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Multiplexed Digital Detection of B-Cell Acute Lymphoblastic Leukemia Fusion Transcripts Using the NanoString nCounter System

2019; Elsevier BV; Volume: 22; Issue: 1 Linguagem: Inglês

10.1016/j.jmoldx.2019.08.008

1943-7811

Yunan Zhong, Kassa Beimnet, Zaman Alli, Anthony Arnoldo, Paul E. Kowalski, Gino R. Somers, Cynthia Hawkins, Mohamed Abdelhaleem,

Lung Cancer Research Studies

Chromosomal rearrangements resulting in fusion transcripts have been reported in precursor B-cell acute lymphoblastic leukemia (B-ALL). The identification of fusion events is crucial in the diagnosis of B-ALL. In this study, we used NanoString nCounter technology to design, validate, and evaluate a multiplex panel for the detection of B-ALL fusion transcripts. Fifty-one B-ALL fusion transcripts reported in children in the literature were included in the design of the NanoString panel. Twenty-six fusion transcripts were validated using 64 positive-control samples and 74 negative-control samples with 100% sensitivity and 99% specificity in comparison to RT-PCR. Our results support a potential role of NanoString's technology as a robust and cost-effective technique that could be used in the detection of fusion transcripts and implemented into the diagnostic algorithm of B-ALL. Chromosomal rearrangements resulting in fusion transcripts have been reported in precursor B-cell acute lymphoblastic leukemia (B-ALL). The identification of fusion events is crucial in the diagnosis of B-ALL. In this study, we used NanoString nCounter technology to design, validate, and evaluate a multiplex panel for the detection of B-ALL fusion transcripts. Fifty-one B-ALL fusion transcripts reported in children in the literature were included in the design of the NanoString panel. Twenty-six fusion transcripts were validated using 64 positive-control samples and 74 negative-control samples with 100% sensitivity and 99% specificity in comparison to RT-PCR. Our results support a potential role of NanoString's technology as a robust and cost-effective technique that could be used in the detection of fusion transcripts and implemented into the diagnostic algorithm of B-ALL. Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, with B-lineage acute lymphoblastic leukemia (B-ALL) constituting approximately 80% to 85% of cases.1Baljevic M. Jabbour E. O'Brien S. Kantarjian H.M. Acute Lymphoblastic Leukemia. The MD Anderson Manual of Medical Oncology.ed 3. McGraw-Hill Education, New York2016Google Scholar Current clinical protocols for diagnosing B-ALL include morphology, flow-cytometric immunophenotyping, and cytogenetics and molecular methods. A high percentage of B-ALL cases have chromosomal rearrangements2Pui C.H. Robison L.L. Look A.T. Acute lymphoblastic leukaemia.Lancet. 2008; 371: 1030-1043Abstract Full Text Full Text PDF PubMed Scopus (1134) Google Scholar, 3Harrison C.J. Cytogenetics of paediatric and adolescent acute lymphoblastic leukaemia.Br J Haematol. 2009; 144: 147-156Crossref PubMed Scopus (181) Google Scholar, 4Mullighan C.G. Molecular genetics of B-precursor acute lymphoblastic leukemia.J Clin Invest. 2012; 122: 3407-3415Crossref PubMed Scopus (179) Google Scholar resulting in fusion events that define B-ALL subtypes and help in diagnostic classification, risk stratification, choice of treatment protocol, and prognosis. Molecular techniques used for detecting fusion events include fluorescent in situ hybridization, RT-PCR, RNA sequencing (RNA-Seq), and microarray (OncoScan/CytoScan; Affymetrix, Thermo Fisher Scientific, Waltham, MA). However, these techniques can be iterative, expensive, and time consuming, and lack detailed information about the fusions and their exon junctions. NanoString nCounter (NanoString Technologies, Inc., Seattle, WA) is a relatively new technology that can be used for resolving most of the limitations of the techniques listed above. It can be used for detecting and distinguishing among multiple (up to 800) different fusions and exon variants5Kulkarni M.M. Digital multiplexed gene expression analysis using the nanoString nCounter system.Curr Protoc Mol Biol. 2011; : 25B.10.1-25B.10.17Google Scholar,6Hu D. Zhou W. Wang F. Shu S.M. Fan L.L. He J. Wang P. He Y.L. Du W. Zhang J.H. Duan J.X. Sun L. Zheng J. Li X.Q. Li H.Y. Feng X.L. Huang S.A. Development of a nanoString assay to detect leukemogenic fusion transcripts in acute myeloid leukemia.Int J Lab Hematol. 2016; 38: 663-673Crossref PubMed Scopus (8) Google Scholar in a single reaction. In addition, the system utilizes a target-specific color-coded barcode/tag to directly measure the level of RNA expression in a 2-day procedure, without the involvement of any amplification step or enzymatic reaction.7Geiss G.K. Bumgarner R.E. Birditt B. Dahl T. Dowidar N. Dunaway D.L. Fell H.P. Ferree S. George R.D. Grogan T. James J.J. Maysuria M. Mitton J.D. Oliveri P. Osborn J.L. Peng T. Ratcliffe A.L. Webster P.J. Davidson E.H. Hood L. Dimitrov K. Direct multiplexed measurement of gene expression with color-coded probe pairs.Nat Biotechnol. 2008; 26: 317-325Crossref PubMed Scopus (1554) Google Scholar, 8Sheth J. Arnoldo A. Zhong Y. Marrano P. Pereira C. Ryall S. Thorner P. Hawkins C. Somers G.R. Sarcoma subgrouping by detection of fusion transcripts using NanoString nCounter technology.Pediatr Dev Pathol. 2019; 22: 205-213Crossref PubMed Scopus (9) Google Scholar, 9Ryall S. Arnoldo A. Krishnatry R. Mistry M. Khor K. Sheth J. Ling C. Leung S. Zapotocky M. Guerreiro Stucklin A. Lassaletta A. Shago M. Tabori U. Hawkins C.E. Multiplex detection of pediatric low-grade glioma signature fusion transcripts and duplications using the NanoString nCounter system.J Neuropathol Exp Neurol. 2017; 76: 562-570Crossref PubMed Scopus (31) Google Scholar To determine the potential clinical utility of NanoString nCounter in detecting fusion transcripts in pediatric B-ALL, we performed this study in which a novel NanoString panel was designed, validated, and evaluated. This NanoString panel was specific to the most frequent pediatric B-ALL leukemia fusions, including breakpoint cluster region–tyrosine-protein kinase ABL1 (BCR-ABL; ABL1), transcription factor 3–pre-B-cell leukemia homeobox 1 [E2A (TCF3)-PBX1], mixed-lineage leukemia–acute lymphoblastic leukemia 1 fused gene on chromosome 4 (MLL-AF4; KMT2A-AFF1), and translocation-Ets-leukemia virus–acute myeloid leukemia 1 (TEL-AML1; ETV6-RUNX1), as well as fusion transcripts detected in the Philadelphia chromosome (Ph)-like subgroup of B-ALL.4Mullighan C.G. Molecular genetics of B-precursor acute lymphoblastic leukemia.J Clin Invest. 2012; 122: 3407-3415Crossref PubMed Scopus (179) Google Scholar Bone marrow aspirate or peripheral blood specimens were processed by centrifugation to obtain the buffy coat layer, which was used in total RNA isolation using the TRIzol method, Norgen Biotek Total RNA Purification Plus kit (catalog number 48300; Norgen Biotek, Thorold, ON, Canada), Master Pure DNA & RNA Purification kit (catalog number MC85200; Lucigen Corp., Middleton, WI), or Direct-zol RNA Miniprep kit (catalog number R2050; Zymo Research, Irvine, CA). In the conventional TRIzol method, 1 mL of TRIzol reagent (catalog number 15596026; Ambion by Life Technologies, Carlsbad, CA) was added to 50 to 100 μL of buffy coat sample, followed by shaking and incubation at room temperature. A total of 0.2 mL of chloroform was then added, followed by shaking and incubation at room temperature. The colorless upper aqueous phase was retained after microcentrifugation and 0.5 mL of isopropyl alcohol was added, followed by shaking and incubation at room temperature. Then, supernatant was removed after microcentrifugation and RNA pellet was washed with 75% ethanol. Supernatant was removed after the final round of microcentrifugation and the RNA pellet was dried. The RNA was dissolved in RNase-free water, and quantified and qualified on a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). Total RNA was isolated using the Norgen Biotek Total RNA Purification Plus kit, Master Pure DNA & RNA Purification kit, or Direct-zol RNA Miniprep kit, by following the manufacturers' protocols. To synthesize cDNA, PCR master mix I (2.5 mmol/L dNTP, GeneAmp 10× PCR Gold buffer, and 25 mmol/L MgCl2) was added to 1 μg of the RNA from specimen and positive-control RNA. Samples were incubated in a thermal cycler (model PTC 200; Marshall Research, Hampton, NH; or Touchgene; Techne Limited, Cambridge, UK) at 70°C for 10 minutes and then at 4°C for 2 minutes. After incubation, Master Mix II (50 μmol/L random hexamers, 20 U/μL RNase inhibitor, 200 U/μL Superscript II RT, and 0.1 mol/L dithiothreitol) was added to the reaction. The reaction was incubated in a thermal cycler at 20°C for 10 minutes, at 42°C for 45 minutes, and at 99°C for 3 minutes. After reverse transcription, quality-control PCR was performed by adding Master Mix III [2.5 mmol/L dNTP, GeneAmp 10× PCR Gold buffer, 25 mmol/L MgCl2, AmpliTaq Gold (0.1 μL), 50 μmol/L primer G6PD–forward (5′-ATTCATCATCATGGGTGCATCG-3′) and reverse (5′-TGTTTGCGGATGTCAGCCACTGT-3′), H2O] to the cDNA from specimen and control. To detect fusion transcripts, Master Mix IV [2.5 mmol/L dNTP, GeneAmp 10× PCR Gold buffer, 25 mmol/L MgCl2, AmpliTaq Gold (0.2 μL), 20 μmol/L target primer (Table 1)—forward and reverse, H2O] was added to the cDNA. The PCR conditions were as follows: 95°C for 10 minutes; 35 cycles of 94°C for 30 seconds, 65°C for 1 minute, and 72°C for 60 seconds; and finally, 72°C for 10 minutes. PCR Master Mixes I, III, and IV were from the GeneAmp Gold PCR reagent kit (catalog number 4312778; Applied Biosystems, Foster City, CA). PCR Master Mix II was from Invitrogen SuperScript II (catalog number 18064014; Invitrogen by Life Technologies, Carlsbad, CA).Table 1Primer Sequences for RT-PCR to Detect the Validated Fusions on the B-ALL Fusion NanoString PanelFusion targetForward primer sequenceReverse primer sequenceG6PD for RNA quality control5′-ATTCATCATCATGGGTGCATCG-3′5′-TGTTTGCGGATGTCAGCCACTGT-3′BCR-ABL p190 to distinguish between e1-a2 and e1-a3BCR-e1-A: 5′-GACTGCAGCTCCAATGAGAAC-3′BCR-e1-C: 5′-CAGAACTCGCAACAGTCCTTC-3′ABL-a3-B: 5′-GTTTGGGCTTCACACCATTCC-3′ABL-a3-D: 5′-TTCCCCATTGTGATTATAGCCTA-3′ABL-a3-E3': 5′-TGACTGGCGTGATGTAGTTGCTT-3′BCR-ABL p210 to distinguish among b3-a2, b2-a2, b3-a3, and b2-a3BCR-b1-A: 5′-GAAGTGTTTCAGAAGCTTCTCC-3'BCR-b2-C: 5′-CAGATGCTGACCAACTCGTGT-3′ABL-a3-B: 5′-GTTTGGGCTTCACACCATTCC-3'ABL-a3-D: 5′-TTCCCCATTGTGATTATAGCCTA-3'ABL-a3-E3': 5′-TGACTGGCGTGATGTAGTTGCTT-3′E2A-PBX1 to distinguish between e13-e2 and e13-ins27-e2E2A-A: 5′-CACCAGCCTCATGCACAAC-3′E2A-C: 5′-CACCCTCCCTGACCTGTCT-3′PBX-B: 5′-TCGCAGGAGATTCATCACG-3′PBX-D: 5′-GGCCTGCTCGTATTTCTCC-3′PBX-E3': 5′-TGAACTTGCGGTGGATGAT-3′TEL-AML1 to distinguish between e5-e2 and e5-e3TEL-A: 5′-GCACCCTCTGATCCTGAAC-3'TEL-C: 5′-AAGCCCATCAACCTCTCTCATC-3'TEL-E5': 5′-CGCACCAGGAGAACAACCAC-3′AML1-B: 5′-AACGCCTCGCTCATCTTGC-3'AML1-D: 5′-TGGAAGGCGGCGTGAAGC-3′MLL-AF4 to distinguish among e10-e4, e10-e5, e11-e4, e11-e5, e8-e7, e9-e4, and e9-e5MLL-A: 5′-CCGCCTCAGCCACCTAC-3′MLL-C: 5′-AGGACCGCCAAGAAAAGA-3′MLL-E5': 5′-AAGCCCGTCGAGGAAAAG-3′AF4-B: 5′-TGTCACTGAGCTGAAGGTCG-3′AF4-D: 5′-CGTTCCTTGCTGAGAATTTG-3′ETV6-ABL1-(e5/e2)1st set:NZ5: 5′-TCCTGATCTCTCTCGCTG-3'NZ7: 5′-ACTCGATCCGCCTGCCTGCG-3'TEL-143F: 5′-GCCGGAGGTCATACTGCATCAG-3'Oligo-20S: 5′-CGTGGAATTCAAACAGTCCA-3′2nd set:ETV6EX5F: 5′-CGTGATCCAGCTGATGCC-3′1st set:Abl-minus: 5′-CATTGTGATTATAGCCTAAGACCCGGAG-3'IP Abl-minus: 5′-TCTCCACTGGCCACAAAATCATACAG-3'ABL-4: 5′-TCCACTGGCCACAAAATCATACAGT-3'Oligo-I: 5′-GACCCGGAGCTTTTCACCTTTAGTT-3′2nd set:ABL1E3R2: 5′-TGTAGTTGCTTGGGACCCAGCCTTG-3′RANBP2-ABL1-(e18/e2)RANBP2e16F2: 5′-TGGTTCTTTGCGAAATGCAGATTCA-3′ABL1E3R2: 5′-TGTAGTTGCTTGGGACCCAGCCTTG-3′SNX2-ABL1-(e3/e4)SNX2e3F1: 5′-CGGAACCTTCTCCTGCAGTCACACC-3′ABL1e4_R2: 5′-GCCACCGTCAGGCTGTATTTCTTCC-3′NUP214-ABL1-(e34/e3)NUPe20F1: 5′-CAGTGGCCTTGGAGGAAAACCCAGT-3′ABL1E3R2: 5′-TGTAGTTGCTTGGGACCCAGCCTTG-3′ZMIZ1-ABL1-(e18/e2)ZMIZ1e17_F1: 5′-GCAACACCATCCAGATCACCGTCAC-3′ABL1E3R2: 5′-TGTAGTTGCTTGGGACCCAGCCTTG-3′SSBP2-CSF1R-(e6/e12)SSBP2e14_F1: 5′-CCCATGGGTGGATTAGGAGGAATGG-3′CSF1Re14_R2: 5′-TGGCTCATGATCTTCAGCTCGGACA-3′EBF1-PDGFRB-(e15/e11)EBF1e14F2: 5′-CACGAGCATGAACGGATACGGCTCT-3′PDGFRBe12R1: 5′-ATGGCCGTCAGAGCTCACAGACTCA-3′ETV6-NTRK3-(e5/e15)ETV6: 5′-ATGGCAAAGCTCTCCTGCTGCTGAC-3′NTRK3: 5′-ATCTTGTCCTTGGTCGGGCTGAGGT-3′ATF7IP-JAK2-(e13/e17)ATF7IPe12_F2: 5′-AACCCATACAACCAGCACCGCCTCT-3′JAK2e19R3: 5′-CGGCACATCTCCACACTCCCAAAAT-3′BCR-JAK2-(e1/e15)BCRe1F1: 5′-GTGCCATAAGCGGCACCGGCACT-3′JAK2e19R3: 5′-CGGCACATCTCCACACTCCCAAAAT-3′BCR-JAK2-(e1/e17)BCRe1F1: 5′-GTGCCATAAGCGGCACCGGCACT-3′JAK2e19R3: 5′-CGGCACATCTCCACACTCCCAAAAT-3′B-ALL, B-cell acute lymphoblastic leukemia. Open table in a new tab B-ALL, B-cell acute lymphoblastic leukemia. Probe design and construction were performed in collaboration with NanoString Technologies (Seattle, WA). nCounter Element reagents were purchased from NanoString, and probes were synthesized by IDT (Coralville, IA). A total of 200 ng of RNA was mixed with the probes (IDT), nCounter Elements TagSet and hybridization buffer (NanoString Technologies) following the manufacturer's protocol. The mix was incubated for 20 hours at 67°C in a Bio-Rad C1000 Touch thermal cycler (Montreal, QC, Canada). Sample processing was performed using the nCounter Prep Station (NanoString Technologies), and RNA counting was performed using the nCounter digital analyzer (NanoString Technologies). The geometric means of the housekeeping transcripts, GUSB, TBP, PGK1, and CLTC, were used to determine the RNA quality. Raw counts directly from the nCounter digital analyzer were subjected to normalization using the internal positive spike-in controls by nSolver analysis software version 4.0 (NanoString Technologies), followed by probe-specific background correction. Boxplots generated by R statistical software package version 3.4.0 (The R Project for Statistical Computing, http://cran.utstat.utoronto.ca, last assessed May 17, 2019) were used for displaying the data produced by NanoString. The extreme outlier statistical method was used for determining the presence/absence of the fusion transcripts. A fusion transcript was considered as expressed if the count number was beyond the outer boundary, defined as the upper quantile (Q3) plus 3 times the interquartile range in the boxplot. To detect the 9 common pediatric B-ALL fusion transcripts, 30 samples that were positive for these nine fusions and 74 samples negative for all of the fusions on the panel, were provided by the Division of Hematopathology, Department of Pediatric Laboratory Medicine, Hospital for Sick Children (Toronto, ON, Canada). Similarly, 17 fusion transcripts seen in Ph-like B-ALL, 34 samples that were positive for these 17 fusions were provided by the Hospital for Sick Children and the Children's Oncology Group (Monrovia, CA), and 74 samples negative for all of the fusions on the panel, were provided by SickKids. RT-PCR was used for confirming the presence/absence of the fusion transcript in each sample. The target fusion transcripts and types of samples are listed in Supplemental Tables S1 and S2, respectively. A literature review of articles from peer-reviewed journals was conducted to identify fusion transcripts in pediatric B-ALL in which the sequence at the fusion junction was available.10Nakao M. Yokota S. Horiike S. Taniwaki M. Kashima K. Sonoda Y. Koizumi S. Takaue Y. Matsushita T. Fujimoto T. Misawa S. Detection and quantification of TEL/AML1 fusion transcripts by polymerase chain reaction in childhood acute lymphoblastic leukemia.Leukemia. 1996; 10: 1463-1470PubMed Google Scholar, 11van Dongen J.J. Macintyre E.A. Gabert J.A. Delabesse E. Rossi V. Saglio G. Gottardi E. Rambaldi A. Dotti G. Griesinger F. Parreira A. Gameiro P. Diáz M.G. Malec M. Langerak A.W. San Miguel J.F. Biondi A. Standardized RT-PCR analysis of fusion gene transcripts from chromosome aberrations in acute leukemia for detection of minimal residual disease. Report of the BIOMED-1 Concerted Action: investigation of minimal residual disease in acute leukemia.Leukemia. 1999; 13: 1901-1928Crossref PubMed Scopus (1006) Google Scholar, 12Iwata S. Mizutani S. Nakazawa S. Yata J. Heterogeneity of the breakpoint in the ABL gene in cases with BCR/ABL transcript lacking ABL exon a2.Leukemia. 1994; 8: 1696-1702PubMed Google Scholar, 13Divoky V. Trka J.M. Watzinger F. Lion T. Cryptic splice site activation during RNA processing of MLL/AF4 chimeric transcripts in infants with t(4;11) positive ALL.Gene. 2000; 247: 111-118Crossref PubMed Scopus (10) Google Scholar, 14Borkhardt A. Repp R. Haupt E. Brettreich S. Buchen U. Gossen R. Lampert F. Molecular analysis of MLL-1/AF4 recombination in infant acute lymphoblastic leukemia.Leukemia. 1994; 8: 549-553PubMed Google Scholar, 15Hilden J.M. Chen C.S. Moore R. Frestedt J. Kersey J.H. Heterogeneity in MLL/AF-4 fusion messenger RNA detected by the polymerase chain reaction in t(4;11) acute leukemia.Leukemia. 2005; 19: 2016-2018PubMed Google Scholar, 16von Bergh A. Gargallo P. De Prijck B. Vranckx H. Marschalek R. Larripa I. Kluin P. Schuuring E. Hagemeijer A. Cryptic t(4;11) encoding MLL-AF4 due to insertion of 5' MLL sequences in chromosome 4.Leukemia. 2001; 15: 595-600Crossref PubMed Scopus (27) Google Scholar, 17Gu Y. Nakamura T. Alder H. Prasad R. Canaani O. Cimino G. Croce C.M. Canaani E. The t(4;11) chromosome translocation of human acute leukemias fuses the ALL-1 gene, related to Drosophila trithorax, to the AF-4 gene.Cell. 1992; 71: 701-708Abstract Full Text PDF PubMed Scopus (793) Google Scholar, 18Lengline E. Beldjord K. Dombret H. Soulier J. Boissel N. Clappier E. Successful tyrosine kinase inhibitor therapy in a refractory B-cell precursor acute lymphoblastic leukemia with EBF1-PDGFRB fusion.Haematologica. 2013; 98: e146-e148Crossref PubMed Scopus (133) Google Scholar, 19Roberts K.G. Li Y. Payne-Turner D. Harvey R.C. Yang Y.L. Pei D. et al.Targetable kinase-activating lesions in ph-like acute lymphoblastic leukemia.N Engl J Med. 2014; 371: 1005-1015Crossref PubMed Scopus (931) Google Scholar, 20Song J.S. Shin S.-Y. Lee S.-T. Kim H.-J. Kim S.-H. A cryptic ETV6/ABL1 rearrangement represents a unique fluorescence in situ hybridization signal pattern in a patient with B acute lymphoblastic leukemia.Ann Lab Med. 2014; 34: 475-477Crossref PubMed Scopus (6) Google Scholar, 21Roberts K.G. Morin R.D. Zhang J. Hirst M. Zhao Y. Su X. et al.Genetic alterations activating kinase and cytokine receptor signaling in high-risk acute lymphoblastic leukemia.Cancer Cell. 2012; 22: 153-166Abstract Full Text Full Text PDF PubMed Scopus (524) Google Scholar, 22Lacronique V. Boureux A. Valle V.D. Poirel H. Quang C.T. Mauchauffé M. Berthou C. Lessard M. Berger R. Ghysdael J. Bernard O.A. A TEL-JAK2 fusion protein with constitutive kinase activity in human leukemia.Science. 1997; 278: 1309-1312Crossref PubMed Scopus (680) Google Scholar, 23Nebral K. Denk D. Attarbaschi A. König M. Mann G. Haas O.A. Strehl S. Incidence and diversity of PAX5 fusion genes in childhood acute lymphoblastic leukemia.Leukemia. 2009; 23: 134-143Crossref PubMed Scopus (152) Google Scholar, 24Mustjoki S. Hernesniemi S. Rauhala A. Kähkönen M. Almqvist A. Lundán T. Porkka K. A novel dasatinib-sensitive RCSD1-ABL1 fusion transcript in chemotherapy-refractory adult pre-B lymphoblastic leukemia with t(1;9)(q24;q34).Haematologica. 2009; 94: 1469-1471Crossref PubMed Scopus (26) Google Scholar, 25Ernst T. Score J. Deininger M. Hidalgo-Curtis C. Lackie P. Ershler W.B. Goldman J.M. Cross N.C. Grand F. Identification of FOXP1 and SNX2 as novel ABL1 fusion partners in acute lymphoblastic leukaemia.Br J Haematol. 2011; 153: 43-46Crossref PubMed Scopus (33) Google Scholar By using these criteria, 51 fusion transcripts were identified, including common fusions and those present in Ph-like B-ALL (Supplemental Table S1). In addition, five expression-level controls and four housekeeping genes were included in the panel to ensure the quality of the RNA and the run. Although the 4 housekeeping genes were used to assess the RNA quality, the five additional expression-level controls were genes that are commonly altered in cancer, namely up-regulation in B-ALL.26Zhong Y: Dataset for "Multiplexed Digital Detection of B-ALL Leukemia Fusion Transcripts using the NanoString nCounter System" Publication, v2. Mendeley Data 2019. Available at https://doi.org/10.17632/2bhgbvgn9p.3#folder-68e4b73a-8e62-481f-8ac8-582ebadb3cb1.Google Scholar They were selected and used as an additional quality-control measure to ensure that the expression pattern of the patient sample was of reasonably expected quality and that there was no obvious aberrancy. Bone marrow, peripheral blood, cell line, and in vitro–transcribed (IVT) RNA samples were used. An example of the panel output with different sample types is shown in Figure 1. Bone marrow samples 1 and 2 showed two positive cases of the expression of fusion transcripts BCR-ABL-(e13e2) and BCR-ABL-(e1e2), respectively, which have the same fusion partners but different exon variants (Figure 1). Bone marrow sample 3 showed one positive case in which one fusion transcript (Supplemental Table S3) showed blast differential count in patient samples. ETV6-ABL1-(e5e2), seen in Ph-like B-ALL, was expressed (Figure 1). Bone marrow sample 4 showed one negative case in which no fusion transcript on the panel was expressed, indicated as no outlier beyond background counts (Figure 1). Bone marrow sample 5 showed that two exon variants of the TEL-AML1 fusion, TEL-AML1-e5e2 and -e5e3, were co-expressed in a single sample (Figure 1), which has been documented before.13Divoky V. Trka J.M. Watzinger F. Lion T. Cryptic splice site activation during RNA processing of MLL/AF4 chimeric transcripts in infants with t(4;11) positive ALL.Gene. 2000; 247: 111-118Crossref PubMed Scopus (10) Google Scholar,27Liang D.C. Shih L.Y. Yang C.P. Hung I.J. Chen S.H. Jaing T.H. Liu H.C. Chang W.H. Multiplex RT-PCR assay for the detection of major fusion transcripts in Taiwanese children with B-lineage acute lymphoblastic leukemia.Med Pediatr Oncol. 2002; 39: 12-17Crossref PubMed Scopus (23) Google Scholar The co-expressions were also confirmed by RT-PCR. Bone marrow sample 6 was an example of a degraded RNA sample, as determined by the geometric means of the expression levels of the four housekeeping genes (GUSB, TBP, PGK1, and CLTC; Supplemental Table S1) (Figure 1). This value was indicative of the RNA quality of the sample. In NanoString assay, good RNA quality (as assessed by the quantification of the four housekeeping transcripts) was necessary and sufficient for reliable detection of the fusion transcripts. RNAs from >650 archived samples (FFPE) ranging from the years 1983 to 2015 were run on the NanoString instrument. For each year, geometric means of the samples for the four housekeeping transcripts were calculated and used as a proxy for RNA quality. The RNA quality decreased with the age of the FFPE sample, and reached a plateau at which low housekeeping count numbers stabilized due to poor RNA quality.28Zhong Y: Dataset for "Multiplexed Digital Detection of B-ALL Leukemia Fusion Transcripts using the NanoString nCounter System" Publication, v2. Mendeley Data 2019. Available at https://doi.org/10.17632/2bhgbvgn9p.3#file-e5e1b529-37a7-440a-a3a8-53567680538f.Google Scholar Based on this observation, RNAs extracted from samples older than 13 years were considered as degraded. As a result, a mean value of 148.6 was obtained from all of the housekeeping values from the years before 2002. Therefore, geometric means of <150 were considered as RNA of insufficient quality and were flagged. In such cases of degraded samples, the test was reported as suboptimal for analysis. Assay or sample failure rate was assessed by noting different factors that might have contributed to a sample's not passing the RNA-quality parameter on NanoString. Of the 138 samples run on NanoString, 5 failed or were flagged as degraded on NanoString, with a success rate of 96%. All 5 samples were SickKids patient cases used as negative controls for the B-ALL fusion transcripts on the NanoString panel. All of them passed other quality-control parameters during data pre-processing using nSolver, including the internal quality-control step and normalization step using internal spiked-in positive controls. Specifically, for the two old archived RNA samples that failed, one was extracted from the bone marrow aspirate of a patient, and one, from the peripheral blood of another patient. The samples were not of particularly old age compared with other SickKids-archived RNA samples. RT-PCR was performed on fresh RNA extracts immediately after patient samples were obtained. One possibility is that during the long-term storage and repeated retrieving and handling procedures of these two samples, the RNA quality decreased over time. One way to resolve this problem was to load more than the required 200 ng of RNA onto NanoString, which worked with one sample because its RNA concentration was relatively high.29Zhong Y: Dataset for "Multiplexed Digital Detection of B-ALL Leukemia Fusion Transcripts using the NanoString nCounter System" Publication, v2. Mendeley Data 2019. Available at https://doi.org/10.17632/2bhgbvgn9p.3#file-d52a0331-a43e-4fcc-967a-0a27d78df859.Google Scholar This method of loading an increased amount of RNA when 200 ng was not sufficient, which is rare, has been routinely practiced in our clinical and research laboratories. The other three failed RNA samples were part of an experiment that compared the efficiency of different RNA-extraction methods. One RNA sample extracted from peripheral blood using the Direct-zol method failed, whereas the RNAs extracted from the same sample using the Norgen Biotek and Master Pure methods worked. The failed sample was of very low concentration, and the maximum amount of RNA was already loaded onto NanoString. It was noted that for all other samples that passed RNA quality control on NanoString, the RNAs extracted using Direct-zol were of consistently lower RNA quality on NanoString compared with Norgen Biotek and Master Pure. Norgen Biotek and Master Pure were further compared, and it was found that Norgen Biotek had better performance than did Master Pure. The other two RNA samples that failed were from a patient's bone marrow aspirate using both Norgen Biotek and Master Pure. The patient samples were provided courtesy of the clinical laboratory after routine work; therefore, the sample amount was limited. This particular patient sample was of a lesser amount compared with the rest of the patient samples. Again, loading the maximum amount of RNA resolved the problem of insufficient RNA quality for the one extracted using Norgen Biotek.30Zhong Y: Dataset for "Multiplexed Digital Detection of B-ALL Leukemia Fusion Transcripts using the NanoString nCounter System" Publication, v2. Mendeley Data 2019. Available at https://doi.org/10.17632/2bhgbvgn9p.3#file-599e95a1-1bca-4509-bb62-9003439cec0c.Google Scholar Peripheral blood sample 7 showed one positive case in which MLL-AF4-(e11e4) was expressed in the sample (Figure 1). Peripheral blood sample 8 showed one negative case in which no fusion transcript on the panel was expressed (Figure 1). Cell line sample 9 showed one positive control in which E2A-PBX1-(e13e2) was expressed (Figure 1). Due to the rarity of some fusions in Ph-like B-ALL, positive patient samples were unavailable. Therefore, RNAs were synthesized from IVT and used as positive controls for these fusions. To mimic the in vivo physiologic conditions, the IVT RNAs were diluted to the fmol/L range that gives fusion counts in the thousand range, which is considered normal in patients in clinical practice.8Sheth J. Arnoldo A. Zhong Y. Marrano P. Pereira C. Ryall S. Thorner P. Hawkins C. Somers G.R. Sarcoma subgrouping by detection of fusion transcripts using NanoString nCounter technology.Pediatr Dev Pathol. 2019; 22: 205-213Crossref PubMed Scopus (9) Google Scholar,31Zhong Y: Dataset for "Multiplexed Digital Detection of B-ALL Leukemia Fusion Transcripts using the NanoString nCounter System" Publication, v2. Mendeley Data 2019. Available at https://doi.org/10.17632/2bhgbvgn9p.3#folder-c2bd9898-7f65-4a52-8e57-dd56eff349a6.Google Scholar The IVT RNAs were then spiked into samples of both normal peripheral blood (sample 10) and normal bone marrow (sample 11). IVT RNA samples 10 and 11 showed two positive controls, in which two fusion transcripts, NUP214-ABL1-(e34e3) and ATF7IP-JAK2-(e13e17), respectively, were expressed, as seen in Ph-like B-ALL (Figure 1). To test reproducibility, 21 samples, including 11 positive and 10 negative patient samples, were run on NanoString in replicates. Representative samples 1 and 2, and samples 3 and 4, showed reproducible results of the detected expression of MLL-AF4-(e10e4) and EBF1-PDGFRB-(e15e11), respectively (Figure 2). Representative samples 5 and 6 showed reproducible results of a negative case sample, with no expression of fusion transcript on the panel (Figure 2). To validate and assess the sensitivity and specificity of the B-ALL leukemia fusion NanoString panel, the results from NanoString were compared with those from RT-PCR. Nine common B-ALL leukemia fusion transcripts were validated using 30 samples that were positive for these 9 fusions and 74 samples negative for all of the fusions on the panel by NanoString and confirmed via RT-PCR with 100% sensitivity and 97% specificity (n = 104) (Table 2). Seventeen Ph-like B-ALL fusion transcripts were validated using 34 samples that were positive for these 17 fusions, including 10 IVT RNAs, and 74 samples negative for all of the fusions on the panel by NanoString and confirmed via RT-PCR with 100% sensitivity and 100% specificity (N = 108) (Table 1). In total, 26 B-ALL leukemic fusions were validated and summarized in Supplemental Table S1, also including four housekeeping genes and five expression-level controls (Supplemental Table S1).Table 2Sensitivity and Specificity of the B-ALL NanoString Panel as Compared with RT-PCR as the Gold StandardCategoryRT-PCRTest positiveTest neg