Journal List > Ann Lab Med > v.46(3) > 1516095228

Krigstein, Jude, Jaffrey, Bye, Wang, Qiu, and Ma: Clinical Validation of a Rapid Automated Lymphoma Next-Generation Sequencing Panel

Abstract

Background

Our genomic understanding of lymphomas, a heterogeneous group of neoplasms, has grown exponentially. The latest World Health Organization (WHO) and International Consensus classifications reflect the importance of genetic assessment in the diagnosis and prognostication of and therapeutic decision making in lymphoid neoplasms. To address this clinical need for routinely available and timely testing, we aimed to validate the Ion AmpliSeq Liverpool Lymphoid Network Panel (IALLNP; Thermo Fisher Scientific, Waltham, MA, USA).

Methods

We clinically validated the IALLNP on the Ion Torrent Genexus Sequencer (Thermo Fisher Scientific). The panel detects single-nucleotide variants (SNVs) and insertions/deletions (indels) in 60 clinically relevant genes. The validation set included a commercial control and 54 DNA samples covering the spectrum of clinically aggressive and indolent lymphomas.

Results

After optimizing for poor coverage regions, recurrent artifacts, and false-negative calls, the panel showed good performance in terms of depth of coverage, on-target reads, and uniformity. Its sensitivity for SNVs and indels at a lower limit of detection of 5% variant allele frequency (VAF) was 100%. Specificity in variant-negative samples was 100%, and the mean per-sample number of false-positive variants—which were easily identifiable and excluded upon interrogation of raw data—was 0.4. The panel demonstrated 92.8% reproducibility; however, all nonreproducible variants fell below the 5% VAF analytical threshold.

Conclusions

The IALLNP is an accurate and reproducible next-generation sequencing panel that delivers genetic results for lymphoid neoplasms in a clinically meaningful timeframe.

INTRODUCTION

Lymphomas are a highly diverse group of hematological neoplasms of variable cellular origins, pathologies, genetics, and clinical courses, ranging from highly aggressive to indolent. In the past, the diagnostic subclassification of lymphomas largely relied on morphological and immunophenotypic assessments. With the rise of next-generation sequencing (NGS)–based technologies in research and clinical practice, our genomic understanding of lymphoid neoplasms has grown exponentially [1]. The 5th edition of the WHO classification and the International Consensus Classification reflect the recognition of the integral role genomics currently plays in the classification and management of lymphoid neoplasms [24].
Complementing the traditional techniques of karyotyping and FISH, NGS profiling of single-nucleotide variants (SNVs) and small insertions/deletions (indels)—and less commonly, copy number variations—in disease-relevant genes is increasingly being employed in routine diagnostic lymphoma workflows [1, 57]. Currently, only a few lymphoid neoplasms are characterized by highly recurrent variants (e.g., MYD88 p.L265P [NM_002468.4] in lymphoplasmacytic lymphoma and BRAF p.V600E in hairy cell leukemia) [8, 9]. No single variant is pathognomonic, and most lymphoid neoplasms have a diverse and complex pattern of variants, reflecting their clinical heterogeneity [5, 10]. However, some genes are biased to certain lymphoma entities, such as SF3B1 in CLL and RHOA in angioimmunoblastic T-cell lymphoma (now known as nodal T-follicular helper-cell lymphoma, angioimmunoblastic type) [10, 11]. Hence, recurrent variant patterns may be useful to help resolve potentially challenging differential diagnoses. Molecular subclassification of diffuse large B-cell lymphoma (DLBCL) has been proposed since several groups identified recurring subtypes but is not yet implemented in routine clinical practice [1215].
Molecular test results have increasing prognostic and predictive impact. For example, the detection of TP53 variants in CLL has become crucial in therapeutic decision-making [16]. Patients with TP53-mutant subclones at low variant allele frequency (VAF), which may be detected by NGS-based techniques but missed by traditional Sanger sequencing, are less responsive to therapy and have poorer disease outcomes [17]. A landscape of therapeutic targets in lymphoid malignancies, such as EZH2 variants in follicular lymphoma predicting response to tazemetostat [18], is emerging. Moreover, genomic testing can identify variants in BTK, PLCG2, and CARD11 that confer resistance to Bruton tyrosine kinase (BTK) inhibitors. This is of increasing importance as the therapeutic armamentarium of covalent and noncovalent BTK inhibitors and degraders expands [19, 20]. Certain BCL2 variants are associated with venetoclax resistance in CLL [21].
To incorporate NGS assessment of lymphoid malignancies into routine clinical practice, assays must be optimized for use with all sample types, including formalin-fixed paraffin-embedded (FFPE) tissues, and must provide a clinically meaningful turn-around time [1]. To address this need, we aimed to analytically validate the Ion AmpliSeq Liverpool Lymphoid Network Panel (IALLNP; Thermo Fisher Scientific, Waltham, MA, USA), using an Ion Torrent Genexus Integrated Sequencer (Thermo Fisher Scientific). The assay and workflow aim to provide rapid actionable results across the spectra of sample types and lymphoid malignancies.

MATERIALS AND METHODS

Panel design and gene coverage

The IALLNP is a commercially available amplicon-based DNA-sequencing assay that targets 60 genes (the complete coding regions of 17 genes and exonic hotspots in 43 genes) recurrently mutated in lymphoid malignancies. The panel includes two primer pools generating 1,337 amplicons. It detects SNVs and insertions/deletions (indels) but not copy number variation, loss of heterozygosity, structural rearrangements, and aneuploidies.

Patient samples

For assay validation, we used 54 samples, including FFPE lymph-node tissues (N=22), bone-marrow (BM) aspirates (N=22), and peripheral blood (PB; N=10), previously tested using a hybridization capture-based NGS assay [22] at an external accredited laboratory. Additionally, one commercial reference standard, SeraSeq Lymphoma DNA Mutation Mix (0710-2203; Seracare, Milford, MA, USA), was used. The testing indications included a wide spectrum of lymphoid malignancies: CLL, mantle cell lymphoma, DLBCL, marginal zone lymphoma, primary mediastinal B-cell lymphoma, Waldenstrom’s macroglobulinemia, and T-cell lymphomas. The publication of this study data was approved by the Human Research Ethics Committee of St. Vincent’s Hospital (Sydney, Australia).

DNA extraction and quantification

DNA from BM aspirates and PB was extracted using the EZ1&2 DNA Blood 350 μL Kit on an EZ2 Connect MDX instrument (Qiagen, Hilden, Germany). The neoplastic content in FFPE samples was assessed using hematoxylin and eosin staining. Macro-dissection was performed when necessary to enrich tumor tissue or to exclude poor-quality tissue. FFPE DNA was extracted using the MagMAX FFPE DNA/RNA Ultra Kit on a Kingfisher Duo Prime Magnetic Particle Processor (Thermo Fisher Scientific). Extracted DNA was quantified using the Qubit HS dsDNA Assay Kit (Thermo Fisher Scientific) and diluted to a working concentration.

Library preparation and sequencing

The Ion Torrent Genexus Integrated Sequencer performs automated barcoded library preparation, templating, and sequencing. Each run can include a maximum of 15 samples and a no-template control (NTC) when utilizing all four lanes on the Ion Torrent GX5 semiconductor chip. The minimum input amount is 27.75 ng gDNA per sample.

Data analysis and bioinformatics

Genexus software v6.8.1.1 (and the latest update, 6.8.2.0; Thermo Fisher Scientific) was used to align reads against the GRCh37/hg19 human reference genome using the Torrent Mapping Alignment Program, and Torrent Variant Caller v5.20 was used to evaluate and annotate variants. Variants are described according to Human Genome Variation Society (HGVS) nomenclature v20.05 (manual correction of indels and duplications is often required). The provided hotspot file was expanded via a comprehensive literature search for clinically significant variants in the genes covered by the panel. Optimized variant-finding parameter settings included a minimum cutoff VAF of 2% for indels and 2.5% for hotspots and SNVs; minimum assigned quality score of 25 for indels and 10 for hotspots and SNVs; and maximum strand bias tolerance of 0.9 for SNVs, 0.85 for indels, and 0.96 for hotspots. A customized filter chain was created using the Genexus software to include variants that 1) were included in the Oncomine Variant Annotation Database; 2) were listed as pathogenic or likely pathogenic in ClinVar; or 3) remained present after the exclusion of University of California, Santa Cruz common single-nucleotide polymorphisms, non-exonic (allowing a 5-bp splicing region) and synonymous variants (variant effect settings were restricted to: missense, nonframeshiftInsertion, nonframeshiftDeletion, nonframeshiftBlockSubstitution, nonsense, stoploss, frameshiftInsertion, frameshiftDeletion, and frameshiftBlockSubstitution), and variants present in population databases at a frequency >10−4.
Candidate variants were visually inspected using Integrative Genomics Viewer (IGV) v2.16 (Broad Institute, Cambridge, MA, USA) and manually annotated using the Catalogue of Somatic Mutations in Cancer, Genome Aggregation Database v4.0, The TP53 Database (when relevant), UniProt, and literature review [2326]. Clinically significant variants were defined as those having an impact on patient diagnosis, prognosis, and/or treatment (predicting therapeutic response or resistance) according to a modified version of the College of American Pathologists/Association for Molecular Pathology joint consensus guidelines [27]. Variants of uncertain clinical significance are reported, whereas variants deemed benign or likely benign are not. Concordance analysis was limited to genomic regions covered by both the IALLNP and the externally accredited laboratory panel. Data were analyzed using Microsoft Office Excel software v2411 (Microsoft Corporation, Redmond, WA, USA).

RESULTS

Run and sample quality metrics

Fifteen validation sequencing runs were performed. Analysis of the initial runs revealed that some amplicons, often in repetitive and GC-rich areas, consistently underperformed (mean depth consistently <100×). These amplicons were omitted from the target region browser extensible display file as the consistent lack of coverage compromised variant calling reliability in these regions. Moreover, the omitted amplicons were confirmed to be devoid of any recurrent clinically significant variants (except for the minor hotspot, BCL2 G47). The final panel coverage is provided in Table 1 (excluded regions are listed in Supplemental Data Table S1). With these improvements, the mean overall run metrics were: Ion Sphere Particle loading, 91.70% (range, 85.18%–94.81%); enrichment, 98.94% (97.47%–99.76%); final reads, 45.18% (38.85%–50.00%); polyclonality, 43.17% (39.08%–47.96%); and raw read accuracy, 98.06% (97.68%–98.38%). The mean overall sample metrics were: mean read length, 122.7 bp; mapped reads, 3,178,028; mean depth, 2,494×; and uniformity, 94%. FFPE samples had a lower mean read length (6.7 bp on average), similar mean depth (2,482× vs. 2,503×), and 1.1% lower uniformity than PB/BM samples (Supplemental Data Table S2). The average NTC metrics were: mean read length, 45.5 bp and mean depth, 9.4×. For the retained 1,319 amplicons, an average of 96.57% targeted bases were covered at 350× depth, and 99.27% at 100×. The most common amplicons not reaching 350× are detailed in Supplemental Data Table S3. The QC metric values are provided in Table 2.

Variant calling accuracy

The external laboratory identified 163 variants across the 54 validation samples, 15 of which were in regions not covered by the IALLNP, resulting in 148 candidate variants. The IALLNP detected 182 variants, representing 133 of 148 of the candidate variants, and 49 that were in regions not covered by the external reference laboratory (with no orthogonal option available for validation). These 182 variants included 136 missense, 14 splicing, and 32 indel variants. The commercial reference standard included 20 assessable variants, all of which were detected by the panel. Overall, 153 of 168 (91%) assessable variants were detected (Supplemental Data Table S2). Six false-negative calls were variants with VAFs of 3%–4.9%, and nine with VAFs of <3%. Notably, the IALLNP does not incorporate unique molecular identifiers. The detection rate was 135/135 (100%) for variants with VAF ≥5%; 16/22 (73%) for variants with VAF 3%–4.9%, and 2/11 (18%) for variants with VAF <3%. The Pearson correlation coefficient between IALLNP and reference laboratory VAFs was R2=0.9347 (Fig. 1).

Limit of detection

The limit of detection was determined using the SeraSeq Lymphoma DNA Mutation Mix diluted in a background of wild-type DNA. At expected VAFs of 3%–5% (1:1 dilution), the assay correctly detected 20 out of 20 variants, including 18 SNVs and two indels, twice (Table 3). At 1:2 dilution (expected VAFs, 2.5%–3.5%), all 20 variants were present; however, 5 of 20 were low-confidence calls held back by the filter settings. Combined with the above accuracy data, the lower limit of detection was established to be 5% VAF (100% sensitivity).

Specificity

The original IALLNP was provided without a sequence variant baseline (SVB) file, resulting in considerable numbers of false-positive calls. A baseline Oncomine Tumor Specific Panel-derived SVB file has since been provided. Unresolved recurrent false positives were identified and confirmed via manual interrogation using IGV. This required assessing each variant call against the sequencing results of other samples in the same run to detect sequencing artifacts (Supplemental Data Fig. S1). These artifacts were subsequently optimized bioinformatically (Supplemental Data Table S4). Following these improvements, the mean per-sample number of false-positive variant calls was 0.4. Five samples known to carry no variants had zero false-positive calls. The persisting false-positive variant calls were all easily identifiable as artifacts upon review in IGV and excluded.

Precision: Intra-assay and inter-assay reproducibility

To determine reproducibility, nine clinical samples were analyzed across two runs, and three samples were analyzed in duplicate over two runs. In total, 77 of 83 variants were reliably detected (92.8%). The six nonreproducible calls all had VAFs <5% (Supplemental Data Table S5). The SeraSeq standard was also analyzed using four separate runs, including twice in duplicate. Given the 20 assessable variants and six occasions, 119/120 variants (99.2%) were called reproducibly. BCL2 NM_000633.3: c.302G>C was missed on one occasion in a GC-rich area known to be challenging.

Time to results

We used NGS workflows existing in our laboratory for IALLNP validation. Microtomy, hematoxylin and eosin staining, and tumor slide assessment took approximately 2 hrs per batch of FFPE samples (5–10 samples). DNA extraction required 3–4 hrs for FFPE samples and 1 hr for fresh PB and BM samples. Fluorometric quantification, sample dilution, plate preparation, and Genexus instrument loading took approximately 2 hrs of hands-on time. The runtime on the instrument is 19 hrs for an eight-sample run using two lanes on the GX5 chip.

DISCUSSION

Genomic testing is increasingly important in the classification and clinical management of lymphoid neoplasms. Recognizing its value in patient care, the Australian government currently subsidizes NGS panel testing for myeloid and lymphoid malignancies. In this context, we assessed and analytically validated the IALLNP using PB, BM, and FFPE samples. The panel is comprehensive and incorporates the vast majority of genes recommended in consensus papers [28]. Exceptions include CD28, CD79A, DDX3X, JAK1, JAK3, KLF2, PLCG1 and TCF3. Our laboratory is currently working to incorporate these genes and to ensure broader coverage of BTK, DNMT3A, ETV6, and TET2 in an in-house customized version of the panel. The amplicon design and low DNA input requirements allow accurate results even with limited FFPE samples with a minimum of 10% tumor content, as confirmed by a pathologist. However, the amplicon design has limitations in terms of panel size and ease of target updates over time.
The assay is highly accurate in detecting SNVs and indels with VAFs≥5%, demonstrating 100% sensitivity and specificity at this cutoff. Between 3%–5% VAF, the sensitivity fell to 75% and, in our experience, most false-positive calls are in that VAF range. However, these can be distinguished via careful interrogation of the sequencing data across samples in the IGV. Excellent concordance and reproducibility were observed in both intra- and inter-assay testing.
False-negatives were reduced by ensuring a minimum tumor content; conducting QC checks on DNA extraction and Genexus run and sample metrics; lowering the default assay variant-finding parameter settings; creating a customized filter chain (including less strict population database thresholds); and expanding the hotspot file via a comprehensive literature review. Moreover, significant optimization was required to remove poorly performing amplicons and establish thresholds (or outright exclusion) for recurrent false positives. This required considerable bioinformatic support that may not be easily accessible. A developed and extensively tested assay-specific SVB file is clearly needed. We strongly recommend manual review of the raw data for all variant calls in IGV (particularly for low-VAF indels). Moreover, manual correction of HGVS nomenclature is often required for indels and duplications.
In combination with the highly automated Genexus system, the IALLNP assay is easy to perform and provides clinically meaningful turn-around times, with results available within 2 days. Variant curation can be challenging, as novel variants are regularly encountered and genomic pathway alterations often overlap among lymphoma entities. Importantly, the assay addresses only one facet of the diagnostic work-up and cannot detect large deletions, structural chromosomal rearrangements, or copy number alterations. Any molecular finding must be interpreted within the context of morphological findings combined with flow-cytometric, karyotypic, and FISH assessment.
Our validation study demonstrated that the IALLNP assay shows robust performance and provides results that can easily be integrated in real time in a diagnostic laboratory. Its implementation can aid in diagnostic categorization, prognostication, and therapeutic decision-making to facilitate individualized management of patients with lymphoma.

ACKNOWLEDGEMENTS

The authors acknowledge the generous support provided by clinical bioinformatics consultant Ms Lauren Olafson.

Notes

AUTHOR CONTRIBUTIONS

MK conceptualized and designed the study; MK, AJ, EJ, BW, and SB processed samples and analyzed experiments; MK, DM and MQ supervised the project; and MK and EJ drafted the first version of the manuscript. All authors revised and approved the final version of the manuscript.

CONFLICTS OF INTEREST

None declared.

RESEARCH FUNDING

None declared.

Appendix

SUPPLEMENTARY MATERIALS

Supplementary materials can be found via https://doi.org/10.3343/alm.2025.0254.

REFERENCES

1. de Leval L, Alizadeh AA, Bergsagel PL, Campo E, Davies A, Dogan A, et al. 2022; Genomic profiling for clinical decision making in lymphoid neoplasms. Blood. 140:2193–227. DOI: 10.1182/blood.2022015854. PMID: 36001803. PMCID: PMC9837456.
2. Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IB, Berti E, et al. 2022; The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia. 36:1720–48. DOI: 10.1038/s41375-022-01620-2. PMID: 35732829. PMCID: PMC9214472.
3. Campo E, Jaffe ES, Cook JR, Quintanilla-Martinez L, Swerdlow SH, Anderson KC, et al. 2022; The International Consensus Classification of Mature Lymphoid Neoplasms: a report from the Clinical Advisory Committee. Blood. 140:1229–53. DOI: 10.1182/blood.2022015851. PMID: 35653592. PMCID: PMC9479027.
4. Falini B, Martino G, Lazzi S. 2023; A comparison of the International Consensus and 5th World Health Organization classifications of mature B-cell lymphomas. Leukemia. 37:18–34. DOI: 10.1038/s41375-022-01764-1. PMID: 36460764. PMCID: PMC9883170.
5. Fend F, van den Brand M, Groenen PJ, Quintanilla-Martinez L, Bagg A. 2024; Diagnostic and prognostic molecular pathology of lymphoid malignancies. Virchows Arch. 484:195–214. DOI: 10.1007/s00428-023-03644-0. PMID: 37747559. PMCID: PMC10948535.
6. Stuckey R, Luzardo Henríquez H, de la Nuez Melian H, Rivero Vera JC, Bilbao-Sieyro C, Gómez-Casares MT. 2023; Integration of molecular testing for the personalized management of patients with diffuse large B-cell lymphoma and follicular lymphoma. World J Clin Oncol. 14:160–70. DOI: 10.5306/wjco.v14.i4.160. PMID: 37124135. PMCID: PMC10134203.
7. Breinholt MF, Schejbel L, Gang AO, Nielsen TH, Pedersen LM, Høgdall E, et al. 2023; Next generation sequencing in routine diagnostics of mature non-Hodgkin's B-cell lymphomas. Eur J Haematol. 111:583–91. DOI: 10.1111/ejh.14048. PMID: 37452559.
8. Tiacci E, Trifonov V, Schiavoni G, Holmes A, Kern W, Martelli MP, et al. 2011; BRAF mutations in hairy-cell leukemia. N Engl J Med. 364:2305–15. DOI: 10.1056/NEJMoa1014209. PMID: 21663470. PMCID: PMC3689585.
9. Treon SP, Xu L, Yang G, Zhou Y, Liu X, Cao Y, et al. 2012; MYD88 L265P somatic mutation in Waldenström's macroglobulinemia. N Engl J Med. 367:826–33. DOI: 10.1056/NEJMoa1200710. PMID: 22931316.
10. Rosenquist R, Rosenwald A, Du MQ, Gaidano G, Groenen P, Wotherspoon A, et al. 2016; Clinical impact of recurrently mutated genes on lymphoma diagnostics: State-of-the-art and beyond. Haematologica. 101:1002–9. DOI: 10.3324/haematol.2015.134510. PMID: 27582569. PMCID: PMC5060016. PMID: 303bd13f8cb34524a922967526f30fb7.
11. Vallois D, Dobay MP, Morin RD, Lemonnier F, Missiaglia E, Juilland M, et al. 2016; Activating mutations in genes related to TCR signaling in angioimmunoblastic and other follicular helper T-cell-derived lymphomas. Blood. 128:1490–502. DOI: 10.1182/blood-2016-02-698977. PMID: 27369867.
12. Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, et al. 2018; Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med. 24:679–90. Erratum in: Nat Med 2018;24:1290-1. DOI: 10.1038/s41591-018-0016-8. PMID: 29713087. PMCID: PMC6613387.
13. Schmitz R, Wright GW, Huang DW, Johnson CA, Phelan JD, Wang JQ, et al. 2018; Genetics and pathogenesis of diffuse large B-cell lymphoma. N Engl J Med. 378:1396–407. DOI: 10.1056/NEJMoa1801445. PMID: 29641966. PMCID: PMC6010183.
14. Lacy SE, Barrans SL, Beer PA, Painter D, Smith AG, Roman E, et al. 2020; Targeted sequencing in DLBCL, molecular subtypes, and outcomes: A Haematological Malignancy Research Network report. Blood. 135:1759–71. DOI: 10.1182/blood.2019003535. PMID: 32187361. PMCID: PMC7259825.
15. Wright GW, Huang DW, Phelan JD, Coulibaly ZA, Roulland S, Young RM, et al. 2020; A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell. 37:551–68.e14. DOI: 10.1016/j.ccell.2020.03.015. PMID: 32289277. PMCID: PMC8459709.
16. Paul P, Stüssi G, Bruscaggin A, Rossi D. 2023; Genetics and epigenetics of CLL. Leuk Lymphoma. 64:551–63. DOI: 10.1080/10428194.2022.2153359. PMID: 36503384.
17. László T, Kotmayer L, Fésüs V, Hegyi L, Gróf S, Nagy Á, et al. 2024; Low-burden TP53 mutations represent frequent genetic events in CLL with an increased risk for treatment initiation. J Pathol Clin Res. 10:e351. DOI: 10.1002/cjp2.351. PMID: 37987115. PMCID: PMC10766018. PMID: 14b09c0fb7f24e7ba0907bcd4ceec468.
18. Julia E, Salles G. 2021; EZH2 inhibition by tazemetostat: Mechanisms of action, safety and efficacy in relapsed/refractory follicular lymphoma. Future Oncol. 17:2127–40. DOI: 10.2217/fon-2020-1244. PMID: 33709777. PMCID: PMC9892962.
19. Wang E, Mi X, Thompson MC, Montoya S, Notti RQ, Afaghani J, et al. 2022; Mechanisms of resistance to noncovalent Bruton's tyrosine kinase inhibitors. N Engl J Med. 386:735–43. DOI: 10.1056/NEJMoa2114110. PMID: 35196427. PMCID: PMC9074143.
20. Tam CS, Balendran S, Blombery P. 2025; Novel mechanisms of resistance in CLL: Variant BTK mutations in second-generation and noncovalent BTK inhibitors. Blood. 145:1005–9. DOI: 10.1182/blood.2024026672. PMID: 39808800.
21. Blombery P. 2020; Mechanisms of intrinsic and acquired resistance to venetoclax in B-cell lymphoproliferative disease. Leuk Lymphoma. 61:257–62. DOI: 10.1080/10428194.2019.1660974. PMID: 31533509.
22. Blombery P, Thompson ER, Nguyen T, Birkinshaw RW, Gong JN, Chen X, et al. 2020; Multiple BCL2 mutations cooccurring with Gly101Val emerge in chronic lymphocytic leukemia progression on venetoclax. Blood. 135:773–7. DOI: 10.1182/blood.2019004205. PMID: 31951646. PMCID: PMC7146015.
23. UniProt Consortium. 2025; UniProt: The universal protein knowledgebase in 2025. Nucleic Acids Res. 53:D609–17. DOI: 10.1093/nar/gkae1010. PMID: 39552041. PMCID: PMC11701636.
24. Sondka Z, Dhir NB, Carvalho-Silva D, Jupe S, Madhumita , McLaren K, et al. 2024; COSMIC: A curated database of somatic variants and clinical data for cancer. Nucleic Acids Res. 52:D1210–7. DOI: 10.1093/nar/gkad986. PMID: 38183204. PMCID: PMC10767972.
25. Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, et al. 2024; A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 625:92–100. DOI: 10.1038/s41586-023-06045-0. PMID: 38057664. PMCID: PMC11629659.
26. de Andrade KC, Lee EE, Tookmanian EM, Kesserwan CA, Manfredi JJ, Hatton JN, et al. 2022; The TP53 Database: Transition from the International Agency for Research on Cancer to the US National Cancer Institute. Cell Death Differ. 29:1071–3. DOI: 10.1038/s41418-022-00976-3. PMID: 35352025. PMCID: PMC9090805.
27. Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI, Roy S, et al. 2017; Standards and guidelines for the interpretation and reporting of sequence variants in cancer: A joint consensus recommendation of the association for molecular pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 19:4–23. DOI: 10.1016/j.jmoldx.2016.10.002. PMID: 27993330. PMCID: PMC5707196.
28. Sujobert P, Le Bris Y, de Leval L, Gros A, Merlio JP, Pastoret C, et al. 2019; The need for a consensus next-generation sequencing panel for mature lymphoid malignancies. HemaSphere. 3:e169. DOI: 10.1097/HS9.0000000000000169. PMID: 31723808. PMCID: PMC6745936. PMID: 1bb96dfca0684750b0271bcca126b8d7.

Fig. 1
Pearson correlation between IALLNP and reference laboratory VAFs.
Abbreviations: VAF, variant allele frequency; IALLNP, Ion AmpliSeq Liverpool Lymphoid Network Panel.
alm-46-3-319-f1.tif
Table 1
Target regions of the IALLNP assay (after the exclusion of poorly performing amplicons)
Gene Transcript Coverage Gene Transcript Coverage
ARID1A NM_006015.6 Exons 2–20 MAP2K1 NM_002755.4 Exons 2, 3, 6
ATM NM_000051.4 All coding MEF2B NM_001145785.2 Exons 2–8
B2M NM_004048.4 All coding MYC NM_002467.6 All coding
BCL2 NM_000633.3 All coding (except p.20–49) MYD88 NM_002468.5 Exons 3–5
BCL6 NM_001706.5 Exons 1, 9, 10 NFKBIE NM_004556.3 All coding
BIRC3 NM_182962.3 Exons 7, 9, 10 NOTCH1 NM_017617.5 Exons 26–28, 34, and 3-UTR
BRAF NM_004333.6 Exons 11, 15 NOTCH2 NM_024408.4 Exon 34 (except p.2435–2467)
BTK NM_000061.3 Exon 15 NRAS NM_002524.5 Exons 2, 3
CARD11 NM_032415.7 Exons 3–10 PAX5 NM_016734.3 Exons 1–9
CCND1 NM_053056.3 Exon 1 PIM1 NM_002648.4 All coding
CCND3 NM_001760.5 Exon 5 PLCG2 NM_002661.5 Exons 19, 20, 24, 27, 30
CD79B NM_001039933.3 Exons 5, 6 POT1 NM_015450.3 Exons 5–8, 10
CDKN2A NM_001195132.2 Exons 1 (except p.25–38), 2 PRDM1 NM_001198.4 All coding (except p.293–310, 493–499)
CREBBP NM_004380.3 Exons 1-30 (except p.877–898) PTEN NM_000314.8 All coding
CXCR4 NM_003467.3 Exon 2 RHOA NM_001664.4 Exon 2
DIS3 NM_014953.5 All coding RPS15 NM_001018.4 Exon 4
DNMT3A NM_022552.5 Exons 4–8, 13–23 (except p.92–129) SAMHD1 NM_015474.4 All coding
EP300 NM_001429.4 Exons 1, 2, 5, 6, 9, 14, 16, 17, 20, 21, 23–31 SF3B1 NM_012433.4 Exons 14–16
ETV6 NM_001987.5 Exons 1, 5–8 SGK1 NM_001143676.1 Exons 4–12, 14
EZH2 NM_004456.5 Exons 16, 18 SMARCA4 NM_001128849.3 Exons 4, 5, 8, 19, 25, 26, 32, 34, 36
FAS NM_000043.6 Exons 6, 7, 9 SOCS1 NM_003745.1 All coding (except p.24–58)
FBXW7 NM_033632.3 Exons 9, 10, 12 STAT3 NM_139276.3 Exons 19–22
FOXO1 NM_002015.4 Exon 1 (p.1–70, 167–210) STAT5B NM_012448.4 Exons 14–18
GNA13 NM_006572.6 All coding STAT6 NM_003153.5 Exons 9–14
HRAS NM_001130442.2 Exons 2, 3 TENT5C NM_017709.4 All coding
ID3 NM_002167.5 All coding TET2 NM_001127208.3 Exons 3, 6, 7, 10, 11
IDH2 NM_002168.4 Exon 4 TNFAIP3 NM_001270507.2 All coding (except p.492–528)
IRF4 NM_002460.4 Exons 2 (except p.1–14, 62–72), 9 TNFRSF14 NM_003820.3 Exons 1–6
KMT2D NM_003482.4 Exons 3–54 (except p.802–831, 3,619–3,656) TP53 NM_000546.6 All coding
KRAS NM_033360.4 Exons 2, 3 XPO1 NM_003400.4 Exon 15

Abbreviation: IALLNP, Ion AmpliSeq Liverpool Lymphoid Network Panel.

Table 2
QC metric values
Metric Pass Flag Fail
Run Loading >85% 80%–85% ≤80%
Enrichment >97% 95%–97% ≤95%
Final reads >45% 40%–45% ≤40%
Raw read accuracy >97% ≤97%
Sample Mean read length (bp) >115 100–115 <100
Mapped reads (N) >2,500,000 2,000,000–2,500,000 <2,000,000
Mean depth >2,000 1,500–2,000 <1,500
Uniformity (%) >94 90–94 <90
Table 3
Limit of detection established using dilutions of the SeraSeq Lymphoma DNA Mutation Mix
Gene Nucleic acid change Amino acid change Expected NGS VAF Neat result 1:1 (50%) dilution 1:2 (33%) dilution 1:3 (25%) dilution
Expected
VAF
Result Result (inter-run) Expected
VAF
Result Expected
VAF
Result
BCL2 c.302G>C p.G101A 8.8 13.68 4.4 6.19 7.8 2.93 4.44 2.20 4.2
BRAF c.1799T>A p.V600E 8 10.05 4 4.77 5.5 2.67 No call (present below filters 2.74%) 2.00 3.38
CXCR4 c.1013C>G p.S338* 7 8.57 3.5 4 5.2 2.33 3.72 1.75 2.74
CXCR4 c.1013C>A p.S338* 7.8 7.61 3.9 3.63 4.3 2.60 3.19 1.95 Not in VCF file
DNMT3A c.2645G>A p.R882H 8.5 9.46 4.25 4.7 5.9 2.83 3.35 2.13 Not in VCF file
EZH2 c.1922A>T p.Y641F 8.9 8.3 4.45 4.51 6.2 2.97 2.74 2.23 No call (present below filters 2.52%)
IDH2 c.515G>A p.R172K 8.8 10.17 4.4 4.92 5.6 2.93 3.89 2.20 No call (present below filters 2.48%)
MYD88 c.794T>C p.L265P 7.3 7.24 3.65 5.42 5.1 2.43 3.91 1.83 No call (present below filters 2.55%)
NOTCH1 c.7541_7542del p.P2514Rfs*4 8.7 8.73 4.35 4.85 7.4 2.90 3.3 2.18 3.27
NOTCH2 c.7198C>T p.R2400* 8.6 8.78 4.3 4.58 4.5 2.87 No call (present below filters 2.52%) 2.15 Not in VCF file
RHOA c.50G>T p.G17V 8.8 7.86 4.4 6.15 6.1 2.93 2.95 2.20 Not in VCF file
SF3B1 c.2098A>G p.K700E 8.6 7.65 4.3 6.16 8.4 2.87 3.89 2.15 3.02
STAT3 c.1919A>T p.Y640F 7.7 6.76 3.85 4.52 5.1 2.57 3.21 1.93 No call (present below filters 2.41%)
STAT3 c.1982A>T p.D661V 6.9 6.22 3.45 4.11 5.7 2.30 No call (present below filters 2.71%) 1.73 No call (present below filters 2.56%)
STAT3 c.1940A>T p.N647I 6.1 6.41 3.05 4.16 3.9 2.03 No call (present below filters 2.7%) 1.53 3.66
STAT5B c.1994A>T p.Y665F 7.7 7.82 3.85 4.3 5.4 2.57 2.96 1.93 No call (present below filters 2.1%)
STAT5B c.1924A>C p.N642H 7.3 7.96 3.65 4.51 5.8 2.43 No call (present below filters 2.3%) 1.83 No call (present below filters 2.3%)
TP53 c.743G>A p.R248Q 10.3 10.57 5.15 6.69 7.3 3.43 4 2.58 3.5
TP53 c.820del p.V274Ffs*71 9.6 10.66 4.8 5.61 7 3.20 3.83 2.40 3.39
TP53 c.818G>A p.R273H 8.5 8.2 4.25 4.85 6 2.83 3.3 2.13 No call (present below filters 2.5%)

Equivalent to NM_002468.5:c.755T>C p.(L252P)

Abbreviations: NGS, next-generation sequencing; VAF, variant allele frequency; VCF, variant call format.

TOOLS
Similar articles