Molecular features encoded in the ctDNA reveal heterogeneity and predict outcome in high-risk aggressive B-cell lymphoma

Leo Meriranta(University of Helsinki), Amjad Alkodsi(University of Helsinki), Annika Pasanen(University of Helsinki), Maija Lepistö(Institute for Molecular Medicine Finland), Parisa Mapar(University of Helsinki), Yngvild Nuvin Blaker(Oslo University Hospital), Judit Jørgensen(Aarhus University Hospital), Marja‐Liisa Karjalainen‐Lindsberg(Helsinki University Hospital), Idun Fiskvik, Lars Tore Gyland Mikalsen(Oslo University Hospital), Matias Autio(University of Helsinki), Magnus Björkholm(Karolinska University Hospital), Mats Jerkeman(Skåne University Hospital), Øystein Fluge(Haukeland University Hospital), Peter de Nully Brown(Copenhagen University Hospital), Sirkku Jyrkkiö(Turku University Hospital), Harald Holte(Oslo University Hospital), Esa Pitkänen(University of Helsinki), Pekka Ellonen(Institute for Molecular Medicine Finland), Sirpa Leppä(University of Helsinki)
Blood
December 21, 2021
Cited by 128Open Access
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Abstract

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 (MRD) 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, used 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 noninvasively guide treatment decisions.


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