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C. Baer et al.
and an acquired variant in the TET2 gene. The variant could certainly prove clonality, which is essential for diag- nosing clonal cytopenia of undetermined significance. The variant would therefore be tier I or tier II in the diag- nostic classification. However, if the same variant is found in an patient with acute myeloid leukemia, for whom the therapeutic procedure remains to be defined, the classification becomes more complicated. Currently, no specific therapy is available for TET2-positive malig- nancies. The variant could therefore end up in tier III if a strict interpretation of the guidelines is used.
Hematologic diseases are closely related and mutations are generally typical, but not exclusive to one disease. For example, the L265P variant in MYD88 is found in 90% of all patients with Waldenström macroglobulinemia, but is also present, at a lower frequency, in patients with other B-cell neoplasms.27 Consequently, no variant has the sen- sitivity or specificity to qualify as “diagnostic” when the rules are applied strictly. For a patient, all three of the cat- egories (diagnostic, prognostic, predictive) are important. The presence of a MYD88 mutation suggests that ibrutinib therapy is an option.28 Therefore, L265P would probably be classified as tier I in most interpretations.
Unlike MYD88 L265P, many genes do not have well- described mutation hotspots. A large variety of variants are observed in genes such as RUNX1, CEBPA and DNMT3A. Databases can be helpful when the variant has been described before. An example is the Catalog of Somatic Mutations in Cancer (COSMIC),29 which has col- lected information on mutations from peer-reviewed journals since 2004.30 The UMD database contains 6,870 variants for TP53 alone.31 COSMIC is manually curated and the UMD-TP53 has developed its own data-driven curation strategy.32 For genes which are less well under- stood than TP53, databases are less helpful.
Alternatively, algorithms that predict the effect of vari- ants on protein structure could be used. In silico analyses are immediately available and can be performed without expert knowledge. Major influencing factors include the type of amino acid exchange and the location of the vari- ant in conserved or functional domains. The dbNSFP database33 contains pre-calculated values for all possible single nucleotide variants which result in amino-acid or splice-site changes in the human genome from 18 differ- ent algorithms. However, the read-out is not necessarily “yes” or “no”. Different algorithms almost never come to exactly the same result. Some well-known pathogenic variants are not rated high enough by common algo- rithms. For example, the W515L mutation in the MPL gene is known to be typical in myeloproliferative neo- plasms, but is rated as damaging/pathogenic by only seven of the 18 algorithms. Currently, most of the algo- rithms are trained on single nucleotide variants and can- not be applied to indels. Finally, it should be emphasized that a high pathogenicity score is not always a synonym for causality or actionability.
Tomorrow
There is no one-step solution for variant classification. Searching software for genetic variant interpretation on Google gives millions of hits. In a cross-laboratory com- parison of variant classification, there was only 34% con- cordance.34 The data provided by whole exome sequenc-
ing or whole genome sequencing will inevitably make the analysis more complex, since the number of identified variants is many times greater than with panel sequenc- ing. Variants will be found in all the approximately 20,000 human genes, but not all of them will be either relevant or redundantly mutated in a disease. A comparison of two large study sets (TCGA and BeatAML)35 confirmed that 33 genes are frequently mutated in acute myeloid leukemia, but there was diversity regarding genes with a mutation frequency of 2% or less. Over 2,000 genes were found to be mutated in only one of the datasets or even one patient. Current guidelines, such as those from the AMP,36 are based on characterized genes. Therefore, Kaur et al.7 argue that panels are preferred for breast cancer.37 They can achieve deeper coverage, which is synonymous with greater sensitivity. Sensitivity in the 1-3% range is increas- ingly required because subclones should be detected,38 or because clonal hematopoiesis of indeterminate potential is already diagnosed if a mutation is found with a VAF of 2%.39 The sensitivity of whole genome sequencing is cur- rently in the range of 15-20%, but the technique allows simultaneous identification of structural and copy number variations and the cost of sequencing is decreasing.40
We therefore suggest short-term strategies for current diagnostic use of large-scale datasets and long-term approaches to advance our understanding of the malig- nant process and therapeutic options.
Overstating the importance of a variant is clearly dan- gerous, but a report with variants of unknown clinical sig- nificance can be difficult to translate into clinical conse- quences. Here, we outline major aspects for today’s usage. First, collaboration between different laboratory branches is needed to compare genetic and other bio- markers and between laboratories and physicians to tailor personalized answers. For example, by integration of dif- ferent laboratory results, a patient in remission according to morphology, but still with a VAF of 50% could be iden- tified to have a rare and possibly less relevant germline variant. Another example derives from the growing awareness of germline predisposition e.g. with SAMD9/SAMD9L mutations in myelodysplastic syn- dromes.41 If the family background and reference material are provided, testing can be adjusted. Second, databases are the cornerstones of variant interpretation. An impres- sive example of the success of combined forces is gnomAD, which is now the worldwide reference for germline variants. Third, in the context of monitoring, serial testing can reveal the outgrowth of a clone with a specific variant and highlight clinical relevance, as demonstrated by retrospective studies.38,42 Well-docu- mented information from multiple time points is a resource for variant classification, also for following patients with the same variant, and ideally should be included in databases and classification algorithms in the future. Finally, filtering for known and well-studied changes is always a valid first step. The first whole- genome sequencing studies in hematology demonstrated respectable sensitivity and specificity when filtering for known copy number variations, structural variations and genes.43,44
Mutations outside coding regions are difficult to asso- ciate with functions. They influence gene expression by altering transcription factor binding, alternative splicing, and certain genomic variants are likely to be causal for the acquisition of chromosomal aberrations.45-47
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haematologica | 2019; 104(8)