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Blood test can predict outcomes in DLBCL, team says


 

Blood sample collection

Photo by Juan D. Alfonso

A blood test can reveal genetic features linked to outcomes in patients with diffuse large B-cell lymphoma (DLBCL), according to research published in Science Translational Medicine.

Investigators used targeted sequencing to analyze circulating tumor DNA (ctDNA) in blood samples from DLBCL patients.

This allowed the team to identify the cell of origin, detect minimal residual disease (MRD), and predict progression-free survival (PFS) in these patients.

Florian Scherer, MD, of Stanford University in California, and his colleagues conducted this research.

They used cancer personalized profiling by deep sequencing (CAPP-Seq) to analyze tumor biopsies and cell-free DNA samples from 92 patients with DLBCL and 24 healthy controls.

The investigators found that CAPP-Seq could effectively detect somatic mutations in DLBCL plasma samples as well as tumor biopsies. They said their results suggest ctDNA is a “robust surrogate for direct assessment of primary tumor genotypes” in most DLBCL patients.

In addition, ctDNA profiling with CAPP-Seq revealed mutations associated with resistance to the BTK inhibitor ibrutinib.

The investigators also said their results suggest ctDNA profiling can be used to classify DLBCL subtypes. The overall concordance in cell of origin predictions between tumor tissue and plasma genotyping was 88%.

Another key finding of this study is that the amount of ctDNA at DLBCL diagnosis was predictive of PFS. The investigators said higher ctDNA levels at diagnosis were “continuously and independently” correlated with inferior PFS.

Dr Scherer and his colleagues also discovered that ctDNA profiling could detect MRD with greater accuracy than immunoglobulin sequencing and radiographic imaging. And patients with ctDNA in their plasma had significantly worse PFS than patients with undetectable ctDNA.

Finally, the investigators found evidence to suggest that ctDNA profiling could provide early detection of disease transformation. They identified “distinct patterns of clonal evolution” by which they could distinguish indolent follicular lymphomas from follicular lymphomas that transformed into DLBCL.

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