Research ArticleCancer

Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA

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Science Translational Medicine  09 Nov 2016:
Vol. 8, Issue 364, pp. 364ra155
DOI: 10.1126/scitranslmed.aai8545

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The telltale DNA in lymphoma

Diffuse large B cell lymphoma is a relatively common type of tumor that can exhibit a wide range of behaviors, from indolent and curable cancers to ones that are very aggressive and difficult to treat. By analyzing DNA in tumor samples and blood of lymphoma patients, Scherer et al. have shown that specific genetic characteristics can determine each tumor’s cell of origin and identify tumors that are going to transform into more aggressive subtypes and may require more intensive treatment. The authors also demonstrated that circulating tumor DNA in the patients’ blood is suitable for this analysis, allowing for periodic monitoring of each patient without repeated invasive biopsies.

Abstract

Patients with diffuse large B cell lymphoma (DLBCL) exhibit marked diversity in tumor behavior and outcomes, yet the identification of poor-risk groups remains challenging. In addition, the biology underlying these differences is incompletely understood. We hypothesized that characterization of mutational heterogeneity and genomic evolution using circulating tumor DNA (ctDNA) profiling could reveal molecular determinants of adverse outcomes. To address this hypothesis, we applied cancer personalized profiling by deep sequencing (CAPP-Seq) analysis to tumor biopsies and cell-free DNA samples from 92 lymphoma patients and 24 healthy subjects. At diagnosis, the amount of ctDNA was found to strongly correlate with clinical indices and was independently predictive of patient outcomes. We demonstrate that ctDNA genotyping can classify transcriptionally defined tumor subtypes, including DLBCL cell of origin, directly from plasma. By simultaneously tracking multiple somatic mutations in ctDNA, our approach outperformed immunoglobulin sequencing and radiographic imaging for the detection of minimal residual disease and facilitated noninvasive identification of emergent resistance mutations to targeted therapies. In addition, we identified distinct patterns of clonal evolution distinguishing indolent follicular lymphomas from those that transformed into DLBCL, allowing for potential noninvasive prediction of histological transformation. Collectively, our results demonstrate that ctDNA analysis reveals biological factors that underlie lymphoma clinical outcomes and could facilitate individualized therapy.

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