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Multicenter validation of targeted NGS in CLL
concordance rates of 96.2% (Multiplicom), 97.7% (Illumina) and 90% (HaloPlex). We next assessed the reproducibility of targeted NGS by looking at the inter- laboratory variation and found that 107of 115 mutations (93% concordance) were consistently detected by all six participating centers. Another encouraging finding was the accuracy or similarity i.e., narrow range, amongst the VAF recorded from all centers. Notable exceptions to the narrow range of VAF pertained to EGR2 and NFKBIE, which provided less homogeneous results, especially between different methodologies and, as mentioned above, these differences likely originate from the subopti- mal coverage obtained in certain regions of these genes.
There is currently no data that definitively determines what VAF cut-off is clinically significant, indeed this cut- off may vary depending on the particular gene in question, and the clinical relevance of any gene mutation can only be deemed by a clinical trial demonstrating a significant association with outcome. That said, a VAF cut-off of 5% or 10% is currently widely used in clinical routine. In this study, several variants were observed at VAF’s bordering the 5-10% threshold and hence would have been removed had a hard filtering step been applied. From a technical perspective, a plausible strategy to prevent clini- cally significant calls from being removed due to stringent cut-offs is to flag relevant variants as hotspots within the bioinformatic pipeline thus increasing the sensitivity by reducing the VAF threshold for that specific variant. Noted exceptions to this work-around would concern genes without defined hotspots i.e., tumor suppressor genes, within which, in theory, any mutation could be detrimen- tal e.g., TP53 and ATM. In order to circumnavigate the VAF/cut-off filtering issue in these instances, one solution would be to lower the technical cut-off of the method and avoid setting a hard cut-off for reporting and implement- ing a review and visualization step, thus removing the possibility of filtering out a potentially relevant mutation.
Owing to the heterogeneous subclonal composition of tumors, together with ongoing clonal evolution, clinical samples are often fraught with somatic mutations that exist at low VAF.26,28,29,40 As evidence accumulates it is becoming apparent that these low-level variants, at least within certain genes, may contribute to disease pathogen- esis or serve as early indicators of resistance to specific therapies.21-24,41-44 Despite data supporting the importance of somatic mutations with low VAF, their detection by conventional NGS is challenging. Even at a high read depth, NGS shows a marked decrease in the detection of somatic mutations with low VAF and accurate separation of erroneous variants from true low-level variants cannot always be achieved by stringent filtering alone. We per- formed an additional round of testing using a high sensi- tivity assay containing UMI as a comparator method. This decision was made for two reasons: (i) to obtain the most accurate estimate of VAF; and, (ii) to confidently detect low-frequency variants. When exploring the validity of minor variants using the dataset generated by the high sensitivity assay as a validator we found that although low frequency variants were detected by all techniques, greater diversity was observed for mutations with VAF <5%. While these results advocate the use of amplicon- based approaches for mutation detection within a clinical or research setting, the need for stringent validation should not be underestimated and is essential prior to the implementation of assays into routine. In order to further
help with this transition, ERIC has recently initiated a multi-center project specifically focusing on low-frequen- cy TP53 mutations with the aim to reach a consensus on whether the current cut-off of 10% for reporting TP53 mutations should be lowered.45
Our study is not without limitations, one of which relates to the fact that the mutational status of all genes to be analyzed was not known prior to their inclusion in the study. While desirable, this was not possible due to the difficulty in trying to obtain samples that had sufficient material to be distributed to all centers. However, this lim- itation was mitigated by performing an additional sequencing round using a high-sensitivity assay incorpo- rating UMI that was independent of the three gene panels being assessed. Another limitation related to the determi- nation of the sensitivity level for each assay included in the study. Although our study was focused on the compa- rability and reproducibility of results obtained in different centers using various amplicon-based assays, commercial controls harboring variants and allelic frequencies that have been well-characterized, would have been beneficial in determining the reliable limits of detection (which can differ depending on the specific variant). In addition, read counts from such controls can aid in the assessment of run variability. For the validation and use of NGS within a clinical laboratory service, sensitivity control samples with a range of VAF for target regions should always be includ- ed to ensure that the validated lower limit of detection is maintained while controls harboring specific variants and types of variants verify assay performance.
In conclusion, the data herein provides strong evidence that distinct gene panel designs and workflows have high analytical specificity and sensitivity and are capable of mak- ing consistent variant calls. Importantly, this assessment of interlaboratory reliability is not only paramount in the molecular diagnostics setting but also impacts on research where combining data generated from centers globally is crucial to provide homogeneous, reliable results and ulti- mately improve our understanding of rarer gene mutations, thus facilitating stratification of patients based on their molecular profiles. As NGS technologies continue to advance and assays extend beyond variant calling to include copy-number analysis and allow for the robust identifica- tion of clinically relevant low-frequency variants, diagnostic and research laboratories need to remain flexible so as to adapt in this dynamic era of precision medicine.
Disclosures
LAS has received honoraria from Abbvie, Gilead and Janssen. RR has received honoraria from AbbVie, Janssen, Illumina and Roche. JCS has received funding from Roche. KS has received honoraria and research funding from AbbVie and Janssen. DR has received research grants from Abbvie, Cellestia, Gilead, Janssen and honoraria from Abbvie, AstraZeneca, Gilead, Janssen and Loxo. SS has received research grants and honoraria from AbbVie, AstraZeneca, Celgene, Gilead, GSK, Hoffmann La-Roche and Janssen. ET has received honoraria from Abbvie and Roche. EC has received funding from Gilead and honoraria from Jansen, Takeda and Celgene.
Contributions
LAS performed research, analysed the data and wrote the manuscript; VL, AN, DC, AS, EU, KSK, FN, MA, JM and TP performed research and analysed the data; JF, ZD, DO, DR, PG, JCS, SP, SS, FD and EC performed research, analysed the
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