Challenging conventional karyotyping by next-generation karyotyping in 281 intensively treated patients with AML.

Mareschal S, Palau A, Lindberg J, Ruminy P, Nilsson C, Bengtzén S, Engvall M, Eriksson A, Neddermeyer A, Marchand V, Jansson M, Björklund M, Jardin F, Rantalainen M, Lennartsson A, Cavelier L, Grönberg H, Lehmann S

Blood Adv 5 (4) 1003-1016 [2021-02-23; online 2021-02-17]

Although copy number alterations (CNAs) and translocations constitute the backbone of the diagnosis and prognostication of acute myeloid leukemia (AML), techniques used for their assessment in routine diagnostics have not been reconsidered for decades. We used a combination of 2 next-generation sequencing-based techniques to challenge the currently recommended conventional cytogenetic analysis (CCA), comparing the approaches in a series of 281 intensively treated patients with AML. Shallow whole-genome sequencing (sWGS) outperformed CCA in detecting European Leukemia Net (ELN)-defining CNAs and showed that CCA overestimated monosomies and suboptimally reported karyotype complexity. Still, the concordance between CCA and sWGS for all ELN CNA-related criteria was 94%. Moreover, using in silico dilution, we showed that 1 million reads per patient would be enough to accurately assess ELN-defining CNAs. Total genomic loss, defined as a total loss ≥200 Mb by sWGS, was found to be a better marker for genetic complexity and poor prognosis compared with the CCA-based definition of complex karyotype. For fusion detection, the concordance between CCA and whole-transcriptome sequencing (WTS) was 99%. WTS had better sensitivity in identifying inv(16) and KMT2A rearrangements while showing limitations in detecting lowly expressed PML-RARA fusions. Ligation-dependent reverse transcription polymerase chain reaction was used for validation and was shown to be a fast and reliable method for fusion detection. We conclude that a next-generation sequencing-based approach can replace conventional CCA for karyotyping, provided that efforts are made to cover lowly expressed fusion transcripts.

Bioinformatics Support for Computational Resources [Service]

Clinical Genomics Uppsala [Collaborative]

PubMed 33591326

DOI 10.1182/bloodadvances.2020002517

Crossref 10.1182/bloodadvances.2020002517

pmc: PMC7903223
pii: S2473-9529(21)00122-1