Next Generation-Targeted Amplicon Sequencing (NG-TAS): an optimised protocol and computational pipeline for cost-effective profiling of circulating tumour DNA

Other authors

Institut Català de la Salut

[Gao M, Callari M] Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK. [Beddowes E] Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK. Breast Cancer Programme, Cancer Research UK Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. [Sammut SJ, Grzelak M] Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK. [Biggs H] Breast Cancer Programme, Cancer Research UK Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. [Cortes J] Ramon y Cajal University Hospital, Madrid, Spain. Vall d'Hebron Institut d'Oncologia, Barcelona, Spain. [Oliveira M] Vall d'Hebron Institut d'Oncologia, Barcelona, Spain.

Vall d'Hebron Barcelona Hospital Campus

Publication date

2019-02-28T12:02:23Z

2019-02-28T12:02:23Z

2019-01-04



Abstract

Cancer; Computational pipeline; Deep sequencing


Càncer; Segmentació computacional; Seqüenciació


Cáncer; Segmentación computacional; Secuenciación


Circulating tumour DNA (ctDNA) detection and monitoring have enormous potential clinical utility in oncology. We describe here a fast, flexible and cost-effective method to profile multiple genes simultaneously in low input cell-free DNA (cfDNA): Next Generation-Targeted Amplicon Sequencing (NG-TAS). We designed a panel of 377 amplicons spanning 20 cancer genes and tested the NG-TAS pipeline using cell-free DNA from two HapMap lymphoblastoid cell lines. NG-TAS consistently detected mutations in cfDNA when mutation allele fraction was > 1%. We applied NG-TAS to a clinical cohort of metastatic breast cancer patients, demonstrating its potential in monitoring the disease. The computational pipeline is available at https://github.com/cclab-brca/NGTAS_pipeline

Document Type

Article


Published version

Language

English

Publisher

BMC

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Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

http://creativecommons.org/licenses/by-nc-nd/4.0/

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