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Assessing the diagnostic yield of targeted next-generation sequencing for melanoma and gastrointestinal tumors
Journal Pre-proof Assessing the diagnostic yield of targeted next-generation sequencing for melanoma and gastrointestinal tumors Swati Garg, Sylvie Gernier, Maksym Misyura, Mahadeo A. Sukhai, Mariam Thomas, Suzanne Kamel- Reid, Tracy Stockley PII:
S1525-1578(20)30014-3
DOI:
https://doi.org/10.1016/j.jmoldx.2019.12.008
Reference:
JMDI 882
To appear in:
The Journal of Molecular Diagnostics
Received Date: 18 June 2019 Revised Date:
19 November 2019
Accepted Date: 20 December 2019
Please cite this article as: Garg S, Gernier S, Misyura M, Sukhai MA, Thomas M, Reid SK-, Stockley T, Assessing the diagnostic yield of targeted next-generation sequencing for melanoma and gastrointestinal tumors, The Journal of Molecular Diagnostics (2020), doi: https://doi.org/10.1016/ j.jmoldx.2019.12.008. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Copyright © 2020 Published by Elsevier Inc. on behalf of the American Society for Investigative Pathology and the Association for Molecular Pathology.
NGS-based diagnostic yield comparison
Assessing the diagnostic yield of targeted next-generation sequencing for melanoma and gastrointestinal tumors Swati Garg,* Sylvie Gernier,†‡ Maksym Misyura,* Mahadeo A Sukhai,* Mariam Thomas,* Suzanne Kamel- Reid,*† ‡§ and Tracy Stockley*†‡
From the Advanced Molecular Diagnostics Laboratory,* Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario; Genome Diagnostics,† Department of Clinical Laboratory Genetics, Laboratory Medicine Program, University Health Network, Toronto, Ontario; and the Departments of Laboratory Medicine and Pathobiology,‡ and Medical Biophysics,§ University of Toronto, Toronto, Ontario, Canada
RUNNING HEAD: NGS based diagnostic yield comparison FUNDING: Princess Margaret Cancer Foundation CONFLICT OF INTEREST: NONE
CORRESPONDING AUTHOR Tracy Stockley Department of Clinical Laboratory Genetics University Health Network Eaton Wing, 11EB-418 200 Elizabeth Street Toronto General Hospital Toronto, ON, M5G 2C4 Email: [email protected] 1
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Current address of S.Ga., Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada, of M.M., Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, of M.A.S., Canadian National Institute for the Blind, 1929 Bayview Ave, Toronto, Ontario, Canada, and of M.T., Institute for Genomic Medicine, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH.
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Abstract A common rationale in molecular diagnostic laboratories is that implementation of next-generation sequencing (NGS) enables simultaneous multi-gene testing, allowing increased information benefit as compared to non-NGS assays. However, minimal published data exist to support this justification. In this study, we compared clinical diagnostic yield of TruSight Tumor 26 Sequencing Panel (TST26) in melanoma, colorectal (CRC) and gastro-intestinal stromal (GIST) tumors to nonNGS assays. 1041 formalin-fixed, paraffin embedded (FFPE) tumors, of melanoma, CRC and GIST were profiled. NGS results were compared to non-NGS single-gene or single-variant assays with respect to variant output and diagnostic yield. 79% melanoma and 94% CRC tumors were variant-positive by panel testing. TST26 panel improved BRAF variant detection in melanoma as compared to single-variant BRAF V600E/K routine tests by 24% and also detected variants in genes other than BRAF, NRAS and KIT which could impact patient management in 20% additional cases. NGS enhanced diagnostic yield in CRC by 36% when compared to routine single gene assays. In contrast, no added benefit of NGS based testing for GIST tumors was observed.TST26 panel either missed or inaccurately called complex insertion/deletion variants in KIT exon 11, which were accurately identified by nonNGS methods. Our findings demonstrate the differential impact of cancer site and variant type on diagnostic test information yield from NGS assays.
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Introduction As the number of tumor-associated biomarkers of diagnosis, prognosis and/or treatment response continues to increase, clinical diagnostic laboratories are implementing multi-gene NGS panels for routine molecular profiling of solid tumors18
. As NGS tests can simultaneously interrogate many genes or regions of interest,
the common justification for implementation of NGS in clinical molecular diagnostics laboratories are increased variant information with time and cost-savings over sequential single-gene or single-variant non-NGS tests9. In addition, multi-gene NGS tests can potentially use less input material from precious formalin fixed paraffin embedded (FFPE) tumor samples10-16. In this study, we assessed the diagnostic utility of implementation of a 26gene amplicon-based NGS panel (TruSight Tumor 26 Sequencing, TST26; Illumina) as part of routine molecular testing. Our study cohort comprised of 1041 tumor samples from melanoma and gastrointestinal tumors (colorectal and gastrointestinal stromal tumors) in our clinical molecular diagnostic laboratory at the University Health Network, a major academic medical Centre in Toronto. The TST26 panel contains 174 amplicons covering clinically relevant variants/regions in 26 genes of particular relevance in melanoma, colon, and lung, gastric and ovarian tumors. We investigated additional benefit on patient management of the NGS panel as compared to single-variant or single-gene non-NGS assays for melanoma, colorectal and GIST tumors. Results of the comparison refute the a priori assumption that NGS panel testing is superior over non-NGS assays for all tumor sites, and establish that the choice of test requires careful evaluation of the molecular landscape for the tumor types under investigation.
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Materials and methods Study cohort The study cohort consisted of DNA extracted from 1041 formalin-fixed paraffin embedded (FFPE) melanoma (n=687), colorectal (CRC, n=281) and gastrointestinal stromal (GIST, n=73) tumors as a part of routine molecular testing in our clinically accredited molecular diagnostic laboratory between January 2015 and October 2016. This study was reviewed and approved by the University Health Network Research Ethics Board (# 17-5380).
DNA Isolation from FFPE tumors Haematoxylin and eosin stained slides had previously undergone pathology review for identification of tumor and cellularity estimate. Tumors either macro-dissected from 10-12 unstained slides or isolated by punch biopsy (two 1 mm cores) from the FFPE block. After de-paraffinization, DNA was extracted using the QIAmicro DNA extraction kit (Qiagen, Germantown, MD) or the Maxwell FFPE extraction Kit (Promega, Madison, WI), according to standard protocols, and quantified by Quantus fluorometer (Promega, Madison, WI). Quality of the extracted DNA for every case was assessed by quantitative PCR (Kapa SYBR Green FFPE QC, KAPA Biosystems, Wimington, Massachusetts) and compared to a non-FFPE reference genomic DNA control with an acceptability threshold of delta Ct ≤4. The amount of FFPE DNA input used for each sample was calculated based on delta Ct values representing the quality of each sample. We used the Kappa FFPE QC qPCR assay to determine what volume of DNA to input into the reaction regardless of the concentration. All samples were diluted to a final volume of 25µl as per Illumina’s recommendations. Samples were excluded if the tumour percentage was less than 5
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20% or if the quality/quantity of DNA was insufficient based on the Kappa FFPE QC check delta Ct (anything with a delta Ct>4.5 was rejected).
NGS TST26 sequencing and data analysis Library preparation for NGS TruSight Tumor 26 (TST26; Illumina, San Diego, CA) panel used the MiSeq Reagent Kit v2 chemistry with libraries diluted to 3nM. Sequencing was performed on the MiSeq platform (Illumina, San Diego, CA) with paired end 220 bp sequencing, and sample specific indices to allow pooling with up to 15 libraries pooled per sequencer run. Sequence alignment, base calling and data visualization were performed using NextGENe v.2.3.4.5 (SoftGenetics, State College, PA) and MiSeq reporter v 2.4 (Illumina, San Diego, CA) software. Variant filtration and annotation was conducted using Variant Studio 2.2 (Illumina, San Diego, CA) or Cartagenia Bench Lab NGS v.4.2 (Agilent Technologies, Santa Clara, CA).
Variant Analysis Initial variant filtration was done using Cartagenia Bench Lab NGS v.4.2. (Agilent Technologies). Variants found in the target regions with read depth >=250x, VCF filter of ‘PASS’, and with variant allele frequency (VAF) >=5%. Variants identified as ‘Low DP’ by the software with VAF>=10% and read depth >=500x were also included. Our analysis excluded variants present in the reference population datasets (1000 Genomes Phase1 release v3.20101123, 1000 Genomes Phase 3 release v5.2013050217, 18; ESP6500SI-V2 dataset of the exome sequencing project19 annotated with SeattleSeqAnnotation137; Exome Aggregation Consortium[ExAC] release 0.320 ; genome build GRCh37.p13) at a global or sub6
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population minor allele frequency of greater than 1%. Suspected artifacts identified during validation were further excluded manually. Well-known hotspots or clinically informative variants were reviewed irrespective of variant allele frequency, coverage or quality thresholds. Non-synonymous missense mutations, deletions/insertions, frame-shifts, truncation variants in the coding regions and splice site (+/-2) changes were retained for annotation. Deletion/insertions were also reviewed manually using alternate sources, such as, Alamut v.2.4.5 (Interactive Biosoftware, Rouen, France), and Integrative Genomics Viewer v.2.3 (IGV, Broad Institute) for quality criteria and appropriate nomenclature prior to inclusion for analysis.
Clinical classification of variants All variants passing quality criteria were classified according to the somatic variant classification scheme described by Sukhai et. al.21. Broadly, this scheme classifies somatic variants into 5 major classes (Class 1- 5) based on their prevalence in cancer, pathogenicity and clinical significance. Clinical significance was defined as the prognostic, predictive, diagnostic or therapeutic score of a particular variant or gene in a tumor site. Overall, variants falling in Class 1, 2 and 3 were considered to be “clinicallyinformative”, whereas, Class 4 and 5 were used for variants of unknown significance (see Supplemental Table S1 for details). The variant classof 26 genes on the TST26 panel for melanoma, CRC or GIST is listed in Supplemental Table S2.
Orthogonal testing Variants identified in NGS were verified by orthogonal methods if they met the following criteria: variant allele frequency less than 20%, insertion/deletion variants with changes of more than 4 bp, or clinically informative hotspot variants/ 7
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regionswhich did not meet quality criteria. Orthogonal testing was performed using one of bi-directional Sanger sequencing for KIT (exons 9, 11, 13 and 17) and PDGFRA (exons 12 and 18) genes, ARMS-PCR for BRAF and restriction fragment length polymorphism (RFLP) testing for KRAS codons 12 and 13. If the RFLP was found negative then Sanger sequencing was performed to test KRAS or NRAS hotspot codons (12, 13, 61, 117 and 146).As many complex insertions/deletions in KIT exon 11 are reported in GIST cancers, GIST cases negative by NGS or cases with complex KIT insertions/deletions/duplications changes were further tested by Sanger sequencing to confirm the variants or to determine correct variant nomenclature.
Results Mutational profiling and diagnostic yield of the NGS TST26 panel on melanoma tumors To investigate the benefit of a targeted NGS panel for molecular profiling of melanomas as compared to single-gene non-NGS assays, molecular profiling data of 687 melanoma tumors profiled using this panel was analyzed for variant load and diagnostic yield. Previously in our laboratory melanoma cases were tested for presence of BRAF variants V600E/K using ARMS methods. If found negative, the cases were then tested for NRAS codons 12, 13, 61, 117, 146 and KIT exon 11, 13, 17, 18 variants using Sanger sequencing. The NGS panel detected variants in 20 other genes in addition to BRAF, NRAS or KIT in melanoma (Supplemental Figure S1), and identified 882 variants in 687 cases (Supplemental Figure S1). Of these 882 variants, only 408 (46%) would have been detected by the single-gene non-NGS assays previously used in our laboratory. Clinical significance (predictive, 8
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prognostic, diagnostic and therapeutic relevance) of the variants was assessed, based on the clinical -informativeness in melanoma of the genes on the NGS TST26 panel, as described in Supplemental Table S2. All the 882 variants detected by NGS were classified using our previously-published 5-tier somatic variant classification scheme21 (Class 1-5). Variants falling under Class 1, 2 and 3 were considered to be clinicallyinformative. Of the 882 variants, 566 (64%) variants were clinically informative (Supplemental Figure S2), and 408 of these 566 variants would have been detected by our previous assays. Therefore, the NGS TST26 panel detected an additional 158 (566 variants vs 408 variants) clinically informative variants when compared to non-NGS assays in our laboratory. At the case level, 79% (545/687) of melanoma cases were variant-positive by NGS (Figure 1). 70% (485/687) cases have at least one clinically informative variant by NGS panel when compared to 50% (343/687) cases by non-NGS methods (Figure 1; light grey boxes). Therefore, 20% (142/687) cases have additional clinically significant variantsby NGS when compared to routine testing (dark grey boxes in Figure 1). In addition, NGS TST26 panel testing detected a variety of BRAF variants in melanoma in comparison to our non-NGS BRAF V600E/K assay, including V600M, V600R , insertion/deletion/duplication variants affecting codons 599-601, variants in exon 15 and variants in other BRAF exons (Table 1).
Mutational profiling and diagnostic yield of the NGS TST26 panel on colorectal tumors For colorectal samples, previous non-NGS assays were compared to NGS results for 281 cases. The non-NGS assays for CRC were non-simultaneous assays, and included an RFLP-PCR performed to test for KRAS codons 12 and 13 variants. If 9
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the RFLP was negative, KRAS or NRAS hotspot codons (12, 13, 61, 117 and 146) were tested using Sanger Sequencing. In cases where Lynch syndrome was suspected, BRAF V600E/K was also tested using ARMS. Using NGS TST26 testing, 686 variants were identified in 281 cases (Figure 2A). Of these variants, only 175 variants (26%) would have been detected by the previous non-NGS assays as described. Of the 686 variants, 291 (42%) were clinically informative (Supplemental Figure S3) with a diagnostic yield improvement of 16% or 1.7 folds over non- NGS assays (291 of 686 variants by NGS vs 175 of 686 variants by non-NGS methods). At the case level, TST26 panel enabled variant detection in 94% (263/281) of CRC cases (Figure 2B). Overall, 76% (215/681) cases would have clinically informative variants in CRC cohort by NGS TST26 panel (as indicated by dark grey boxes in Figure 2B) compared to only 40% (114/281) cases if non-NGS methods were employed (as indicated by light grey boxes in Figure 2B). NGS TST26 panel testing enhanced the diagnostic yield by 36% (additional 101 cases with clinically informative variants) at the case level.
Mutational profiling and diagnostic yield of the NGS TST26 panel on GIST tumors For GIST tumors, non-NGS testing consisted of testing of KIT exons 9, 11, 13 and 17 and PDGFRA exons 12 and 18 using Sanger sequencing. Using NGS, 77% (56/73) of GIST cases were variant-positive (Figure 3A and 3B). Due to the known issues with testing of the complex insertions/deletions KIT exon 11 variants using this panel, GIST cases which were either variant-negative by NGS TST26 panel (n = 17) or carried complex KIT insertions/deletions/duplication changes (n=30) were also 10
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re-tested by Sanger sequencing for KIT variants. 6 of the 17 variant-negative GIST cases were found to contain KIT exon 11 variants not detected by NGS (Figure 3B; Supplemental Table S3). In addition, 14 of 56 variant positive cases had uncertainties in nomenclature following NGS that could only be resolved by Sanger sequencing (Figure 3B; Supplemental Table S4). Overall, 27% (20/73) GIST cases initially screened by NGS underwent changes in variant status after Sanger sequencing. Unlike melanoma or colorectal cancer cases, GIST cases were predominantly single-variant for KIT or PDGFRA variants (Figure 3B). KIT variants were mutuallyexclusive of PDGFRA variants (Figure 3A). KIT variants in GIST were mostly insertions, deletions, duplications, frame-shifts or indels as when compared to substitutions in melanoma (see KIT variants in Figure 3A versus Supplemental Figure S1). A total of 69 variants were detected by NGS TST26 panel, 60 which were clinically informative (Figure 3C). Non-NGS assays would have identified 55 of these variants, and 6 additional clinically informative variants missed by NGS. Overall, non-NGS assays would identify similar number of clinically informative variants for GIST cases (61 variants by Sanger vs 60 variants by NGS; Figure 3C).
Discussion
In this study the diagnostic utility of a targeted NGS panel of 26 genes was compared to single-gene or single-variant non-NGS assays for 1041 cases from three tumor sites- melanoma, colorectal and GIST. It is commonly assumed that NGS panels allows for detection of a larger amount of clinically relevant genetic information which offset the cost, time and resources compared to sequential nonNGS tests in the clinical diagnostic laboratory. We demonstrated that the NGS panel 11
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enhanced the overall diagnostic yield for melanoma and colorectal tumors.On the other hand, for GIST tumors, NGS did not enhance variant detection when compared to Sanger sequencing. However, Sanger sequencing enhanced detection of KIT exon 11 indels but missed other variants and had similar overall variant detection to NGS.
Since the TST26 panel was specifically designed to detect variants and genes cited in CAP guidelines, NCCN guidelines and well-established literature/late-stage clinical trials, detection of co-occurring mutations in any combination of these genes may enhance the scope of treatment options available for these patients and predict for response against existing treatment regimen22. In melanoma, NGS testing led to a two-fold increase in variant load (n=882) when compared to our previous non-NGS single-gene or single-variant assays (n=408). This in turn, translated to an increase in variants of potential clinical significance in 20% (142/687) of melanoma cases (Figure 1). Even for genes/variants tested with non-NGS assays (BRAF, NRAS and KIT), NGS detected additional variants not covered by the non-NGS assay. 24% (56/232) of BRAF variants detected in melanoma were non-V600E/K variants (Table 1). Detection of non-BRAF V600E variants was important as patients carrying different BRAF variants are seen to vary in their clinico-pathological features and may represent different subtypes of melanoma23, 24. NGS panel also identified other driver mutations in melanoma, such as, hotspot variants in codons Glu209 and Arg183 in the GNAQ gene in 11 samples. These variants are well-known driver mutations associated with the uveal subtype of melanomas25.
33% (230/687) of melanoma cases were multi-variant. 209 of these had one or more variants in genes of potential therapeutic relevance (Figure 1). These co-occurrence 12
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patterns may provide better insights into disease patho-physiology, guide response to targeted therapy and also dictate response to immunotherapy in melanoma26,22. For instance, pre-clinical studies have shown that PTEN loss in concert with BRAFV600E results in the metastatic melanoma phenotype27. Also, the WNT/βcatenin signaling pathway is considered as an attractive therapeutic target in melanoma28 and its activation may alter the course of patient response after treatment with BRAF inhibitors29. BRAF variants co-occurred quite frequently with these two genetic changes in our melanoma cohort (Supplemental Figure S1). Evidence suggest that, BRAF and NRAS variants are not mutually-exclusive to each other in melanoma, despite occurring in the same biochemical pathway30, 31. Our data also identified cases in which BRAF and NRAS variants co-occur (Supplemental Figure S1), which may impact on how BRAF/NRAS double mutant patients respond to treatment as compared to patients with single mutants in either gene.
In colorectal cancers, most of the cases were multi-variant (76%; 213/281). The gain in variant information compared to previous assays in CRC was 4-fold (686 variants by NGS versus 175 variants by non-NGS assays). This corresponded to a 36% (101/281) increase in cases with additional clinically significant information; Figure 2B). Most of the variants seen in CRC using the NGS panel were in genes of emerging clinical -importance. For instance, the clinical impact of variants identified in the genesTP53, APC, PIK3CA, FBXW7 and SMAD4, which after KRAS, BRAF and NRAS constituted the most variants in our CRC cohort, although not yet used for treatment is rapidly evolving (Figure 2A). Studies show that TP53 variants in combination with KRAS or PIK3CA variants may be used for disease 13
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prognostication32 or may even represent a molecular subtype of CRC with high chromosomal instability (CIN)33. Association of PIK3CA and KRAS variants and their impact on anti-EGFR therapy in CRC is a subject of much interest34-37. A study by Schell et. al.38 showed that not only the presence of APC variants but also the number of APC variants in conjunction with other genetic variants may exert a differential impact on patient prognosis in CRC. Similar evidence of potential clinical significance are available throughout literature for FBXW739 and SMAD4 genes40. The point to be noted is that, the knowledge of co-existence of these variants is only available through NGS based multi-gene testing and would entirely be missed by single-gene/single-variants assays. This emphasizes the utility of targeted NGS multi-gene panels for routine testing in highly mutant tumors types such as CRC and melanoma to better understand the landscape of biomarkers and how they may be probed to guide treatment decisions. Unlike melanoma and CRC, NGS TST26 panel did not add to the diagnostic utility when compared to standard single-gene testing in GIST tumors (Figure 3C). Most of the GIST tumors had single variants in either KIT or PDGFRA, identified by our previous Sanger sequencing protocols. In addition, Sanger identified additional KIT variants which were missed by our NGS assay (Figure 3C, Supplemental Table S3) due to large deletions or substitutions difficult to detect by short read NGS.
Our study demonstrated that a significant consideration in the implementation of NGS assays to replace non-NGS assays is the nature of the variants to be identified. For example, BRAF variants detected in our melanoma cohort varied both in coding effect and genomic distribution (Supplemental Figure S1 and Table 1), and the NGS TST26 panel was able to detect all the complex BRAF variants with great accuracy 14
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and precision, therefore, enhancing the scope of BRAF testing in melanoma by using NGS. The NGS panel, however, missed KIT variants in GIST, or labeled variants with inaccurate nomenclature (Supplemental Tables S3 and S4), partly due to the fact that most of the KIT large deletions/substitutions were found in codons 550–560 of KIT exon 1141, which fell under the amplicon primer region of exon 11 in the TST26 panel. In addition, certain large insertions/duplications were either missed or inaccurately called because the amplicons did not fully cover the region or variant. This comparative study across melanoma and GI tumor sites demonstrated that a major consideration of implementation of NGS panels to replace non-NGS assays for clinical diagnostics is the diagnostic yield depending upon the tumor site. In our case, NGS testing using TST26 panel proved to be an asset in routine diagnostics for tumor testing of melanoma and colorectal tumors as NGS allowed for simultaneous detection of variants in multiple genes of biological interest. On the other hand, NGS using TST26 panel would not serve as a standalone test for screening GIST tumors due to the complex KIT variants found in this tumor site. There is a need to utilize specialized bioinformatics algorithms such as, Pindel42, 43 to enhance indel detection by NGS. Some laboratories have developed their in-house pipelines to deal with these challenges44, however, additional time and cost is involved in such an implementation. Our experience suggests that, for any assay, the diagnostic yield should be an important consideration, including the ability to detect clinically relevant changes. However, for assays with overall similar diagnostic yields, other considerations are also relevant- such as the cost (including reagent and labor cost) and effort of maintaining multiple assays; turn-around times; test workflow and batching; capital investment or maintenance required; and the number of samples that would be deemed insufficient based on the method. Overall, multiple 15
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factors need to be considered before the deployment of single NGS panel-based diagnostic testing either as a standalone test or with supplemental non-NGS assays.
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Zhang L, Chen L, Sah S, Latham GJ, Patel R, Song Q, Koeppen H, Tam R, Schleifman E, Mashhedi H: Profiling cancer gene mutations in clinical formalin-fixed, paraffin-embedded colorectal tumor specimens using targeted next-generation sequencing. The oncologist 2014, 19:336-343. Chevrier S, Arnould L, Ghiringhelli F, Coudert B, Fumoleau P, Boidot R: Next-generation sequencing analysis of lung and colon carcinomas reveals a variety of genetic alterations. International journal of oncology 2014, 45:1167-1174. Malapelle U, Vigliar E, Sgariglia R, Bellevicine C, Colarossi L, Vitale D, Pallante P, Troncone G: Ion Torrent next-generation sequencing for routine identification of clinically relevant mutations in colorectal cancer patients. Journal of clinical pathology 2015, 68:64-68. Consortium GP: A global reference for human genetic variation. Nature 2015, 526:68-74. Siva N: 1000 Genomes project. Nature Publishing Group, 2008. Li H, Durbin R: Fast and accurate short read alignment with Burrows–Wheeler transform. bioinformatics 2009, 25:1754-1760. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O’Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB: Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016, 536:285-291. Sukhai MA, Craddock KJ, Thomas M, Hansen AR, Zhang T, Siu L, Bedard P, Stockley TL, KamelReid S: A classification system for clinical relevance of somatic variants identified in molecular profiling of cancer. Genetics in Medicine 2015, 18:128-136. Shtivelman E, Davies MA, Hwu P, Yang J, Lotem M, Oren M, Flaherty KT, Fisher DE: Pathways and therapeutic targets in melanoma. Oncotarget 2014, 5:1701-1752. Menzies AM, Haydu LE, Visintin L, Carlino MS, Howle JR, Thompson JF, Kefford RF, Scolyer RA, Long GV: Distinguishing clinicopathologic features of patients with V600E and V600K BRAF-mutant metastatic melanoma. Clinical cancer research 2012, 18:3242-3249. Voskoboynik M, Mar V, Mailer S, Colebatch A, Fennessy A, Logan A, Hewitt C, Cebon J, Kelly J, McArthur G: Clinicopathological characteristics associated with BRAFK601E and BRAFL597 mutations in melanoma. Pigment cell & melanoma research 2016, 29:222-228. Helgadottir H, Höiom V: The genetics of uveal melanoma: current insights. The Application of Clinical Genetics 2016, 9:147. Rajkumar S, Watson IR: Molecular characterisation of cutaneous melanoma: creating a framework for targeted and immune therapies. British Journal of Cancer 2016. Dankort D, Curley DP, Cartlidge RA, Nelson B, Karnezis AN, Damsky Jr WE, You MJ, DePinho RA, McMahon M, Bosenberg M: BrafV600E cooperates with Pten loss to induce metastatic melanoma. Nature genetics 2009, 41:544-552. Atkinson JM, Rank KB, Zeng Y, Capen A, Yadav V, Manro JR, Engler TA, Chedid M: Activating the Wnt/β-Catenin Pathway for the Treatment of Melanoma–Application of LY2090314, a Novel Selective Inhibitor of Glycogen Synthase Kinase-3. PloS one 2015, 10:e0125028. Chien AJ, Haydu LE, Biechele TL, Kulikauskas RM, Rizos H, Kefford RF, Scolyer RA, Moon RT, Long GV: Targeted BRAF inhibition impacts survival in melanoma patients with high levels of Wnt/β-catenin signaling. PloS one 2014, 9:e94748. Raaijmakers MI, Widmer DS, Narechania A, Eichhoff O, Freiberger SN, Wenzina J, Cheng PF, Mihic-Probst D, Desalle R, Dummer R: Co-existence of BRAF and NRAS driver mutations in the same melanoma cells results in heterogeneity of targeted therapy resistance. Oncotarget 2016, 7:77163-77174. Siroy AE, Boland GM, Milton DR, Roszik J, Frankian S, Malke J, Haydu L, Prieto VG, Tetzlaff M, Ivan D: Beyond BRAFV600: clinical mutation panel testing by next-generation sequencing in advanced melanoma. Journal of Investigative Dermatology 2015, 135:508-515. Dallol A, Buhmeida A, Al-Ahwal MS, Al-Maghrabi J, Bajouh O, Al-Khayyat S, Alam R, Abusanad A, Turki R, Elaimi A: Clinical significance of frequent somatic mutations detected 18
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by high-throughput targeted sequencing in archived colorectal cancer samples. Journal of translational medicine 2016, 14:118. Rodriguez-Salas N, Dominguez G, Barderas R, Mendiola M, García-Albéniz X, Maurel J, Batlle JF: Clinical relevance of colorectal cancer molecular subtypes. Critical Reviews in Oncology/Hematology 2017, 109:9-19. Van Cutsem E, Cervantes A, Adam R, Sobrero A, Van Krieken J, Aderka D, Aguilar EA, Bardelli A, Benson A, Bodoky G: ESMO consensus guidelines for the management of patients with metastatic colorectal cancer. Annals of Oncology 2016:mdw235. Patil H, Saxena SG, Barrow CJ, Kanwar JR, Kapat A, Kanwar RK: Chasing the personalized medicine dream through biomarker validation in colorectal cancer. Drug discovery today 2017, 22:111-119. Davis SL, Robertson KM, Pitts TM, Tentler JJ, Bradshaw-Pierce EL, Klauck PJ, Bagby SM, Hyatt SL, Selby HM, Spreafico A: Combined inhibition of MEK and Aurora A kinase in KRAS/PIK3CA double-mutant colorectal cancer models. Frontiers in pharmacology 2015, 6. Grasselli J, Elez E, Martinez GA, Sanz-Garcia E, Macarulla T, Capdevila J, Alsina M, Sauri T, Hierro C, Verdaguer H: Molecular, clinical and prognostic characterization of double KRAS/PIK3CA (dKP) mutated metastatic colorectal cancer (mCRC). Annals of Oncology 2016, 27:560P. Schell MJ, Yang M, Teer JK, Lo FY, Madan A, Coppola D, Monteiro AN, Nebozhyn MV, Yue B, Loboda A: A multigene mutation classification of 468 colorectal cancers reveals a prognostic role for APC. Nature Communications 2016, 7. Akhoondi S, Sun D, von der Lehr N, Apostolidou S, Klotz K, Maljukova A, Cepeda D, Fiegl H, Dofou D, Marth C: FBXW7/hCDC4 is a general tumor suppressor in human cancer. Cancer research 2007, 67:9006-9012. Lupini L, Bassi C, Mlcochova J, Musa G, Russo M, Vychytilova-Faltejskova P, Svoboda M, Sabbioni S, Nemecek R, Slaby O: Prediction of response to anti-EGFR antibody-based therapies by multigene sequencing in colorectal cancer patients. BMC cancer 2015, 15:808. Szucs Z, Thway K, Fisher C, Bulusu R, Constantinidou A, Benson C, van der Graaf WT, Jones RL: Molecular subtypes of gastrointestinal stromal tumors and their prognostic and therapeutic implications. Future Oncology 2017, 13:93-107. Ye K, Schulz MH, Long Q, Apweiler R, Ning Z: Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 2009, 25:2865-2871. Spencer DH, Abel HJ, Lockwood CM, Payton JE, Szankasi P, Kelley TW, Kulkarni S, Pfeifer JD, Duncavage EJ: Detection of FLT3 internal tandem duplication in targeted, short-read-length, next-generation sequencing data. The Journal of molecular diagnostics 2013, 15:81-93. Schneider T, Smith GH, Rossi MR, Hill CE, Zhang L: Validation of a Customized Bioinformatics Pipeline for a Clinical Next-Generation Sequencing Test Targeting Solid Tumor–Associated Variants. The Journal of Molecular Diagnostics 2018.
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Figure Legends
Figure 1: Distribution of melanoma cases (n = 687) by clinically informative variants. Cases were counted based on presence of one or more clinically informative variants (Class 1-3). Cases with Class 4 and/or 5 variants were considered cases with variants of unknown significance; VOUS. Light grey boxes represent number of cases where clinically informative variant yield (Class 1-3 variants) was the same between the NGS TST26 panel and non-NGS assays (cases with one BRAF, NRAS or KIT variants; or multi-variant on NGS but; Class 4 and 5 variants). Dark grey boxes represent cases that had additional clinically informative variants detected by NGS.
Figure 2A: Mutational Landscape of Colorectal cancer cohort (n=281). Genes on the TST26 panel are listed on the vertical axis. The number of variants in a each gene is indicated by colors: blue = 1 variant/gene; orange = 2 variants/gene and red = 3 variants/gene. Bars on the right indicate the overall number of variants observed per gene. Different type of variants (missense, nonsense, duplication, frameshift, ins/dels and splice site) are indicated by colors. B: Distribution of CRC cases (n = 281) based on presence of clinically informative variants. Cases were counted based on presence of one or more clinically informative variants (Class 1-3). Cases with Class 4-5 variants were considered cases with variants of unknown significance; VOUS. Light grey boxes indicate cases where the clinically variant load was same between TST26 panel and previous non-NGS laboratory assays. These were either cases that were single-variant for BRAF, NRAS or KIT variants/exonic regions tested by our laboratory or multi-variant for genomic regions previously tested for along with 20
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variants of unknown significance (VOUS). Dark grey boxes were cases that had additional clinically informative variants detected by NGS when compared to nonNGS assays.
Figure 3A: Mutational Landscape of GIST cohort (n=73). Some cases had more than one variant in a particular gene as indicated by different colors: blue = 1 variant/gene;orange = 2 variants/gene and red = 3 variants/gene. The bars on the right plotted the overall number of variants observed per gene by NGS and Sanger combined. Different type of variants (missense, nonsense, duplication, frameshift, ins/dels and splice site) were indicated by different colors.The pink cells in the first row indicated the cases that underwent nomenclature changes following Sanger sequencing; the purple cells indicate the number of the cases that were variant negative by NGS but variant positive by Sanger. B: Distribution of GIST cases (n = 73) based on the variant status post NGS testing. Grey boxes indicate cases in which variant status changed post Sanger sequencing. These were either cases that test were previously variant-negative by NGS but variant positive for KIT exon 11 variants by Sanger or cases were the nomenclature of KIT/PDGFRA variants changed following Sanger sequencing. Majority of single or multi-variant variant positive case on NGS detected either KIT or PDGFRA variant. C: Comparison of the distribution of overall variants and clinically informative variants in GIST cases detected by NGS or Sanger shown.
21
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Table 1: Distribution of BRAF variants in melanoma: A total of 232 BRAF variants were detected in 230 BRAF positive melanoma cases. A total of 175 cases had BRAF V600E or V600K variants identifiable by non-NGS assays (columns 1 and 2). 53 cases had a BRAF variant identified only by NGS (columns 3-8), and 2 cases had two BRAF variants (columns 9 and 10) Cases with Cases with one BRAF variant (N = 228)
multiple BRAF variants (N = 2) BRAF
BRAF
# of
V600
case
variants
s
BRAF V600like variants
BRAF
# of
BRAF
# of
other
cas
other
cas
variants
es
variants
es
# of cases
variant
# of
combinati
cases
ons
V600
V600E; 136
T599dup
2
N581I
3
S467L
5
E
G606E
V600
V600_Lys60 39
K
1
L597S; 2
L584F
1
G469E
4
1delinsEN
1 L584F
V600_Lys60 1
E586K
1
G466E
2
1
D594N
3
G464A
1
V600R
9
D594E
1
G466R
1
V600M
1
D594G
1
G466V
1
K601E
4
G596C
3
G469A
1
G596V
1
G469R
1
L597R
1
G469S
1
1delinsEI V600_K601 delinsE
22
NGS-based diagnostic yield comparison
S616F
1
Total 175
20
16
17
2
cases
23
A
B
S1525-1578(20)30014-3
DOI:
https://doi.org/10.1016/j.jmoldx.2019.12.008
Reference:
JMDI 882
To appear in:
The Journal of Molecular Diagnostics
Received Date: 18 June 2019 Revised Date:
19 November 2019
Accepted Date: 20 December 2019
Please cite this article as: Garg S, Gernier S, Misyura M, Sukhai MA, Thomas M, Reid SK-, Stockley T, Assessing the diagnostic yield of targeted next-generation sequencing for melanoma and gastrointestinal tumors, The Journal of Molecular Diagnostics (2020), doi: https://doi.org/10.1016/ j.jmoldx.2019.12.008. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Copyright © 2020 Published by Elsevier Inc. on behalf of the American Society for Investigative Pathology and the Association for Molecular Pathology.
NGS-based diagnostic yield comparison
Assessing the diagnostic yield of targeted next-generation sequencing for melanoma and gastrointestinal tumors Swati Garg,* Sylvie Gernier,†‡ Maksym Misyura,* Mahadeo A Sukhai,* Mariam Thomas,* Suzanne Kamel- Reid,*† ‡§ and Tracy Stockley*†‡
From the Advanced Molecular Diagnostics Laboratory,* Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario; Genome Diagnostics,† Department of Clinical Laboratory Genetics, Laboratory Medicine Program, University Health Network, Toronto, Ontario; and the Departments of Laboratory Medicine and Pathobiology,‡ and Medical Biophysics,§ University of Toronto, Toronto, Ontario, Canada
RUNNING HEAD: NGS based diagnostic yield comparison FUNDING: Princess Margaret Cancer Foundation CONFLICT OF INTEREST: NONE
CORRESPONDING AUTHOR Tracy Stockley Department of Clinical Laboratory Genetics University Health Network Eaton Wing, 11EB-418 200 Elizabeth Street Toronto General Hospital Toronto, ON, M5G 2C4 Email: [email protected] 1
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Current address of S.Ga., Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada, of M.M., Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, of M.A.S., Canadian National Institute for the Blind, 1929 Bayview Ave, Toronto, Ontario, Canada, and of M.T., Institute for Genomic Medicine, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH.
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Abstract A common rationale in molecular diagnostic laboratories is that implementation of next-generation sequencing (NGS) enables simultaneous multi-gene testing, allowing increased information benefit as compared to non-NGS assays. However, minimal published data exist to support this justification. In this study, we compared clinical diagnostic yield of TruSight Tumor 26 Sequencing Panel (TST26) in melanoma, colorectal (CRC) and gastro-intestinal stromal (GIST) tumors to nonNGS assays. 1041 formalin-fixed, paraffin embedded (FFPE) tumors, of melanoma, CRC and GIST were profiled. NGS results were compared to non-NGS single-gene or single-variant assays with respect to variant output and diagnostic yield. 79% melanoma and 94% CRC tumors were variant-positive by panel testing. TST26 panel improved BRAF variant detection in melanoma as compared to single-variant BRAF V600E/K routine tests by 24% and also detected variants in genes other than BRAF, NRAS and KIT which could impact patient management in 20% additional cases. NGS enhanced diagnostic yield in CRC by 36% when compared to routine single gene assays. In contrast, no added benefit of NGS based testing for GIST tumors was observed.TST26 panel either missed or inaccurately called complex insertion/deletion variants in KIT exon 11, which were accurately identified by nonNGS methods. Our findings demonstrate the differential impact of cancer site and variant type on diagnostic test information yield from NGS assays.
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Introduction As the number of tumor-associated biomarkers of diagnosis, prognosis and/or treatment response continues to increase, clinical diagnostic laboratories are implementing multi-gene NGS panels for routine molecular profiling of solid tumors18
. As NGS tests can simultaneously interrogate many genes or regions of interest,
the common justification for implementation of NGS in clinical molecular diagnostics laboratories are increased variant information with time and cost-savings over sequential single-gene or single-variant non-NGS tests9. In addition, multi-gene NGS tests can potentially use less input material from precious formalin fixed paraffin embedded (FFPE) tumor samples10-16. In this study, we assessed the diagnostic utility of implementation of a 26gene amplicon-based NGS panel (TruSight Tumor 26 Sequencing, TST26; Illumina) as part of routine molecular testing. Our study cohort comprised of 1041 tumor samples from melanoma and gastrointestinal tumors (colorectal and gastrointestinal stromal tumors) in our clinical molecular diagnostic laboratory at the University Health Network, a major academic medical Centre in Toronto. The TST26 panel contains 174 amplicons covering clinically relevant variants/regions in 26 genes of particular relevance in melanoma, colon, and lung, gastric and ovarian tumors. We investigated additional benefit on patient management of the NGS panel as compared to single-variant or single-gene non-NGS assays for melanoma, colorectal and GIST tumors. Results of the comparison refute the a priori assumption that NGS panel testing is superior over non-NGS assays for all tumor sites, and establish that the choice of test requires careful evaluation of the molecular landscape for the tumor types under investigation.
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Materials and methods Study cohort The study cohort consisted of DNA extracted from 1041 formalin-fixed paraffin embedded (FFPE) melanoma (n=687), colorectal (CRC, n=281) and gastrointestinal stromal (GIST, n=73) tumors as a part of routine molecular testing in our clinically accredited molecular diagnostic laboratory between January 2015 and October 2016. This study was reviewed and approved by the University Health Network Research Ethics Board (# 17-5380).
DNA Isolation from FFPE tumors Haematoxylin and eosin stained slides had previously undergone pathology review for identification of tumor and cellularity estimate. Tumors either macro-dissected from 10-12 unstained slides or isolated by punch biopsy (two 1 mm cores) from the FFPE block. After de-paraffinization, DNA was extracted using the QIAmicro DNA extraction kit (Qiagen, Germantown, MD) or the Maxwell FFPE extraction Kit (Promega, Madison, WI), according to standard protocols, and quantified by Quantus fluorometer (Promega, Madison, WI). Quality of the extracted DNA for every case was assessed by quantitative PCR (Kapa SYBR Green FFPE QC, KAPA Biosystems, Wimington, Massachusetts) and compared to a non-FFPE reference genomic DNA control with an acceptability threshold of delta Ct ≤4. The amount of FFPE DNA input used for each sample was calculated based on delta Ct values representing the quality of each sample. We used the Kappa FFPE QC qPCR assay to determine what volume of DNA to input into the reaction regardless of the concentration. All samples were diluted to a final volume of 25µl as per Illumina’s recommendations. Samples were excluded if the tumour percentage was less than 5
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20% or if the quality/quantity of DNA was insufficient based on the Kappa FFPE QC check delta Ct (anything with a delta Ct>4.5 was rejected).
NGS TST26 sequencing and data analysis Library preparation for NGS TruSight Tumor 26 (TST26; Illumina, San Diego, CA) panel used the MiSeq Reagent Kit v2 chemistry with libraries diluted to 3nM. Sequencing was performed on the MiSeq platform (Illumina, San Diego, CA) with paired end 220 bp sequencing, and sample specific indices to allow pooling with up to 15 libraries pooled per sequencer run. Sequence alignment, base calling and data visualization were performed using NextGENe v.2.3.4.5 (SoftGenetics, State College, PA) and MiSeq reporter v 2.4 (Illumina, San Diego, CA) software. Variant filtration and annotation was conducted using Variant Studio 2.2 (Illumina, San Diego, CA) or Cartagenia Bench Lab NGS v.4.2 (Agilent Technologies, Santa Clara, CA).
Variant Analysis Initial variant filtration was done using Cartagenia Bench Lab NGS v.4.2. (Agilent Technologies). Variants found in the target regions with read depth >=250x, VCF filter of ‘PASS’, and with variant allele frequency (VAF) >=5%. Variants identified as ‘Low DP’ by the software with VAF>=10% and read depth >=500x were also included. Our analysis excluded variants present in the reference population datasets (1000 Genomes Phase1 release v3.20101123, 1000 Genomes Phase 3 release v5.2013050217, 18; ESP6500SI-V2 dataset of the exome sequencing project19 annotated with SeattleSeqAnnotation137; Exome Aggregation Consortium[ExAC] release 0.320 ; genome build GRCh37.p13) at a global or sub6
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population minor allele frequency of greater than 1%. Suspected artifacts identified during validation were further excluded manually. Well-known hotspots or clinically informative variants were reviewed irrespective of variant allele frequency, coverage or quality thresholds. Non-synonymous missense mutations, deletions/insertions, frame-shifts, truncation variants in the coding regions and splice site (+/-2) changes were retained for annotation. Deletion/insertions were also reviewed manually using alternate sources, such as, Alamut v.2.4.5 (Interactive Biosoftware, Rouen, France), and Integrative Genomics Viewer v.2.3 (IGV, Broad Institute) for quality criteria and appropriate nomenclature prior to inclusion for analysis.
Clinical classification of variants All variants passing quality criteria were classified according to the somatic variant classification scheme described by Sukhai et. al.21. Broadly, this scheme classifies somatic variants into 5 major classes (Class 1- 5) based on their prevalence in cancer, pathogenicity and clinical significance. Clinical significance was defined as the prognostic, predictive, diagnostic or therapeutic score of a particular variant or gene in a tumor site. Overall, variants falling in Class 1, 2 and 3 were considered to be “clinicallyinformative”, whereas, Class 4 and 5 were used for variants of unknown significance (see Supplemental Table S1 for details). The variant classof 26 genes on the TST26 panel for melanoma, CRC or GIST is listed in Supplemental Table S2.
Orthogonal testing Variants identified in NGS were verified by orthogonal methods if they met the following criteria: variant allele frequency less than 20%, insertion/deletion variants with changes of more than 4 bp, or clinically informative hotspot variants/ 7
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regionswhich did not meet quality criteria. Orthogonal testing was performed using one of bi-directional Sanger sequencing for KIT (exons 9, 11, 13 and 17) and PDGFRA (exons 12 and 18) genes, ARMS-PCR for BRAF and restriction fragment length polymorphism (RFLP) testing for KRAS codons 12 and 13. If the RFLP was found negative then Sanger sequencing was performed to test KRAS or NRAS hotspot codons (12, 13, 61, 117 and 146).As many complex insertions/deletions in KIT exon 11 are reported in GIST cancers, GIST cases negative by NGS or cases with complex KIT insertions/deletions/duplications changes were further tested by Sanger sequencing to confirm the variants or to determine correct variant nomenclature.
Results Mutational profiling and diagnostic yield of the NGS TST26 panel on melanoma tumors To investigate the benefit of a targeted NGS panel for molecular profiling of melanomas as compared to single-gene non-NGS assays, molecular profiling data of 687 melanoma tumors profiled using this panel was analyzed for variant load and diagnostic yield. Previously in our laboratory melanoma cases were tested for presence of BRAF variants V600E/K using ARMS methods. If found negative, the cases were then tested for NRAS codons 12, 13, 61, 117, 146 and KIT exon 11, 13, 17, 18 variants using Sanger sequencing. The NGS panel detected variants in 20 other genes in addition to BRAF, NRAS or KIT in melanoma (Supplemental Figure S1), and identified 882 variants in 687 cases (Supplemental Figure S1). Of these 882 variants, only 408 (46%) would have been detected by the single-gene non-NGS assays previously used in our laboratory. Clinical significance (predictive, 8
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prognostic, diagnostic and therapeutic relevance) of the variants was assessed, based on the clinical -informativeness in melanoma of the genes on the NGS TST26 panel, as described in Supplemental Table S2. All the 882 variants detected by NGS were classified using our previously-published 5-tier somatic variant classification scheme21 (Class 1-5). Variants falling under Class 1, 2 and 3 were considered to be clinicallyinformative. Of the 882 variants, 566 (64%) variants were clinically informative (Supplemental Figure S2), and 408 of these 566 variants would have been detected by our previous assays. Therefore, the NGS TST26 panel detected an additional 158 (566 variants vs 408 variants) clinically informative variants when compared to non-NGS assays in our laboratory. At the case level, 79% (545/687) of melanoma cases were variant-positive by NGS (Figure 1). 70% (485/687) cases have at least one clinically informative variant by NGS panel when compared to 50% (343/687) cases by non-NGS methods (Figure 1; light grey boxes). Therefore, 20% (142/687) cases have additional clinically significant variantsby NGS when compared to routine testing (dark grey boxes in Figure 1). In addition, NGS TST26 panel testing detected a variety of BRAF variants in melanoma in comparison to our non-NGS BRAF V600E/K assay, including V600M, V600R , insertion/deletion/duplication variants affecting codons 599-601, variants in exon 15 and variants in other BRAF exons (Table 1).
Mutational profiling and diagnostic yield of the NGS TST26 panel on colorectal tumors For colorectal samples, previous non-NGS assays were compared to NGS results for 281 cases. The non-NGS assays for CRC were non-simultaneous assays, and included an RFLP-PCR performed to test for KRAS codons 12 and 13 variants. If 9
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the RFLP was negative, KRAS or NRAS hotspot codons (12, 13, 61, 117 and 146) were tested using Sanger Sequencing. In cases where Lynch syndrome was suspected, BRAF V600E/K was also tested using ARMS. Using NGS TST26 testing, 686 variants were identified in 281 cases (Figure 2A). Of these variants, only 175 variants (26%) would have been detected by the previous non-NGS assays as described. Of the 686 variants, 291 (42%) were clinically informative (Supplemental Figure S3) with a diagnostic yield improvement of 16% or 1.7 folds over non- NGS assays (291 of 686 variants by NGS vs 175 of 686 variants by non-NGS methods). At the case level, TST26 panel enabled variant detection in 94% (263/281) of CRC cases (Figure 2B). Overall, 76% (215/681) cases would have clinically informative variants in CRC cohort by NGS TST26 panel (as indicated by dark grey boxes in Figure 2B) compared to only 40% (114/281) cases if non-NGS methods were employed (as indicated by light grey boxes in Figure 2B). NGS TST26 panel testing enhanced the diagnostic yield by 36% (additional 101 cases with clinically informative variants) at the case level.
Mutational profiling and diagnostic yield of the NGS TST26 panel on GIST tumors For GIST tumors, non-NGS testing consisted of testing of KIT exons 9, 11, 13 and 17 and PDGFRA exons 12 and 18 using Sanger sequencing. Using NGS, 77% (56/73) of GIST cases were variant-positive (Figure 3A and 3B). Due to the known issues with testing of the complex insertions/deletions KIT exon 11 variants using this panel, GIST cases which were either variant-negative by NGS TST26 panel (n = 17) or carried complex KIT insertions/deletions/duplication changes (n=30) were also 10
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re-tested by Sanger sequencing for KIT variants. 6 of the 17 variant-negative GIST cases were found to contain KIT exon 11 variants not detected by NGS (Figure 3B; Supplemental Table S3). In addition, 14 of 56 variant positive cases had uncertainties in nomenclature following NGS that could only be resolved by Sanger sequencing (Figure 3B; Supplemental Table S4). Overall, 27% (20/73) GIST cases initially screened by NGS underwent changes in variant status after Sanger sequencing. Unlike melanoma or colorectal cancer cases, GIST cases were predominantly single-variant for KIT or PDGFRA variants (Figure 3B). KIT variants were mutuallyexclusive of PDGFRA variants (Figure 3A). KIT variants in GIST were mostly insertions, deletions, duplications, frame-shifts or indels as when compared to substitutions in melanoma (see KIT variants in Figure 3A versus Supplemental Figure S1). A total of 69 variants were detected by NGS TST26 panel, 60 which were clinically informative (Figure 3C). Non-NGS assays would have identified 55 of these variants, and 6 additional clinically informative variants missed by NGS. Overall, non-NGS assays would identify similar number of clinically informative variants for GIST cases (61 variants by Sanger vs 60 variants by NGS; Figure 3C).
Discussion
In this study the diagnostic utility of a targeted NGS panel of 26 genes was compared to single-gene or single-variant non-NGS assays for 1041 cases from three tumor sites- melanoma, colorectal and GIST. It is commonly assumed that NGS panels allows for detection of a larger amount of clinically relevant genetic information which offset the cost, time and resources compared to sequential nonNGS tests in the clinical diagnostic laboratory. We demonstrated that the NGS panel 11
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enhanced the overall diagnostic yield for melanoma and colorectal tumors.On the other hand, for GIST tumors, NGS did not enhance variant detection when compared to Sanger sequencing. However, Sanger sequencing enhanced detection of KIT exon 11 indels but missed other variants and had similar overall variant detection to NGS.
Since the TST26 panel was specifically designed to detect variants and genes cited in CAP guidelines, NCCN guidelines and well-established literature/late-stage clinical trials, detection of co-occurring mutations in any combination of these genes may enhance the scope of treatment options available for these patients and predict for response against existing treatment regimen22. In melanoma, NGS testing led to a two-fold increase in variant load (n=882) when compared to our previous non-NGS single-gene or single-variant assays (n=408). This in turn, translated to an increase in variants of potential clinical significance in 20% (142/687) of melanoma cases (Figure 1). Even for genes/variants tested with non-NGS assays (BRAF, NRAS and KIT), NGS detected additional variants not covered by the non-NGS assay. 24% (56/232) of BRAF variants detected in melanoma were non-V600E/K variants (Table 1). Detection of non-BRAF V600E variants was important as patients carrying different BRAF variants are seen to vary in their clinico-pathological features and may represent different subtypes of melanoma23, 24. NGS panel also identified other driver mutations in melanoma, such as, hotspot variants in codons Glu209 and Arg183 in the GNAQ gene in 11 samples. These variants are well-known driver mutations associated with the uveal subtype of melanomas25.
33% (230/687) of melanoma cases were multi-variant. 209 of these had one or more variants in genes of potential therapeutic relevance (Figure 1). These co-occurrence 12
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patterns may provide better insights into disease patho-physiology, guide response to targeted therapy and also dictate response to immunotherapy in melanoma26,22. For instance, pre-clinical studies have shown that PTEN loss in concert with BRAFV600E results in the metastatic melanoma phenotype27. Also, the WNT/βcatenin signaling pathway is considered as an attractive therapeutic target in melanoma28 and its activation may alter the course of patient response after treatment with BRAF inhibitors29. BRAF variants co-occurred quite frequently with these two genetic changes in our melanoma cohort (Supplemental Figure S1). Evidence suggest that, BRAF and NRAS variants are not mutually-exclusive to each other in melanoma, despite occurring in the same biochemical pathway30, 31. Our data also identified cases in which BRAF and NRAS variants co-occur (Supplemental Figure S1), which may impact on how BRAF/NRAS double mutant patients respond to treatment as compared to patients with single mutants in either gene.
In colorectal cancers, most of the cases were multi-variant (76%; 213/281). The gain in variant information compared to previous assays in CRC was 4-fold (686 variants by NGS versus 175 variants by non-NGS assays). This corresponded to a 36% (101/281) increase in cases with additional clinically significant information; Figure 2B). Most of the variants seen in CRC using the NGS panel were in genes of emerging clinical -importance. For instance, the clinical impact of variants identified in the genesTP53, APC, PIK3CA, FBXW7 and SMAD4, which after KRAS, BRAF and NRAS constituted the most variants in our CRC cohort, although not yet used for treatment is rapidly evolving (Figure 2A). Studies show that TP53 variants in combination with KRAS or PIK3CA variants may be used for disease 13
NGS-based diagnostic yield comparison
prognostication32 or may even represent a molecular subtype of CRC with high chromosomal instability (CIN)33. Association of PIK3CA and KRAS variants and their impact on anti-EGFR therapy in CRC is a subject of much interest34-37. A study by Schell et. al.38 showed that not only the presence of APC variants but also the number of APC variants in conjunction with other genetic variants may exert a differential impact on patient prognosis in CRC. Similar evidence of potential clinical significance are available throughout literature for FBXW739 and SMAD4 genes40. The point to be noted is that, the knowledge of co-existence of these variants is only available through NGS based multi-gene testing and would entirely be missed by single-gene/single-variants assays. This emphasizes the utility of targeted NGS multi-gene panels for routine testing in highly mutant tumors types such as CRC and melanoma to better understand the landscape of biomarkers and how they may be probed to guide treatment decisions. Unlike melanoma and CRC, NGS TST26 panel did not add to the diagnostic utility when compared to standard single-gene testing in GIST tumors (Figure 3C). Most of the GIST tumors had single variants in either KIT or PDGFRA, identified by our previous Sanger sequencing protocols. In addition, Sanger identified additional KIT variants which were missed by our NGS assay (Figure 3C, Supplemental Table S3) due to large deletions or substitutions difficult to detect by short read NGS.
Our study demonstrated that a significant consideration in the implementation of NGS assays to replace non-NGS assays is the nature of the variants to be identified. For example, BRAF variants detected in our melanoma cohort varied both in coding effect and genomic distribution (Supplemental Figure S1 and Table 1), and the NGS TST26 panel was able to detect all the complex BRAF variants with great accuracy 14
NGS-based diagnostic yield comparison
and precision, therefore, enhancing the scope of BRAF testing in melanoma by using NGS. The NGS panel, however, missed KIT variants in GIST, or labeled variants with inaccurate nomenclature (Supplemental Tables S3 and S4), partly due to the fact that most of the KIT large deletions/substitutions were found in codons 550–560 of KIT exon 1141, which fell under the amplicon primer region of exon 11 in the TST26 panel. In addition, certain large insertions/duplications were either missed or inaccurately called because the amplicons did not fully cover the region or variant. This comparative study across melanoma and GI tumor sites demonstrated that a major consideration of implementation of NGS panels to replace non-NGS assays for clinical diagnostics is the diagnostic yield depending upon the tumor site. In our case, NGS testing using TST26 panel proved to be an asset in routine diagnostics for tumor testing of melanoma and colorectal tumors as NGS allowed for simultaneous detection of variants in multiple genes of biological interest. On the other hand, NGS using TST26 panel would not serve as a standalone test for screening GIST tumors due to the complex KIT variants found in this tumor site. There is a need to utilize specialized bioinformatics algorithms such as, Pindel42, 43 to enhance indel detection by NGS. Some laboratories have developed their in-house pipelines to deal with these challenges44, however, additional time and cost is involved in such an implementation. Our experience suggests that, for any assay, the diagnostic yield should be an important consideration, including the ability to detect clinically relevant changes. However, for assays with overall similar diagnostic yields, other considerations are also relevant- such as the cost (including reagent and labor cost) and effort of maintaining multiple assays; turn-around times; test workflow and batching; capital investment or maintenance required; and the number of samples that would be deemed insufficient based on the method. Overall, multiple 15
NGS-based diagnostic yield comparison
factors need to be considered before the deployment of single NGS panel-based diagnostic testing either as a standalone test or with supplemental non-NGS assays.
16
NGS-based diagnostic yield comparison
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Figure Legends
Figure 1: Distribution of melanoma cases (n = 687) by clinically informative variants. Cases were counted based on presence of one or more clinically informative variants (Class 1-3). Cases with Class 4 and/or 5 variants were considered cases with variants of unknown significance; VOUS. Light grey boxes represent number of cases where clinically informative variant yield (Class 1-3 variants) was the same between the NGS TST26 panel and non-NGS assays (cases with one BRAF, NRAS or KIT variants; or multi-variant on NGS but; Class 4 and 5 variants). Dark grey boxes represent cases that had additional clinically informative variants detected by NGS.
Figure 2A: Mutational Landscape of Colorectal cancer cohort (n=281). Genes on the TST26 panel are listed on the vertical axis. The number of variants in a each gene is indicated by colors: blue = 1 variant/gene; orange = 2 variants/gene and red = 3 variants/gene. Bars on the right indicate the overall number of variants observed per gene. Different type of variants (missense, nonsense, duplication, frameshift, ins/dels and splice site) are indicated by colors. B: Distribution of CRC cases (n = 281) based on presence of clinically informative variants. Cases were counted based on presence of one or more clinically informative variants (Class 1-3). Cases with Class 4-5 variants were considered cases with variants of unknown significance; VOUS. Light grey boxes indicate cases where the clinically variant load was same between TST26 panel and previous non-NGS laboratory assays. These were either cases that were single-variant for BRAF, NRAS or KIT variants/exonic regions tested by our laboratory or multi-variant for genomic regions previously tested for along with 20
NGS-based diagnostic yield comparison
variants of unknown significance (VOUS). Dark grey boxes were cases that had additional clinically informative variants detected by NGS when compared to nonNGS assays.
Figure 3A: Mutational Landscape of GIST cohort (n=73). Some cases had more than one variant in a particular gene as indicated by different colors: blue = 1 variant/gene;orange = 2 variants/gene and red = 3 variants/gene. The bars on the right plotted the overall number of variants observed per gene by NGS and Sanger combined. Different type of variants (missense, nonsense, duplication, frameshift, ins/dels and splice site) were indicated by different colors.The pink cells in the first row indicated the cases that underwent nomenclature changes following Sanger sequencing; the purple cells indicate the number of the cases that were variant negative by NGS but variant positive by Sanger. B: Distribution of GIST cases (n = 73) based on the variant status post NGS testing. Grey boxes indicate cases in which variant status changed post Sanger sequencing. These were either cases that test were previously variant-negative by NGS but variant positive for KIT exon 11 variants by Sanger or cases were the nomenclature of KIT/PDGFRA variants changed following Sanger sequencing. Majority of single or multi-variant variant positive case on NGS detected either KIT or PDGFRA variant. C: Comparison of the distribution of overall variants and clinically informative variants in GIST cases detected by NGS or Sanger shown.
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Table 1: Distribution of BRAF variants in melanoma: A total of 232 BRAF variants were detected in 230 BRAF positive melanoma cases. A total of 175 cases had BRAF V600E or V600K variants identifiable by non-NGS assays (columns 1 and 2). 53 cases had a BRAF variant identified only by NGS (columns 3-8), and 2 cases had two BRAF variants (columns 9 and 10) Cases with Cases with one BRAF variant (N = 228)
multiple BRAF variants (N = 2) BRAF
BRAF
# of
V600
case
variants
s
BRAF V600like variants
BRAF
# of
BRAF
# of
other
cas
other
cas
variants
es
variants
es
# of cases
variant
# of
combinati
cases
ons
V600
V600E; 136
T599dup
2
N581I
3
S467L
5
E
G606E
V600
V600_Lys60 39
K
1
L597S; 2
L584F
1
G469E
4
1delinsEN
1 L584F
V600_Lys60 1
E586K
1
G466E
2
1
D594N
3
G464A
1
V600R
9
D594E
1
G466R
1
V600M
1
D594G
1
G466V
1
K601E
4
G596C
3
G469A
1
G596V
1
G469R
1
L597R
1
G469S
1
1delinsEI V600_K601 delinsE
22
NGS-based diagnostic yield comparison
S616F
1
Total 175
20
16
17
2
cases
23
A
B