| Literature DB >> 32561841 |
Frédéric Davi1, Anton W Langerak2, Anne Langlois de Septenville1, P Martijn Kolijn3, Paul J Hengeveld3, Anastasia Chatzidimitriou4, Silvia Bonfiglio5, Lesley-Ann Sutton6, Richard Rosenquist6,7, Paolo Ghia5, Kostas Stamatopoulos4,6.
Abstract
Twenty years after landmark publications, there is a consensus that the somatic hypermutation (SHM) status of the clonotypic immunoglobulin heavy variable (IGHV) gene is an important cornerstone for accurate risk stratification and therapeutic decision-making in patients with chronic lymphocytic leukemia (CLL). The IGHV SHM status has traditionally been determined by conventional Sanger sequencing. However, NGS has heralded a new era in medical diagnostics and immunogenetic analysis is following this trend. There is indeed a growing demand for shifting practice and using NGS for IGHV gene SHM assessment, although it is debatable whether it is always justifiable, at least taking into account financial considerations for laboratories with limited resources. Nevertheless, as this analysis impacts on treatment decisions, standardization of both technical aspects, and data interpretation becomes essential. Also, the need for establishing new recommendations and providing dedicated education and training on NGS-based immunogenetics is greater than ever before. Here we address potential and challenges of NGS-based immunogenetics in CLL. We are convinced that this perspective helps the hematological community to better understand the pros and cons of this new technological development for CLL patient management.Entities:
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Year: 2020 PMID: 32561841 PMCID: PMC7515836 DOI: 10.1038/s41375-020-0923-9
Source DB: PubMed Journal: Leukemia ISSN: 0887-6924 Impact factor: 11.528
Comparison of Sanger sequencing vs. NGS for IGHV gene SHM analysis.
| Sanger sequencing | NGS | |
|---|---|---|
| Pros | Relatively cheap | More detailed insight into subclonal architecture and intraclonal diversity |
| Relatively short TAT (1–2 weeks)* | Combination with other assays in one NGS workflow | |
| Adapted to small number of cases | Adapted to large number of cases | |
| Data interpretation mostly straightforward and highly standardized | Circumvents need of laborious (cloning) techniques in cases with biallelic rearrangements | |
| Cons | No insight into subclonal architecture and intraclonal diversity | (Still) relatively costly (although depending on the number of samples analyzed simultaneously) |
| No combination with other assays in one workflow possible | Longer TAT (>2 weeks)* | |
| Need of dedicated bioinformatics tools | ||
| Data interpretation more complicated and not (yet) standardized |
TAT turn around time.
*TAT is highly dependent on the number of analyzed samples (batch efficiency).
Fig. 1Intraclonal diversity apparent with NGS analysis.
a Vidjil display of sequences obtained from the clonotypic IGHV/IGHD/IGHJ gene rearrangement of a CLL case. All sequences correspond to the same IGHV3-72/IGHD2-2/IGHJ4 gene rearrangement and are grouped according to their identity. The size of each “bubble” reflects the sequence abundance, resulting in a dominant clonotype surrounded by multiple “satellite” minor variant clonotypes. b Nucleotide sequence alignment by IMGT/V-QUEST of the five most abundant clonotypes showing evidence of intraclonal diversity. Nucleotide variants within the VH CDR3 are boxed. Clonotype frequencies: clone 1, 35.2%; clone 2, 2.4%; clone 3, 1.5%; clone 4, 0.15%; clone 5, 0.13%.
Fig. 2Oligoclonality.
a GeneScan profiling of a CLL case showing a dominant clonal peak (385 bp) but also a very minor one (379 bp). Using Sanger sequencing, only the dominant clonal IGHV/IGHD/IGHJ gene rearrangement would be characterized. By contrast, NGS offers the possibility of providing sequence data for both rearrangements, as shown in these two types of visualization by Vidjil: either by “GeneScan-like” clonotype size (b), or by IGHV and IGHJ gene composition (c). The dominant clonotype (84.5% of all sequences) corresponds to a mutated (88.5% germline identity) IGHV4-4/IGHD1-26/IGHJ4 gene rearrangement, while the minor one (9.3% of all sequences) corresponds to a mutated (93.1% germline identity) IGHV3-7/IGHD3-16/IGHJ4 gene rearrangement. Both rearrangements are productive.