Abstract
Introduction: Chronic Lymphocytic Leukemia (CLL) is a deeply heterogeneous disease from both biological and clinical points of view. This heterogeneity is partly reflected by differences in the mutation status of immunoglobulin variable heavy chain (IgHV) genes. According to this criterion, two main subsets are distinguished by whether CLL cells express an unmutated or mutated, reflecting the stage of normal B cell differentiation from which they originate. These groups also have important clinical differences in terms of clinical outcome. Thus, patients with unmutated IgHV (UM; ≥98% of identity to the germline) genes have a more aggressive disease course and develop more frequently unfavourable genetic deletions or mutations than patients with mutated IgHV (M; <98%).
Currently, IgHV Somatic Hypermutation testing is assessed by Sanger sequencing (Sseq), which allows defining the main clone. However, it is not always possible to identify different clones in subclonal patients, which accounts for 20% of cases. Deep next generation sequencing (NGS) of the IgHV locus using consensus primers could broad the availability of deciphering the heterogeneity of CLL.
Objective: Validation of a deep next generation sequencing (NGS) tool to analyze the IgHV locus and determinate the presence of multiple productive rearrangements in a series of patients with CLL.
Methods: We included 140 samples extracted from peripheral blood of patients diagnosed of CLL according to the National Cancer Institute Working Group guidelines in our institution between 1986 and 2017 (median absolute lymphocytes 11.4x109/L [2,8-239,5x109/L]). Sseq amplification and sequence analysis of IgHV rearrangements were performed on either DNA or cDNA conforming to the updated ERIC recommendations. In any cases we were able to determinate the IGVH identity (68 M and 72 UM) and the stereotypic subset.
For IgHV-NGS analysis gDNA was amplified using the Sequencing Multiplex Kit based on based on IGH leader (forward primers) and consensus JH (reverse primer), master mixes and PCR products were purified using magnetic beads, normalized and pooled to create a library for sequencing using a MiSeq equipment. Sequencing data were analyzed using our bioinformatics pipeline.
Results:
The median of: a) total sequence count analyzed per patient was 2492 (111-17445); b) most abundant unique sequence was 984 (111-7029); c) homology of variable region (IgHV) was 99.3% (87.22-100). We excluded unproductive rearrangement, and found 81.42% (114/140) of patients with unique clone and 18.6% (26/140) with multiple productive rearrangements (Figure 1). The median frequency of the second most abundant productive clone was 9.16% (3.33%-50%) (Table 1).
Our tool led to identify of a dominant clonotypic IgHV in all cases (140/140, 100%). When we compared the NGS sequence/mutational status for the most abundant clone with Sseq and for the IgHV status determination, only 1 of 140 (0,7%) showed a major clone with productive rearrangement mutated by Sseq but unmutated by NGS.
On the basis of IgVH-NGS subclonal profiles recently described, we identified the following distribution: 5.71% (8/140) of patients presented multiple M clones; 35% (49/140) 1 M clone; 13.6% (19/140) mix of M-UM clones; 37.85% (53/140) 1 UM clone; and 7.85% (11/140) multiple UM clones (Figure 2).
Conclusion:
The results indicate that our tool is useful to approach IgVH rearrangements since we were able both classified correctly according to IgVH mutational status, and V gene concordant. Moreover it was permitted to provide both the complete V-D-J sequence as well as a relative quantification of different subclones.
Acknowledgments: Samples provided by the INCLIVA Biobank (PT13/0010/0004). Supported by: FISS PI 10/02095 and FISS PI 14/02018
Solano: Neovii: Honoraria.
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