Research Topic: Measurable Residual Disease in Hematologic Malignancies. Can digital droplet PCR improve measurable residual disease monitoring in chronic lymphoid malignancies?

chronic lymphocytic leukemia digital droplet PCR hairy cell leukaemia (HCL) measurable residual disease (MRD) non-Hodgkin lymphoma

Journal

Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867

Informations de publication

Date de publication:
2023
Historique:
received: 27 01 2023
accepted: 01 03 2023
medline: 1 4 2023
entrez: 31 3 2023
pubmed: 1 4 2023
Statut: epublish

Résumé

Minimal/measurable residual disease (MRD) monitoring is progressively changing the management of hematologic malignancies. The possibility of detecting the persistence/reappearance of disease in patients in apparent clinical remission offers a refined risk stratification and a treatment decision making tool. Several molecular techniques are employed to monitor MRD, from conventional real-time quantitative polymerase chain reaction (RQ-PCR) to next generation sequencing and digital droplet PCR (ddPCR), in different tissues or compartments through the detection of fusion genes, immunoglobulin and T-cell receptor gene rearrangements or disease-specific mutations. RQ-PCR is still the gold standard for MRD analysis despite some limitations. ddPCR, considered the third-generation PCR, yields a direct, absolute, and accurate detection and quantification of low-abundance nucleic acids. In the setting of MRD monitoring it carries the major advantage of not requiring a reference standard curve built with the diagnostic sample dilution and of allowing to reduce the number of samples below the quantitative range. At present, the broad use of ddPCR to monitor MRD in the clinical practice is limited by the lack of international guidelines. Its application within clinical trials is nonetheless progressively growing both in acute lymphoblastic leukemia as well as in chronic lymphocytic leukemia and non-Hodgkin lymphomas. The aim of this review is to summarize the accumulating data on the use of ddPCR for MRD monitoring in chronic lymphoid malignancies and to highlight how this new technique is likely to enter into the clinical practice.

Identifiants

pubmed: 36998457
doi: 10.3389/fonc.2023.1152467
pmc: PMC10043164
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

1152467

Informations de copyright

Copyright © 2023 Assanto, Del Giudice, Della Starza, Soscia, Cavalli, Cola, Bellomarino, Di Trani, Guarini and Foà.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Giovanni Manfredi Assanto (GM)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.

Ilaria Del Giudice (I)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.

Irene Della Starza (I)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.
Gruppo Italiano Malattie Ematologiche dell'Adulto (GIMEMA), Fondazione GIMEMA Franco Mandelli Onlus, Rome, Italy.

Roberta Soscia (R)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.

Marzia Cavalli (M)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.

Mattia Cola (M)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.

Vittorio Bellomarino (V)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.

Mariangela Di Trani (M)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.

Anna Guarini (A)

Department of Molecular Medicine, Sapienza University, Rome, Italy.

Robin Foà (R)

Hematology, Department of Translational and Precision Medicine, Sapienza University, Rome, Italy.

Classifications MeSH