal was to identify the key cost drivers hindering the widespread adoption of these comprehensive diagnostic technologies. Researchers developed a multidimensional costing model, which revealed that consumables, particularly flow cells, represent the main expenditure. While personnel and equipment costs decreased with increasing sample volumes, consumable and data processing/storage costs remained largely unchanged. The conclusion emphasizes that falling consumable prices are crucial for broader integration of WGS/WTS as a clinical-grade test.
Analysis
Clinique: The integration of WGS/WTS into routine clinical practice is vital for precise molecular characterization of solid tumors, enabling the identification of drug targets and biomarkers for improved patient management. However, the high cost compared to targeted approaches is a major barrier to widespread adoption. This cost analysis, while not describing a clinical trial, provides essential insights to justify changes in reimbursement policies and laboratory organization. A significant reduction in consumable costs could make WGS/WTS accessible to more patients, with a potential clinical impact within 3-5 years. Biomol: The study focuses on implementing a WGS/WTS workflow based on short-read sequencing technology, representing the most comprehensive approach for molecular cancer diagnostics. The analysis identified consumables, particularly flow cells, as the primary cost drivers, a key finding for cost reduction strategies. The analytical validity of these technologies is presumed, with the study focusing on the economic viability of their deployment. A decrease in consumable prices could directly facilitate the adoption of these advanced diagnostic tests. Bioinfo: The study developed a multidimensional costing model to analyze WGS/WTS implementation, specifically including data processing and storage costs. This model is not a novel algorithmic architecture but an incremental application of a micro-costing methodology to an existing bioinformatics workflow. It quantifies the impact of sample volumes and other scenarios on costs, providing a basis for optimizing bioinformatics infrastructures. The reproducibility of the model is implicit through its detailed description of cost categories. If deployed clinically, a better understanding of data costs could optimize workflows and storage strategies.