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Original Articles

Analysis of circulating tumor DNA by targeted ultra-deep sequencing across various non-Hodgkin lymphoma subtypes

, , , , , , , ORCID Icon, & show all
Pages 2237-2246 | Received 05 Oct 2018, Accepted 17 Jan 2019, Published online: 18 Feb 2019
 

Abstract

Although targeted deep sequencing of cell-free DNA (cfDNA) was recently used to investigate tumor somatic mutations in particular subtypes of non-Hodgkin lymphomas (NHLs), the immense genetic heterogeneity across subtypes poses a hurdle to design a universal gene panel applicable for diverse subtypes of NHLs. We designed a panel targeting 66 genes associated with NHLs and performed targeted deep sequencing to analyze plasma cfDNA from patients with various subtypes of NHLs. Genetic profiling in plasma cfDNA using the method resulted in 88.0% sensitivity and >99% specificity in detecting mutations present at a frequency greater than 20% in the tumor biopsies. Furthermore, the level of ctDNA significantly decreased and increased depending on designated clinical responses to therapy and disease progression. These results demonstrated that ctDNA sensitively indicated the presence of cancer and reliably correlated with tumor burden, suggesting potential utility of the method for patients with various subtypes of NHLs.

Potential conflict of interest

Disclosure forms provided by the authors are available with the full text of this article online at https://doi/org/10.1080/10428194.2019.1573998.

Acknowledgements

We thank the technical staff of the Samsung Genome Institute for performing the next-generation sequencing.

Additional information

Funding

This study was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea (HI13C2096), the National Research Foundation of Korea (NRF) Grants funded by the Korean Government (ME) (2017R1D1A1B03035186 and 2017R1D1A1B03028759), and the NRF Grant funded by the Korean Government (MSIT) (2017M3A9G5060264).

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