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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
Published on: August 24, 2017
Harmonising ctDNA Measurement in Haematological Malignancies: Traceability, Commutability and Reporting
1School of Health and Biomedical Sciences, RMIT University, P.O. Box 71, Bundoora, Melbourne, VIC 3083, Australia.
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Circulating tumour DNA (ctDNA) assays are increasingly applied in haematological malignancies for non-invasive genotyping, quantitative response assessment, measurable residual disease (MRD) detection, and relapse surveillance, often complementing bone marrow-based testing and, in selected scenarios, potentially reducing its frequency. Yet, translating ctDNA results into comparable clinical decisions across laboratories, platforms, and time remains challenging because ctDNA measurements are influenced by the definition of the measurand (for example, variant allele fraction versus mutant molecules per mL), pre-analytical variables, end-to-end workflow losses, and lineage-specific confounders such as clonal haematopoiesis of indeterminate potential (CHIP), therapy-related clonal haematopoiesis, and compartmental disease (marrow, plasma, cerebrospinal fluid, extramedullary sites). This review proposes a harmonisation framework for haematological ctDNA based on three linked concepts-metrological traceability, which connects reported values to reference systems with stated uncertainty, commutability, which ensures that reference materials behave like patient specimens across diverse workflows and fit-for-purpose reference materials that support calibration, and quality control, external quality assessment, and cut-off setting for intended uses such as early molecular response in large B-cell lymphoma, molecular MRD in acute myeloid leukaemia, and deep response monitoring in multiple myeloma. This framework is accompanied by harmonised CHIP-aware reporting rules for settings without matched cellular DNA and practical change-control/bridging strategies to preserve clinical decision thresholds when platforms or bioinformatic pipelines evolve.

