: The clinical management of myeloid neoplasms increasingly relies on the accurate detection and longitudinal monitoring of disease-defining genetic alterations. Many clinically relevant mutations are often present at very low variant allele frequencies, below the detection limits of conventional approaches routinely used in diagnostic workflows. In recent years, a growing number of ultra-sensitive molecular technologies have been developed to overcome these limitations, enabling the detection of rare variants with unprecedented precision, offering complementary strengths in terms of sensitivity, quantification, throughput, and clinical applicability. This review provides a comprehensive overview of established and emerging ultra-sensitive technologies for the diagnosis and molecular monitoring of myeloid neoplasms, discussing their technical principles, advantages, and limitations.

Ultra-Sensitive Mutation Detection Technology in Myeloid Neoplasms: New Tools for Patient Monitoring

Ferrando, Alessandro;Bonuomo, Valentina;Savi, Arianna;Cilloni, Daniela
2026-01-01

Abstract

: The clinical management of myeloid neoplasms increasingly relies on the accurate detection and longitudinal monitoring of disease-defining genetic alterations. Many clinically relevant mutations are often present at very low variant allele frequencies, below the detection limits of conventional approaches routinely used in diagnostic workflows. In recent years, a growing number of ultra-sensitive molecular technologies have been developed to overcome these limitations, enabling the detection of rare variants with unprecedented precision, offering complementary strengths in terms of sensitivity, quantification, throughput, and clinical applicability. This review provides a comprehensive overview of established and emerging ultra-sensitive technologies for the diagnosis and molecular monitoring of myeloid neoplasms, discussing their technical principles, advantages, and limitations.
2026
15
3
1
21
LS4_11
diagnosis; myeloid neoplasms; patient monitoring; personalized medicine; sensitivity
Ferrando, Alessandro; Bonuomo, Valentina; Savi, Arianna; Cilloni, Daniela
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2133491
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