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QuantiGene Plex Represents a Promising Diagnostic Tool for Cell-of-Origin Subtyping of Diffuse Large B-Cell Lymphoma

2015; Elsevier BV; Volume: 17; Issue: 4 Linguagem: Inglês

10.1016/j.jmoldx.2015.03.010

ISSN

1943-7811

Autores

John Hall, Suzanne Usher, Richard Byers, Rebekah Clare Higgins, Danish Memon, John Radford, Kim Linton,

Tópico(s)

RNA Research and Splicing

Resumo

Emerging therapies targeting the molecularly distinct GCB and non-GCB/ABC subtypes of diffuse large B-cell lymphoma (DLBCL) have created the need to develop an accurate subtyping assay for routine use. We investigated the potential of QuantiGene Plex (QGP)—branched DNA signal amplification assay—for DLBCL subtyping. We performed in silico analysis of public DLBCL datasets to develop and validate a naïve Bayes classifier, and migrated the resulting 21-gene classifier to QGP and real-time quantitative PCR (qPCR) assays. Forty DLBCL formalin-fixed, paraffin-embedded tumors of known subtype (20 per subtype by gene expression profiling of paired fresh-frozen tissues) were reclassified, and results for QGP (on 38/40 for 21/21 targets) and qPCR (on 40/40 samples for 19/21 targets) compared for recapitulation of microarray data and classification accuracy. The 21-gene bayesian classifier achieved mean area under the curve values >0.9 on independent validation. QGP showed a higher correlation with microarray data (mean R2 = 0.66 ± 0.05 versus 0.34 ± 0.07; P < 0.0001) and classification accuracy (92.1% versus 78.9%). The proportion of validated targets was also higher for QGP (85.7% versus 47.4%). The QGP protocol was rapid and simple to perform, at a cost similar to qPCR. These promising preliminary results strongly support ongoing work to develop a QGP companion diagnostic assay for DLBCL subtyping. Emerging therapies targeting the molecularly distinct GCB and non-GCB/ABC subtypes of diffuse large B-cell lymphoma (DLBCL) have created the need to develop an accurate subtyping assay for routine use. We investigated the potential of QuantiGene Plex (QGP)—branched DNA signal amplification assay—for DLBCL subtyping. We performed in silico analysis of public DLBCL datasets to develop and validate a naïve Bayes classifier, and migrated the resulting 21-gene classifier to QGP and real-time quantitative PCR (qPCR) assays. Forty DLBCL formalin-fixed, paraffin-embedded tumors of known subtype (20 per subtype by gene expression profiling of paired fresh-frozen tissues) were reclassified, and results for QGP (on 38/40 for 21/21 targets) and qPCR (on 40/40 samples for 19/21 targets) compared for recapitulation of microarray data and classification accuracy. The 21-gene bayesian classifier achieved mean area under the curve values >0.9 on independent validation. QGP showed a higher correlation with microarray data (mean R2 = 0.66 ± 0.05 versus 0.34 ± 0.07; P < 0.0001) and classification accuracy (92.1% versus 78.9%). The proportion of validated targets was also higher for QGP (85.7% versus 47.4%). The QGP protocol was rapid and simple to perform, at a cost similar to qPCR. These promising preliminary results strongly support ongoing work to develop a QGP companion diagnostic assay for DLBCL subtyping. Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of lymphoma, with an annual incidence rate of approximately 5000 new cases in the United Kingdom (Haematological Malignancy Diagnostic Service, https://www.hmrn.org/statistics/incidence, last accessed March 3, 2015) and >55,000 new cases in the United States (Patients against Lymphoma, http://www.lymphomation.org/lymphoma-stats-seer-2014.pdf, last accessed March 3, 2015). Treatment with combination immunochemotherapy using rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or R-CHOP–like regimens is potentially curative, but cure rates are variable and influenced by underlying genetic heterogeneity. The cell-of-origin classification recognizes at least two discrete prognostic subtypes: activated B cell (ABC) and germinal center B cell (GCB). Patients with ABC tumors have a significantly inferior survival1Lenz G. Wright G. Dave S.S. Xiao W. Powell J. 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Immunostaining to identify molecular subtypes of diffuse large B-cell lymphoma in a population-based epidemiologic study in the pre-rituximab era.Int J Mol Epidemiol Genet. 2011; 2: 245-252PubMed Google Scholar, 25Rayman N. Lam K.H. van der Holt B. Koss C. Veldhuizen D. Budel L.M. Mulder A.H. Verdonck L.F. Delwel R. de J.D. van Imhoff G.W. Sonneveld P. Prognostic relevance of immunohistochemical subclassification of diffuse large B-cell lymphoma in two prospective phase III clinical trials.Clin Lymphoma Myeloma Leuk. 2011; 11: 23-32Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar There remains an unmet need to develop a simple, inexpensive, accurate, and reproducible cell-of-origin assay for routine DLBCL subtyping on diagnostic FFPE biopsy specimens. Real-time quantitative PCR (qPCR) is the industry gold standard assay for validating microarray data and is widely available in diagnostic laboratories, but it works less well with FFPE compared with fresh-frozen (FF) tissues. This is due to the presence of cross-linked RNA within FFPE tissue, which impedes RNA extraction and reduces the efficiency of enzymatic-driven reactions, such as reverse transcription and target amplification. Branched chain DNA technology offers a nonenzymatic alternative for nucleic acid detection and quantification,26Tsongalis G.J. Branched DNA technology in molecular diagnostics.Am J Clin Pathol. 2006; 126: 448-453Crossref PubMed Scopus (74) Google Scholar and branched chain DNA assays have an established track record as U.S. Food and Drug Administration–approved diagnostic assays for viral load quantification of hepatitis C and HIV-1 (Versant HIV and hepatitis C virus tests; Siemens Healthcare Diagnostics, Malvern, PA).27Murphy D.G. Gonin P. Fauvel M. 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Exon-array profiling unlocks clinically and biologically relevant gene signatures from formalin-fixed paraffin-embedded tumour samples.Br J Cancer. 2011; 104: 971-981Crossref PubMed Scopus (31) Google Scholar The QGP system uses up to 100 combinations of fluorescently impregnated (color-coded) polystyrene microspheres to generate capture beads with unique spectral characteristics. Each bead has a reagent coating specific to a particular bioassay, thereby generating a multiplex assay featuring up to approximately 100 probe sets or target sequences of interest. The 2-day protocol begins with an overnight incubation, during which RNA hybridizes directly to the capture probe on the bead. The resulting hybridization complex signal is amplified using branched capture extenders and preamplifier probes to generate a branched DNA structure. The structure is labeled with phycoerythrin-conjugated streptavidin, and a Luminex reader detects individual beads by flow cytometry. The fluorescent signal is proportionate to complexed RNA quantity, and the bead color gives capture probe coordinates and probe set identity. QGP is particularly well suited for FFPE because it avoids the problems generated by FFPE RNA cross-linkage: first, by hybridizing RNA directly (thus obviating an RNA extraction step), and second, by using signal rather than enzymatic amplification methods. QGP probe design is also more similar to gene microarrays than qPCR with multiple redundant (six to eight) small oligonucleotides (25 to 80 bp). Thus, even when a particular region is degraded or fails to hybridize, other probe sets are available to generate a signal. This results in excellent concordance between QGP and microarray data,33Yim H.W. Song B.J. Jung S.S. Kim H.-J. Choi Y.-J. Lee K.-Y. Lee A. Branched-chain assay for ER, PR, and HER2 RNA levels is a useful adjunct in the evaluation of ER, PR, and HER2 in breast cancer.J Breast Cancer. 2010; 13: 267-274Crossref Scopus (5) Google Scholar and makes QGP an ideal candidate for development of a diagnostic DLBCL subtyping assay. Herein, we investigated the performance of QGP for microarray validation and cell-of-origin classification of DLBCL in comparison with gold standard qPCR. Patients and samples used in this study have previously been described34Linton K. Howarth C. Wappett M. Newton G. Lachel C. Iqbal J. Pepper S. Byers R. Chan W.J. Radford J. Microarray gene expression analysis of fixed archival tissue permits molecular classification and identification of potential therapeutic targets in diffuse large B-cell lymphoma.J Mol Diagn. 2012; 14: 223-232Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar and included 40 histologically confirmed DLBCL cases preclassified (20 ABC and 20 GCB) by Affymetrix U133 Plus 2.0 (Santa Clara, CA) GEP profiling of FF tissues.1Lenz G. Wright G. Dave S.S. Xiao W. Powell J. Zhao H. et al.Stromal gene signatures in large-B-cell lymphomas.N Engl J Med. 2008; 359: 2313-2323Crossref PubMed Scopus (1335) Google Scholar, 4Lenz G. Wright G.W. Emre N.C. Kohlhammer H. Dave S.S. Davis R.E. Carty S. Lam L.T. Shaffer A.L. Xiao W. Powell J. Rosenwald A. Ott G. Muller-Hermelink H.K. Gascoyne R.D. Connors J.M. Campo E. Jaffe E.S. Delabie J. Smeland E.B. Rimsza L.M. Fisher R.I. Weisenburger D.D. Chan W.C. Staudt L.M. Molecular subtypes of diffuse large B-cell lymphoma arise by distinct genetic pathways.Proc Natl Acad Sci U S A. 2008; 105: 13520-13525Crossref PubMed Scopus (771) Google Scholar The data discussed herein have been deposited in National Center for Biotechnology Information's Gene Expression Omnibus34Linton K. Howarth C. Wappett M. Newton G. Lachel C. Iqbal J. Pepper S. Byers R. Chan W.J. Radford J. Microarray gene expression analysis of fixed archival tissue permits molecular classification and identification of potential therapeutic targets in diffuse large B-cell lymphoma.J Mol Diagn. 2012; 14: 223-232Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar (GEO; http://www.ncbi.nlm.nih.gov/geo; accession number GSE64555). Cores (1 mm) were removed from paired FFPE tissue under RNAse-free conditions, and RNA was extracted using an in-house optimized Qiagen RNeasy kit (Qiagen, Valencia, CA).34Linton K. Howarth C. Wappett M. Newton G. Lachel C. Iqbal J. Pepper S. Byers R. Chan W.J. Radford J. Microarray gene expression analysis of fixed archival tissue permits molecular classification and identification of potential therapeutic targets in diffuse large B-cell lymphoma.J Mol Diagn. 2012; 14: 223-232Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar Cases were reclassified by QGP and PCR gene expression analysis of FFPE RNA extracts. The UK National Research Ethics Committee reviewed the study and granted a favorable opinion (Multi-Research Ethics Committee/03/08/016). Written, informed patient consent was obtained for all cases. A signature generation workflow was implemented in R version 1.12 (http://www.r-project.org) to train a naïve Bayes signature of predefined content and length in good-quality data from the Lenz FF data set.1Lenz G. Wright G. Dave S.S. Xiao W. Powell J. Zhao H. et al.Stromal gene signatures in large-B-cell lymphomas.N Engl J Med. 2008; 359: 2313-2323Crossref PubMed Scopus (1335) Google Scholar The signature was derived from a previously reported set of 21 discriminatory ABC/GCB genes comprising 37 probe sets (Linton data set)34Linton K. Howarth C. Wappett M. Newton G. Lachel C. Iqbal J. Pepper S. Byers R. Chan W.J. Radford J. Microarray gene expression analysis of fixed archival tissue permits molecular classification and identification of potential therapeutic targets in diffuse large B-cell lymphoma.J Mol Diagn. 2012; 14: 223-232Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar: MME, MARCKSL1, MYBL1, CCND2, AFF2, CHST2, RRAS2, BIC, STAP1, KIAA1274, LOC440864, BCL6, LILRB2, C1QC, MPEG1, FGL2, RASLllA, CCL18, ADAM29, FCRL5, and CPNE5. The aim was to determine whether this signature could predict the probability of a DLBCL patient belonging to the ABC category. The Lenz data set1Lenz G. Wright G. Dave S.S. Xiao W. Powell J. Zhao H. et al.Stromal gene signatures in large-B-cell lymphomas.N Engl J Med. 2008; 359: 2313-2323Crossref PubMed Scopus (1335) Google Scholar was downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo; accession number GSE10846), and the R-CHOP–treated cohort was retained (n = 233). Eleven samples (GSM275110, GSM275112, GSM275115, GSM275172, GSM275186, GSM275187, GSM275204, GSM275209, GSM275259, GSM275265, and GSM275276) failed standard outlier detection (quality control) and were excluded. A further 31 GEP-unclassified samples were also excluded. The remaining 191 samples were labeled as ABC (n = 90) or GCB (n = 101). Data were normalized with MAS5.0 (Affymetrix) using a target intensity of 100. Data were transformed to log2. Bayes signature training in the Lenz FF DLBCL data set was performed with metrics generated under fivefold times 10 repeats of cross validation. A sample dichotomization threshold was selected to optimize the specificity and sensitivity of predictions, where the sum of sensitivity and specificity is a maximum. The Williams data set35Williams P.M. Li R. Johnson N.A. Wright G. Heath J.D. Gascoyne R.D. A novel method of amplification of FFPET-derived RNA enables accurate disease classification with microarrays.J Mol Diagn. 2010; 12: 680-686Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar was also downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo; accession number GSE19246). This data set consists of 59 matched FF and FFPE samples. Published labels were as follows: ABC (n = 21), GCB (n = 27), or unclassified (n = 11). Each sample is represented three times (177 samples): Eberwine FF, NuGEN FF, and NuGEN FFPE. This data set would, therefore, allow us to verify the classification potential of the Linton signature in independent paired FF and FFPE samples. Reverse transcription was performed using a method optimized for FFPE applications.36Byers R.J. Sakhinia E. Joseph P. Glennie C. Hoyland J.A. Menasce L.P. Radford J.A. Illidge T. Clinical quantitation of immune signature in follicular lymphoma by RT-PCR-based gene expression profiling.Blood. 2008; 111: 4764-4770Crossref PubMed Scopus (82) Google Scholar, 37Byers R. Roebuck J. Sakhinia E. Hoyland J. PolyA PCR amplification of cDNA from RNA extracted from formalin-fixed paraffin-embedded tissue.Diagn Mol Pathol. 2004; 13: 144-150Crossref PubMed Scopus (22) Google Scholar Briefly, cDNA was prepared using polyA cDNA reverse transcription. This uses a polyT primer to construct a 300- to 500-bp first-strand cDNA using the polyA mRNA tail as the priming site. The first-strand cDNA is then polyadenylated at its 3′ end using TDT, and the resultant cDNA, tailed with polyA at its 3′ end and polyT at its 5′ end, is used in multiple rounds of polyA PCR using polyT primers. cDNA (10 ng/μL) was used in subsequent real-time PCR reactions performed using fluorescent probes (and FAM fluorescent reporter) and gene-specific probes designed across exons within 500 bp of the 3′ polyA tail of relevant genes. Primers were designed using the Roche Universal Probe Library, also within 500 bp of the 3′ polyA tail; this was possible for all but 2 of 21 transcripts (FCRL5 and CCND2) (Table 1). Gene Expression Mastermix (Life Technologies, Paisley, UK) and standard thermocycling conditions were used; data were collected using an AB7900 instrument (Applied Biosystems, Foster City, CA), and values were exported for analysis in Microsoft Excel (Office 2013, Redmond, WA). Relative quantification was performed using the 2−ΔCT method,38Livak K.J. Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.Methods. 2001; 25: 402-408Crossref PubMed Scopus (123392) Google Scholar including normalization of target expression data to the geometric mean of four previously characterized reference genes (ACTB, GAPDH, RS9, and EIF2B).36Byers R.J. Sakhinia E. Joseph P. Glennie C. Hoyland J.A. Menasce L.P. Radford J.A. Illidge T. Clinical quantitation of immune signature in follicular lymphoma by RT-PCR-based gene expression profiling.Blood. 2008; 111: 4764-4770Crossref PubMed Scopus (82) Google ScholarTable 1Roche UPL and Custom Primer Design for Real-Time Quantitative PCR ExperimentsPrimer pairAffymetrix IDGene namePrimer designPrimer sequenceProbe sequence (UPL no.)11564078_atADAM29Roche UPL assay designF: 5′-CTGAGAGCAAAAAGCAGAAATG-3′R: 5′-GACACTGAGCTTAAGAAACACGAA-3′5′-TCCTGCTC-3′ (15)2203434_s_atMMERoche UPL assay designF: 5′-CCTGCTTTGACTGATGCTGA-3′R: 5′-ACCAAGTTTTGAGAGATAGAGCAAG-3′5′-CTTCCTGC-3′ (4)3200644_atMARCKSL1Roche UPL assay designF: 5′-ACTGCCCCGTCTAGGTTTTT-3′R: 5′-CCCAAAGCCTAATACTTGCT-3′5′-CTCCAGCC-3′ (19)4231887_s_atKIAA1274Roche UPL assay designF: 5′-CCACTGGCTGATCACATCAC-3′R: 5′-AGGCAAGTATTATTCTCCCATTTTAC-3′5′-CTCTGCCT-3′ (13)51569034_a_atLOC440864Primer ExpressF: 5′-CTCTTTGGTGGTTTCCCATGA-3′R: 5′-GCTGAAGGTAAATCTGTGCACACT-3′5′-CAGCCAAGGGAAATACCCAACAATCATTC-3′ (custom)6210957_s_atAFF2Roche UPL assay designF: 5′-AATGATCTCAGCCCTGCAAC-3′R: 5′-GCTTGGGTGACAAGTGAAGG-3′5′-GGCCACCA-3′ (49)7213906_atMYBL1Roche UPL assay designF: 5′-TTTCAGTATGTTATACAAATGCCAGA-3′R: 5′-TGTATGTGTAGTCAGTTTCCATGC-3′5′-GCAGCCAT-3′ (46)8203140_atBCL6Roche UPL assay designF: 5′-TCTGCGTCATGCTTGTGTTA-3′R: 5′-CAACGCGGTAATGCAGTTTA-3′5′-TGGCTGTG-3′ (76)9226818_atMPEG1Roche UPL assay designF: 5′-TCCCTGATGGAAGAGCTCAC-3′R: 5′-AGACCCCAGGCTAAGGTCAC-3′5′-CTGCCTTC-3′ (8)10208456_s_atRRAS2Roche UPL assay designF: 5′-AGAGCAGCCCGGCTAGATA-3′R: 5′-TGTTCTCTCATGGCTCCAAA-3′5′-CAGCAGGA-3′ (18)11238353_atRASL11APrimer ExpressF: 5′-ACGCTTCAAGCAGGCTCTGT-3′R: 5′-GAGAGGCATCTGTCTGAGATAGTTCA-3′5′-AGTCAAGCCCCCTCTGCACTGGG-3′ (custom)12229437_atBICRoche UPL assay designF: 5′-CTACCTTTCCACTTCTAAGCCTGT-3′R: 5′-AAGCCTCACAACAACCTTGTAAA-3′5′-CTTCCTCC-3′ (69)13203921_atCHST2Roche UPL assay designF: 5′-CCTTCTCTTGTCCTCTTTCTCCTAT-3′R: 5′-TTTCATTAACATCCCACTTCAAAA-3′5′-CTCCACCT-3′ (39)14227189_atCPNE5Roche UPL assay designF: 5′-ATCCCATCACCCCAAACATA-3′R: 5′-AGGAGCTCGGGCTCAGAT-3′5′-CTTCAGCC-3′ (41)15210146_x_atLILRB2Roche UPL assay designF: 5′-ATATGGGAGTGAGCCAGCA-3′R: 5′-AGCCTGTTCTGGGGTTAGTTT-3′5′-CAGCCCAG-3′ (26)1632,128_at and 209924_atCCL18Roche UPL assay designF: 5′-GAAGTTGCTGAAAGCCTTGG-3′R: 5′-AGCCTCATCTCGTATAGTCATGG-3′5′-GGAGGATG-3′ (88)17220059_atSTAP1Roche UPL assay designF: 5′-TCCAACAGCAGTTTCTCTAGTTCT-3′R: 5′-AAAGGGCTCCTCTAGTACATTTCA-3′5′-GCTGGAAG-3′ (11)18225353_s_atC1QCRoche UPL assay designF: 5′-CTGCTCTTCCCCGACTAGG-3′R: 5′-AGTAAGGTGGGTCCATGCAG-3′5′-CCACCTCC-3′ (10)19227265_atFGL2Roche UPL assay designF: 5′-GCTCCAAATGAATTAATGACACA-3′R: 5′-GGGTAGCTATTCAAAAGTAAAGACCA-3′5′-CTGGGCAA-3′ (61)20ReferenceGAPDHPrimer ExpressF: 5′-CATGGCCTCCAAGGAGTAAG-3′R: 5′-GGGACTCCCCAGCAGTGA-3′5′-ACCAGCCCCAGCAAGAGCACAAGA-3′ (custom)21ReferenceACTBPrimer ExpressF: 5′-CCACCCCACTTCTCTCTAAGGA-3′R: 5′-CATAATTACACGAAAGCAATGCTATC-3′5′-TGGCCCAGTCCTCTCCCAAGTCC-3′ (custom)22ReferenceRS9Primer ExpressF: 5′-GAGACACGAGGAGGAGGATTA-3′R: 5′-GCAGGAAAACGAGACAATCCA-3′5′-TCCACCTGTCCCTCCTGGGCTG-3′ (custom)23ReferenceEIF2BPrimer ExpressF: 5′-TGGAGTTGGGATGTGGAAGTG-3′R: 5′-CTGCCGGCCTGCTTAG-3′5′-TCCTCCCTAGGCAGAAGCTTGTTCCAT-3′ (custom)Roche UPL assay design is from Roche Life Science (Basel, Switzerland). Primer Express is from Applied Biosystems (Foster City, CA).F, forward; R, reverse;

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