Background: Next generation sequencing (NGS) is an integral component in the characterization of hematologic malignancies, including chronic lymphocytic leukemia (CLL). Fluorescence in situ hybridization (FISH) and conventional cytogenetics (CC) are cost effective and are currently the gold standard for detecting copy number abnormalities (CNAs) in hematologic malignancies. NGS is emerging as a comprehensive assay that can detect CNAs while surveying the whole genome for single nucleotide variants and loss of heterozygosity (CN-LOH). Identifying CNA events in addition to mutations and RNA fusions may help identify and characterize the highly complex genetic landscape of hematologic malignancies.

Methods: A custom total nucleic acid (TNA) NGS panel was designed which consists of mutation profiles of 297 genes, transcriptome profile of 213 genes, and genomic backbones of 14 chromosomes to identify unbalanced abnormalities. Two-hundred seventy CLL patients were included in the study (abnormalities detected in 236 cases in total: 61 cases by CC; 230 cases by FISH; and 53 cases by both CC and FISH, and no abnormalities detected in 34 cases by both FISH and CC). Mutation profiles including SNVs, indels, and structural changes were interrogated with a custom bioinformatic pipeline which utilized PureCN and CNVkit algorithms to identify structural changes. NGS results were compared to results of CC and FISH. CNA detection of sex chromosome and balanced rearrangement including translocation and inversion was excluded from the analysis

Results: CNAs were detected by NGS in 56 of 61 cases (91%) reported by CC and in 178 of 230 cases (77%) detected by FISH. Seventy-seven CNAs detected by CC and 202 CNAs detected by FISH were identified by NGS. NGS failed to detect 13q deletion, detected by FISH in 48 cases. Abnormalities not detected by neither cytogenetics nor FISH were detected by NGS in 108 (gain) and 32 (loss) cases. In addition, we observed abnormalities in 9 of 34 cases by NGS reported as normal by both FISH and cytogenetics. CN-LOH was detected in 9% of cases predominantly on 13q, 17p and 22q.

In addition to trisomy 12, gains of 20p and 20q were observed in each 72 (30%) and 43 (18%) cases. CN gains of 7p, 8q, and 17q were also observed in 12%, 12%, and 7% of cases, respectively. Oncogenic driver mutations in KRAS (p.G12D) and (p.G13D) were observed in four and five cases with CN gains, respectively. IKZF3, a recurrent hotspot pathogenic mutation in CLL and a potential prognostic marker that may positively regulate MYC, was detected in five patients with CN gains.

CN loss of 11q, 2q, 13q, 3p, 17p, 21q, and 6q were among the most common chromosomes with CN loss (Figure 1). Notably, LOH of RB1, DLEU7, COG3, and FOX1 genes on 13q, of TP53, WRAP53, SLC52A1, CTC1, and ABR genes on 17p and of PRDM1, EPHA7, and CASP8AP2 genes on 6q were observed. Identifying cases with 13q14 deletions that include RB1 could change the CLL patient management due to the aggressive clinical course. Recurrent loss of function mutations in KMT2C (p.E2798Gfs*11), NOTCH1 (p.P2514Rfs*4), and TP53 (p.H179R) in 7q, 9q, and 17p were observed. Identifying both CN loss combined with loss of function mutations in tumor suppressors could help improve patient care.

Conclusions: Abnormalities detected by cytogenetics were mostly detected by NGS, but NGS offers a higher resolution including CN events of various length, LOH events, and single gene mutations. CNAs detected at higher resolution is useful in identifying patients with 13q14 loss that include/exclude RB1 which may affect patient management. However, an accurate detection of the CNA could be affected in part by a baseline established by a panel of normal and the depth of coverage. Differences in sensitivity of methodologies can also be attributed to in vitro proliferation and tissue culture conditions utilized for CC analysis. CC and FISH can identify clones with multiple abnormalities as well as clonal evolution. Comprehensive genomic profile including high resolution copy number changes and mutational profiles, detectable by NGS, may provide better profiling for a patient for clinical management.


Jung:NeoGenomics: Current Employment. Thangavelu:NeoGenomics: Current Employment. Nam:NeoGenomics: Current Employment. Bender:NeoGenomics: Current Employment. Agersborg:NeoGenomics: Current Employment. Weiss:Bayer: Other: speaker; Genentech: Other: Speaker; Merck: Other: Speaker; NeoGenomics: Current Employment. Funari:NeoGenomics: Current Employment.

Author notes


Asterisk with author names denotes non-ASH members.