Multicenter study of the ion AmpliseqTM Liverpool Lymphoid Network community NGS panel for molecular characterization of lymphoid malignancies

Abstract

Abstract Introduction Molecular testing is increasingly essential in lymphoid malignancies to support future efforts in molecular classification and precision oncology. A custom 60-gene, DNA-only amplicon NGS panel using Ion AmpliSeq™ technology was developed for genomic profiling of lymphomas and lymphoid leukemias. The panel targets key exons from 36 genes and full coding sequences from 24 genes, including some relevant non-coding regions (e.g., NOTCH1 3' UTR). Gene selection was informed by the 4th and 5th WHO classifications, 2022 ICC classification, NHS Genomic Test Directory, disease incidence, and key literature. The panel is compatible with fully automated workflows on the Ion Genexus™ (GX5 chip) and Ion GeneStudio™ (Ion 530 chip) platforms. A network of 9 laboratories across Europe and Africa were involved in the panel evaluation. Methods A 2-phase, multicentre study was conducted to assess the LLN-panel performance, across a variety of sample types (including formalin fixed paraffin embedded tissue, bone marrow aspirates, and peripheral blood) using 5 Genexus and 4 GeneStudio platforms. Phase 1 focused on amplicon performance and variant detection, using commercial controls and a heterogenous set of available pre-characterized clinical research haematological samples, and samples from unrelated solid tumours known to carry similar variants. Orthogonal data sources included amplicon and hybrid capture based NGS assays and qPCR. Of 148 samples, 137 met the QC criteria (>1500x mean coverage per sample; >98.65% raw read accuracy). Phase 2 assessed inter-laboratory concordance through a sample sharing strategy using clinical research samples. Three sets containing extracted DNA from 16 lymphoma clinical research samples were cross shared between 8 labs. All phase 2 samples were analyzed blind, using IonReporterTM with the Lymphoma_Network_530_ver_1.0 (5.18) pipeline. Results Phase 1, sample level sequencing metrics for the Ion Genexus/GeneStudio platforms across all sample types showed average of 3,037,788/3,697,164 reads, mean read length of 124 bp/129 bp, 93.08%/95.96% uniformity, mean read coverage of 2118x/3117x, 97.71%/98.60% amplicons at 100x coverage, 88.88%/94.75% reads at 500x coverage, respectively. Regarding variant calling performance, SeraSeq® Lymphoma DNA Mutation Mix control data reported 100% sensitivity and specificity. Across 68 samples with orthogonal data available, 163 of 174 expected variants with limit cut-off set at 5% variant allele frequency (VAF) were detected, yielding a sensitivity of 93.68%. Among the pre-characterized lymphoid samples, 136 variants had comparative information, showing 93.4% of detection rate. KMT2D, ATM, and NOTCH1 emerged as the most frequently altered genes within the benchmarked lymphoid subset, with most expected variants detected. Significant findings included a p.Cys481 BTK alteration associated with resistance to covalent BTK-inhibitors in a CLL sample; BRAF V600E altered in 2 HCL research samples. Furthermore, most TP53 variants detected by the LLN panel were concordant with orthogonal data; the few discrepancies observed corresponded to anon-lymphoid tumor sample. A small number of expected alterations across several genes were not identified, potentially due to suboptimal performance of specific amplicon regions. Phase 2 results showed an inter-lab concordance of 92,8% for variants with VAF>5% (96.4%; 97.8%; and 84.2%, for each set). Lower concordance in the third set suggests sample quality issues. Conclusions The Ion AmpliSeq™ Liverpool Lymphoid Network (LLN) NGS panel is a research tool that demonstrates strong sequencing performance, coverage, variant detection, and inter-lab concordance. Ongoing refinements to the panel's gene content is informed by emerging research to support improved molecular characterization of lymphoid malignancies. While the inclusion of additional gene targets may expand biological insights, it will also increase the number of variants of uncertain significance (VUS). Feedback from the LLN research network is used to inform updates to the bioinformatics workflow, with the aim of enhancing analytical sensitivity, specificity, and variant annotation.