4.7 Article

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

Journal

BLOOD
Volume 139, Issue 12, Pages 1863-1877

Publisher

AMER SOC HEMATOLOGY
DOI: 10.1182/blood.2021012852

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Funding

  1. Academy of Finland [311171]
  2. Biomedicum Helsinki Foundation
  3. Digital Precision Cancer Medicine Flagship iCAN
  4. Finnish Cancer Organizations
  5. Sigrid Juselius Foundation
  6. Southern Finland Regional Cancer Center FICAN South
  7. Helsinki University Hospital
  8. University of Helsinki
  9. Ida Montin Foundation
  10. Orion Research Foundation
  11. Paolo Foundation
  12. Academy of Finland (AKA) [311171, 311171] Funding Source: Academy of Finland (AKA)

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Liquid biopsy has important clinical significance in high-risk DLBCL patients, as it can reveal hidden clinical and biological heterogeneity, predict the risk of relapse and death, and provide molecular information for treatment decisions.
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 non invasively guide treatment decisions.

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