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Harmonizing Cell-Free DNA Collection and Processing Practices through Evidence-Based Guidance.

Sarah R GreytakKelly B EngelSonya Parpart-LiMuhammed MurtazaAbel Jacobus BronkhorstMark Domenic PertileHelen M Moore
Published in: Clinical cancer research : an official journal of the American Association for Cancer Research (2020)
Circulating cell-free DNA (cfDNA) is rapidly transitioning from discovery research to an important tool in clinical decision making. However, the lack of harmonization of preanalytic practices across institutions may compromise the reproducibility of cfDNA-derived data and hamper advancements in cfDNA testing in the clinic. Differences in cellular genomic contamination, cfDNA yield, integrity, and fragment length have been attributed to different collection tube types and anticoagulants, processing delays and temperatures, tube agitation, centrifugation protocols and speeds, plasma storage duration and temperature, the number of freeze-thaw events, and cfDNA extraction and quantification methods, all of which can also ultimately impact subsequent downstream analysis. Thus, there is a pressing need for widely applicable standards tailored for cfDNA analysis that include all preanalytic steps from blood draw to analysis. The NCI's Biorepositories and Biospecimen Research Branch has developed cfDNA-specific guidelines that are based upon published evidence and have been vetted by a panel of internationally recognized experts in the field. The guidelines include optimal procedures as well as acceptable alternatives to facilitate the generation of evidence-based protocols by individual laboratories and institutions. The aim of the document, which is entitled "Biospecimen Evidence-based Best Practices for Cell-free DNA: Biospecimen Collection and Processing," is to improve the accuracy of cfDNA analysis in both basic research and the clinic by improving and harmonizing practices across institutions.
Keyphrases
  • primary care
  • healthcare
  • decision making
  • randomized controlled trial
  • machine learning
  • clinical practice
  • dna methylation
  • artificial intelligence