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Deriving HLA genotyping from whole genome sequencing data using Omixon HLA twin(tm) in G3’s global clinical study
Abstracts / Human Immunology 76 (2015) 38–167
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THE EFFECT OF PRONASE ON LYMPHOCYTE SURFACE MARKERS AND IMPLICATIONS FOR FLOW CYTOMETRIC CROSSMATCH. Eszter Lazar-Molnar a, Laura Spruit b, Ann Pole b, Kevin Williams b, Michelle R. Taylor b, Julio C. Delgado a. a University of Utah, Salt Lake City, UT, United States; b University of Utah Health Care, Salt Lake City, UT, United States. Aim: Pronase treatment of lymphocytes has been used to improve the specificity and sensitivity of the flow cytometric crossmatch for almost 20 years, due to its ability to remove Fc receptors from B cells and reduce non-specific background. Due to controversy in the published data, and significant variations in laboratory practices about the use of pronase, our goal was to determine the concentration-dependent effect of pronase on lymphocyte surface molecules, such as CD32 (FccRIIB), HLA class I and II, CD3, CD19, and CD20, and to provide a standardized procedure to improve the performance of flow cytometric crossmatch assays. Methods: Lymphocytes from 8 donors were isolated from blood, lymph nodes (LN) or spleen, and were digested using different concentrations of pronase ranging from 0.3 through 2 mg/ml. Surface CD32, CD3, CD19, CD20, HLA class I and class II were monitored by antibody staining using multi-color flow cytometry. Results: Digestion with pronase at 0.6 mg/ml concentration or higher decreased CD32 expression by 80% on peripheral blood B cells and 85–90% on LN and splenic B cells. Pronase treatment did not reduce expression of HLA class I, however, it increased W6/32 antibody staining by 20–40% on B cells, and 150–200% on T cells. Similarly, pronase did not decrease HLA class II expression, but increased Tu39 antibody staining by 10–30% on lymph node and blood B cells. Binding of the CD3 antibody on pronase-treated T cells was increased by 2– 2.5 fold, compared to untreated cells. There was no significant effect on CD19 expression up to 1 mg/ml, but 45–55% decrease was observed at 2 mg/ml. Pronase reduced CD20 expression in a concentration-dependent manner by up to 98–99% at 2 mg/ml. Conclusions: Pronase treatment increases specificity of the crossmatch assay since it reduces CD32 expression on B cells. Our data show that concentrations higher than 0.6 mg/ml do not decrease CD32 expression any further. Pronase treatment is safe since it does not decrease HLA class I or class II expression, but rather increases pan-HLA antibody binding, likely due to receptor ‘‘unmasking’’, thus increasing the sensitivity of the crossmatch assay. Finally, increasing pronase concentration to 2 mg/ml eliminates B cell CD20 expression, a procedure that can help to eliminate interferences by rituximab-containing sera used for crossmatch.
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DERIVING HLA GENOTYPING FROM WHOLE GENOME SEQUENCING DATA USING OMIXON HLA TWIN(TM) IN G3’S GLOBAL CLINICAL STUDY. Szilveszter Juhos a, Tünde Vágó a, Deborah Ferriola b, Jamie Duke b, Szilárd Vörös c, Bradley O. Brown c, Idean Marvasty c, Tim Hague a, Dimitri Monos b. a Omixon Ltd, Budapest, Hungary; b Children’s Hospital of Philadelphia, Philadelphia, PA, United States; c Global Genomics Group, Richmond, VA, United States. Aim: Identify the degree of concordance and the ambiguities when DNA samples are analyzed using two HLA genotyping systems; Whole Genome Sequencing (WGS) data analyzed by Omixon HLA Target software and genotyping by the Holotype HLA typing system. Methods: WGS data were generated of 235 human samples from G3’s GLOBAL study (NCT01738828) using paired 100 bps long HiSeq Illumina 30 coverage and analyzed by the Omixon HLA Target software. Genotyping was also performed for the same samples using the Holotype HLA typing system, whereby the generated libraries were sequenced on the Illumina MiSeq using the 250 bps paired-end sequencing protocol. Data were analyzed by the Omixon HLA Twin software. Concordance at the three-field level between the two approaches was assessed for each of the HLA-A, B, C, DQB1 and DRB1 loci. Ambiguities were also determined for each of the typing methods. Results: A pairwise comparison between the two NGS-based methods shows that in average they were 97.2% concordant at the three-fields level (HLA-A: 450/470 95.7%, HLA-B: 464/470 98.7%, HLA-C: 464/470 98.7%, HLA-DQB1: 438/470 93.2%, HLA-DRB1 467/468 99.8%). Most of the discrepancies were due to allele dropout in the WGS sample or systematic errors of the software. The 41 non-systematic mistypings were due to either insufficient coverage of the WGS data or algorithmic problems. The Holotype HLA system did not generate any ambiguities for the HLA-A, -B, -C and HLA-DQB1 loci at the three-fields typing level. However, as intron 1 and exon 1 of DRB1 are not targeted, 1.4% of ambiguities were generated for the DRB1 locus.The WGS typing considers only the exons of the reference database, leading to phase ambiguities and overall ambigous typings of 9.8%, 4.3%, 7.7%, 8.3% and 3.4% for HLA-A, B, C, DQB1 and DRB1 respectively. Conclusions: HLA genotyping using WGS data derived through next-generation sequencing and analyzed by the Omixon HLA Twin software is possible at 97.2% accuracy level. Future improvements of the software addressing ambiguities and remaining systematic errors can make HLA typing from WGS data more reliable. S. Juhos: Employee; Company/Organization; Omixon Ltd. T. Vágó: Employee; Company/Organization; Omixon Ltd. D. Ferriola: Employee; Company/Organization; Children’s Hospital of Philadelphia. J. Duke: Employee; Company/Organization; Children’s Hospital of Philadelphia. S. Vörös: Employee; Company/Organization; Global Genomics Group. B.O. Brown: Employee; Company/Organization; Global Genomics Group. I. Marvasty: Employee; Company/Organization; Global Genomics Group. T. Hague: Employee; Company/Organization; Omixon Ltd. D. Monos: Employee; Company/Organization; Children’s Hospital of Philadelphia.
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THE EFFECT OF PRONASE ON LYMPHOCYTE SURFACE MARKERS AND IMPLICATIONS FOR FLOW CYTOMETRIC CROSSMATCH. Eszter Lazar-Molnar a, Laura Spruit b, Ann Pole b, Kevin Williams b, Michelle R. Taylor b, Julio C. Delgado a. a University of Utah, Salt Lake City, UT, United States; b University of Utah Health Care, Salt Lake City, UT, United States. Aim: Pronase treatment of lymphocytes has been used to improve the specificity and sensitivity of the flow cytometric crossmatch for almost 20 years, due to its ability to remove Fc receptors from B cells and reduce non-specific background. Due to controversy in the published data, and significant variations in laboratory practices about the use of pronase, our goal was to determine the concentration-dependent effect of pronase on lymphocyte surface molecules, such as CD32 (FccRIIB), HLA class I and II, CD3, CD19, and CD20, and to provide a standardized procedure to improve the performance of flow cytometric crossmatch assays. Methods: Lymphocytes from 8 donors were isolated from blood, lymph nodes (LN) or spleen, and were digested using different concentrations of pronase ranging from 0.3 through 2 mg/ml. Surface CD32, CD3, CD19, CD20, HLA class I and class II were monitored by antibody staining using multi-color flow cytometry. Results: Digestion with pronase at 0.6 mg/ml concentration or higher decreased CD32 expression by 80% on peripheral blood B cells and 85–90% on LN and splenic B cells. Pronase treatment did not reduce expression of HLA class I, however, it increased W6/32 antibody staining by 20–40% on B cells, and 150–200% on T cells. Similarly, pronase did not decrease HLA class II expression, but increased Tu39 antibody staining by 10–30% on lymph node and blood B cells. Binding of the CD3 antibody on pronase-treated T cells was increased by 2– 2.5 fold, compared to untreated cells. There was no significant effect on CD19 expression up to 1 mg/ml, but 45–55% decrease was observed at 2 mg/ml. Pronase reduced CD20 expression in a concentration-dependent manner by up to 98–99% at 2 mg/ml. Conclusions: Pronase treatment increases specificity of the crossmatch assay since it reduces CD32 expression on B cells. Our data show that concentrations higher than 0.6 mg/ml do not decrease CD32 expression any further. Pronase treatment is safe since it does not decrease HLA class I or class II expression, but rather increases pan-HLA antibody binding, likely due to receptor ‘‘unmasking’’, thus increasing the sensitivity of the crossmatch assay. Finally, increasing pronase concentration to 2 mg/ml eliminates B cell CD20 expression, a procedure that can help to eliminate interferences by rituximab-containing sera used for crossmatch.
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DERIVING HLA GENOTYPING FROM WHOLE GENOME SEQUENCING DATA USING OMIXON HLA TWIN(TM) IN G3’S GLOBAL CLINICAL STUDY. Szilveszter Juhos a, Tünde Vágó a, Deborah Ferriola b, Jamie Duke b, Szilárd Vörös c, Bradley O. Brown c, Idean Marvasty c, Tim Hague a, Dimitri Monos b. a Omixon Ltd, Budapest, Hungary; b Children’s Hospital of Philadelphia, Philadelphia, PA, United States; c Global Genomics Group, Richmond, VA, United States. Aim: Identify the degree of concordance and the ambiguities when DNA samples are analyzed using two HLA genotyping systems; Whole Genome Sequencing (WGS) data analyzed by Omixon HLA Target software and genotyping by the Holotype HLA typing system. Methods: WGS data were generated of 235 human samples from G3’s GLOBAL study (NCT01738828) using paired 100 bps long HiSeq Illumina 30 coverage and analyzed by the Omixon HLA Target software. Genotyping was also performed for the same samples using the Holotype HLA typing system, whereby the generated libraries were sequenced on the Illumina MiSeq using the 250 bps paired-end sequencing protocol. Data were analyzed by the Omixon HLA Twin software. Concordance at the three-field level between the two approaches was assessed for each of the HLA-A, B, C, DQB1 and DRB1 loci. Ambiguities were also determined for each of the typing methods. Results: A pairwise comparison between the two NGS-based methods shows that in average they were 97.2% concordant at the three-fields level (HLA-A: 450/470 95.7%, HLA-B: 464/470 98.7%, HLA-C: 464/470 98.7%, HLA-DQB1: 438/470 93.2%, HLA-DRB1 467/468 99.8%). Most of the discrepancies were due to allele dropout in the WGS sample or systematic errors of the software. The 41 non-systematic mistypings were due to either insufficient coverage of the WGS data or algorithmic problems. The Holotype HLA system did not generate any ambiguities for the HLA-A, -B, -C and HLA-DQB1 loci at the three-fields typing level. However, as intron 1 and exon 1 of DRB1 are not targeted, 1.4% of ambiguities were generated for the DRB1 locus.The WGS typing considers only the exons of the reference database, leading to phase ambiguities and overall ambigous typings of 9.8%, 4.3%, 7.7%, 8.3% and 3.4% for HLA-A, B, C, DQB1 and DRB1 respectively. Conclusions: HLA genotyping using WGS data derived through next-generation sequencing and analyzed by the Omixon HLA Twin software is possible at 97.2% accuracy level. Future improvements of the software addressing ambiguities and remaining systematic errors can make HLA typing from WGS data more reliable. S. Juhos: Employee; Company/Organization; Omixon Ltd. T. Vágó: Employee; Company/Organization; Omixon Ltd. D. Ferriola: Employee; Company/Organization; Children’s Hospital of Philadelphia. J. Duke: Employee; Company/Organization; Children’s Hospital of Philadelphia. S. Vörös: Employee; Company/Organization; Global Genomics Group. B.O. Brown: Employee; Company/Organization; Global Genomics Group. I. Marvasty: Employee; Company/Organization; Global Genomics Group. T. Hague: Employee; Company/Organization; Omixon Ltd. D. Monos: Employee; Company/Organization; Children’s Hospital of Philadelphia.
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