Refereed journal article or data article (A1)
Molecular features encoded in the ctDNA reveal heterogeneity and predict outcome in high-risk aggressive B-cell lymphoma
List of Authors: Meriranta Leo, Alkodsi Amjad, Pasanen Annika, Lepistö Maija, Mapar Parisa, Blaker Yngvild Nuvin, Meszaros Jørgensen Judit, Karjalainen-Lindsberg Marja-Liisa, Fiskvik Idun, Gyland Mikalsen Lars Tore, Autio Matias, Björkholm Magnus, Jerkeman Mats, Fluge Øystein, Brown Peter, Jyrkkiö Sirkku, Holte Harald, Pitkänen Esa, Ellonen Pekka, Leppä Sirpa
Publisher: Ash Publications
Publication year: 2022
Journal: Blood
Journal name in source: Blood
Journal acronym: Blood
Volume number: 139
Issue number: 12
Start page: 1863
End page: 1877
ISSN: 0006-4971
eISSN: 1528-0020
DOI: http://dx.doi.org/10.1182/blood.2021012852
URL: https://ashpublications.org/blood/article-abstract/doi/10.1182/blood.2021012852/483214/Molecular-features-encoded-in-the-ctDNA-reveal?redirectedFrom=fulltext
Inadequate molecular and clinical stratification of the patients with high-risk diffuse large B-cell lymphoma (DLBCL) is a clinical challenge hampering the establishment of personalized therapeutic options. We studied the translational significance of liquid biopsy in a uniformly treated trial cohort. Pretreatment circulating tumor DNA (ctDNA) revealed hidden clinical and biological heterogeneity, and high ctDNA burden determined increased risk of relapse and death independently of conventional risk factors. Genomic dissection of pretreatment ctDNA revealed translationally relevant phenotypic, molecular, and prognostic information that extended beyond diagnostic tissue biopsies. During therapy, chemorefractory lymphomas exhibited diverging ctDNA kinetics, whereas end-of-therapy negativity for minimal residual disease characterized cured patients and resolved clinical enigmas, including false residual PET positivity. Furthermore, we discovered fragmentation disparities in the cell-free DNA that characterize lymphoma-derived ctDNA and, as a proof-of-concept for their clinical application, utilized machine learning to show that end-of-therapy fragmentation patterns predict outcome. Altogether, we have discovered novel molecular determinants in the liquid biopsy that can non-invasively guide treatment decisions.
