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Multi‐loci diagnosis of acute lymphoblastic leukaemia with high‐throughput sequencing and bioinformatics analysis
Author(s) -
Ferret Yann,
Caillault Aurélie,
Sebda Shéhérazade,
Duez Marc,
Grardel Nathalie,
Duployez Nicolas,
Villenet Céline,
Figeac Martin,
Preudhomme Claude,
Salson Mikaël,
Giraud Mathieu
Publication year - 2016
Publication title -
british journal of haematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.907
H-Index - 186
eISSN - 1365-2141
pISSN - 0007-1048
DOI - 10.1111/bjh.13981
Subject(s) - sanger sequencing , dna sequencing , minimal residual disease , massive parallel sequencing , computational biology , bioinformatics , medicine , biology , oncology , genetics , immunology , bone marrow , gene
Summary High‐throughput sequencing ( HTS ) is considered a technical revolution that has improved our knowledge of lymphoid and autoimmune diseases, changing our approach to leukaemia both at diagnosis and during follow‐up. As part of an immunoglobulin/T cell receptor‐based minimal residual disease ( MRD ) assessment of acute lymphoblastic leukaemia patients, we assessed the performance and feasibility of the replacement of the first steps of the approach based on DNA isolation and Sanger sequencing, using a HTS protocol combined with bioinformatics analysis and visualization using the Vidjil software. We prospectively analysed the diagnostic and relapse samples of 34 paediatric patients, thus identifying 125 leukaemic clones with recombinations on multiple loci ( TRG , TRD , IGH and IGK ), including Dd2/Dd3 and Intron/ KDE rearrangements. Sequencing failures were halved (14% vs. 34%, P = 0.0007), enabling more patients to be monitored. Furthermore, more markers per patient could be monitored, reducing the probability of false negative MRD results. The whole analysis, from sample receipt to clinical validation, was shorter than our current diagnostic protocol, with equal resources. V(D)J recombination was successfully assigned by the software, even for unusual recombinations. This study emphasizes the progress that HTS with adapted bioinformatics tools can bring to the diagnosis of leukaemia patients.