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E.K.M. Mack et al.
gene, t(6;9)(p23;q34.1)/DEK-NUP214, inv(3)(q21.3;q26.2) or t(3;3)(q21.3;q26.2)/GATA2, MECOM, loss of chromo- some 5/5q, 7, or 17/17p, mutations in CEPBA (biallelic), NPM1, RUNX1, ASXL1 and TP53, and internal tandem duplications (ITD) in the FLT3 gene.2 Additionally, AML with IDH2R172 mutations alone and AML with mutations in chromatin regulators or splicing factors such as DNMT3A, TET2, SRSF2 and SF3B1 have been proposed recently as distinct genomic subclasses of AML.3
In spite of the considerable genetic heterogeneity of the disease, chemotherapy with cytarabine and anthracy- clines has been the backbone of induction treatment for adults with AML throughout the last 30 years.4,5 Only acute promyelocytic leukemia with the hallmark translo- cation t(15;17)/PML-RARA has been shown to be highly curable by all-trans retinoic acid and arsenic trioxide.6 Immediate chemotherapy-free first-line treatment of acute promyelocytic leukemia is possible because this spe- cific entity can be diagnosed within just a few hours by peripheral blood smear or bone marrow cytology and tar- geted reverse transcriptase polymerase chain reaction (PCR) analysis for PML-RARA. In contrast, discrimination of all other AML subtypes requires multiple molecular and cytogenetic analyses. In particular, AML karyotyping often requires shipping of samples to specialized laborato- ries, thus precluding completion within 5-7 days, as rec- ommended.2 Here, we developed and evaluated an inte- grated NGS platform for numerical karyotyping and focused screening for translocations and mutations in AML-related genes which enables fast identification of the majority of genetic alterations in AML with prognostic and therapeutic implications.
Methods
Patients and cell lines
The 33 patients’ samples analyzed in this study were obtained from the repository of the Clinic for Hematology, Oncology and Immunology or the Munich Leukemia Laboratory with the patients’ informed consent. Cell lines were purchased from DSMZ. This study was approved by the clinical ethics committee at the Faculty of Medicine, Philipps-University Marburg (N. 38/16).
Library preparation and sequencing
Whole genome libraries were constructed using the NEBNext® Ultra II kit (New England Biolabs, Ipswich, MA, USA). Fusion libraries were prepared using the FusionPlex® Heme v1 or v2 pan- els (ArcherDX, Boulder, CO, USA). Variant libraries were generat- ed using the TruSight® Myeloid panel (Illumina, San Diego, CA, USA) or the QIAseqTM Human Myeloid Neoplasms panel (Qiagen, Hilden, Germany). Sequencing was performed on an Illumina MiSeq instrument. The experimental procedures are detailed in the Online Supplementary Methods.
Bioinformatics
Copy number variations (CNV) were analyzed using a propri- etary algorithm designated CAI[N] (chromosomal aberration identi- fier [numerical]) which was implemented in Python. Fusion calling was performed using the Archer® Analysis pipeline (version 4.1). For variant calling, the BaseSpace® TruSight® Myeloid App (Illumina) and ITD-seek7 or, respectively, smCounter8 (Qiagen) were used as appropriate. For identification of KMT2A-partial tandem duplica- tions (PTD) in amplicon libraries, a novel Python algorithm (PTDi:
PTD identifier) was developed. Details are given in the Online Supplementary Methods.
Results
Design of an integrated next-generation sequencing platform for comprehensive genetic analyses in acute myeloid leukemia
Our NGS platform for comprehensive genetic character- ization of AML samples was designed to enable fast and reliable detection of genetic aberrations that are of critical importance for diagnosis, prognosis and therapy in adult AML.1–3,9 The complete workflow including three NGS- library preparations and data analysis by five different algorithms can be completed within 5 days (Figure 1A).
In order to limit the required sequencing resources to the capacities of a benchtop sequencing device, we addressed AML-relevant translocations on the level of RNA using anchored multiplex PCR10 for targeted enrichment of chimeric transcripts. RNA-based detection of common gene fusions in AML and DNA-based mutational screening are already available through predesigned commercial kits (Online Supplementary Tables S1-S4) with associated analy- sis software. Thus, we included numerical karyotyping into our platform by a strategy that does not require spe- cific target enrichment. In particular, we performed low coverage whole genome sequencing (lc-WGS), which has been shown previously to enable robust detection of CNV.11,12 For data analysis, we developed novel algorithms for the detection of CNV (Figure 1B) and KMT2A-PTD (CAI[N] and PTDi, respectively). Moreover, we added ITD-seek7 for the identification of FLT3-ITD to the avail- able bioinformatics pipelines for fusion- or mutation call- ing. Depending on the size of the mutation panel, one or two samples can be analyzed at a time on a standard (max- imum 15 x106 reads) flowcell (Figure 1C). Operational costs per sample are comparable to the total expenses for con- ventional cytogenetics, fluorescence in situ hybridization (FISH) and mutation analysis. Taken together, our integrat- ed NGS approach rapidly and economically delivers clini- cally meaningful insights into AML genomes, opening up the possibility to inform treatment decisions early based on molecular features and calculated cytogenetic informa- tion.
Principles of the CAI[N] algorithm and stability of in silico-generated reference karyotypes
In order to facilitate clinical interpretation, we modified the concept of “virtual” or “digital” karyotypes13,14 and con- structed “calculated karyotypes” from NGS data which resemble cytogenetic karyotypes. CNV in the range from cytogenetic bands to whole chromosomes are convenient- ly identified using a read depth approach15 and require only 5-10% genome coverage for detection with >90% sensitivity and specificity,16,17 corresponding to 1-2x106 reads. CAI[N] compares read frequencies in 1 Mb fixed genomic windows to in silico-generated normal reference karyotypes and maps amplified/deleted regions to cytoge- netic bands so that chromosomal gains or losses can be reported using cytogenetic notation (Figures 1B and 2A). Centromeres are not covered by our NGS karyotyping method as they include repetitive sequences that prevent unique alignment of sequencing reads.
To examine the stability of in silico reference karyotypes,
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haematologica | 2019; 104(2)