Marcelo Fernández-Viña

Transcription

1 Marcelo Fernández-Viña Histocompatibility, Immunogenetics and Disease Profiling Laboratory Stanford Blood Center HLA: Quo Vadis (Where are going to)? Development and Implementation of Next Generation Sequencing (NGS) Based Methods for Tissue Typing Marcelo A. Fernández Viña, Ph.D. Stanford University 2 Images from: / 3 1

2 Rapid pace of technology development for DNA sequence analysis 4 Image from: Log of P Values of HLA Loci and SNP TDT in Psoriasis Families Hum Genet Dec;118(3-4): Helms C, Saccone NL, Cao L, Daw JA, Cao K, Hsu TM, Taillon-Miller P, Duan S, Gordon D, Pierce B, Ott J, Rice J, Fernandez-Vina MA, Kwok PY, Menter A, Bowcock AM. 5 NGS at HLA lab Stanford Blood Center Roche Stanford Genome Research Center Life Technologies Illumina 2015 Sirona Immucor GenDx Pac Bio (2013) Omixon (2015) One Lambda (2015) 6 2

3 Roche 454 HLA study Designed to evaluate feasibility/reproducibility of using the Roche 454 sequencing platform to perform HLA typing in the clinical laboratory Two independent pilot studies had previously demonstrated the power of this approach Eight participating laboratories 20 double-blinded samples with difficult SBT results (rare alleles, etc) submitted for analysis Laboratories trained to perform experiment independently Holcomb CL, Hoglund, B, Anderson, MW, et al., Tissue Antigens2011, 77:

4 Extract DNA Genomic Purify amplicons Quantify amplicons Dilute amplicons Pool amplicons Emulsion Purify sequencing templates Sequence Analyze using Conexio Generate final report Extract DNA using Access Array Emulsion Purify sequencing templates Sequence Analyze using Conexio Generate final report Automate using robotics and emulsion purification device Barcode primers and up to 48 individual samples Tagged sequencespecific primers (up to 48 individual targets) Intron Exon Intron Up to 48 barcoded amplicons from each sample (up to 2,304 amplicons) 10 HLA typing using high throughput sequencing technologies. Exon-wise amplification of few exons. Whole-gene amplification. 11 Disclosure Alpha and Beta Studies Sirona Genomics and Immucor Reagents, Equipment, and Software supplied by Manufacturers *Thermal cyclers, Biomek 4000, and Illumina MiSeq 4

5 Commercial kits available for NGS HLA typing Illumina Sequencer: Immucor/Sirona - MIA FORA NGS HLA Illumina Trusight HLA Omixon Holotype HLA NGS Ion Torrent: One Lambda/ Thermo Fisher NXType NGS Solution Illumina/Ion Torrent/ Pacific Biosiences GenDx - NGSgo One Lambda Amplification Strategy HLA-DQA1 Exon 1 Exon 2Exon 3Exon 4Exon 5 7 kb HLA-DPA1 Exon 1 Exon 2Exon 3Exon 4 Exon 5 ~10 kb Omixon Amplification Strategy HLA-A HLA-B ~3 kb ~3 kb HLA-C 1 DPB ~3 kb 2 3 DQA1 ~4.5 kb ~4.5 kb 2 3 DQB1 2 3 ~4.7 kb ~5.8 kb DRB1 2 3 ~4.3 kb 5

6 Long Range Amplification , 5.6 Single condition used for all loci Optimized extensively to preserve allele balance and prevent allele dropout NGS HLA Integrated Analysis Packages Immucor MIA FORA Ion Torrent HLA Plug In Illumina Conexio Omixon HLA Twin GENDX NGSengine Stanford Blood Center NGS-HLA Validation Study Evaluated 4 NGS methods and software Illumina Conexio Sirona Immucor Omixon Ion torrent PGM Compared previously typed samples (n=128) at the 2- field resolution with NGS genotypes generated by different NGS methods and software 6

7 Identical REF: HLA-A*01:01 NGS: HLA-A*01:01:01:01 Stanford Blood Center NGS-HLA Validation Study: Concordant vs Dis-concordant alleles Ambiguous Ref vs Unambiguous NGS REF: HLA-DRB3*02:02/HLA-DRB3*02:28/HLA-DRB3*02:29N NGS: HLA-DRB3*02:02:01:02 Concordant Unambiguous Ref vs Ambiguous NGS REF: HLA-B*46:01 NGS: HLA-B*46:01:01/HLA-B*46:18 Ambiguous Ref vs Ambiguous NGS HLA-DPB1*13:01/HLA-DPB1*107:01 HLA-DPB1*13:01:01/HLA-DPB1*107:01 REF: HLA-A*23:01 NGS: HLA-A*23:17 Dis-Concordant System 1 System 2 7

8 System 3 System 4 Sirona Workflow Day 1 (~5 hours) Samples 1-8 Samples 9-16 Samples Long Range Quantification, Balancing and Pooling amplicons Day 2 3 Library prep Enzymatic Fragmentation, End repair, A tailing Pool Index adaptor ligation Pooling, Size Selection, q Day 4 (24 hours) Sequencing Day 5 (4 hours) HLA genotype Assignment 2 x 150 bp paired end reads 8

9 NGS-HLA typing requirements for the Stanford Blood Center Unambiguous phased genotypes Automated methods Accurate unedited genotype calls Easy to perform Easy intuitive software Cost effective No reflexive testing Potential for completing tests in3-4 days Implementation of NGS in the Clinical laboratory Reasons for Implementation and Time Table Fully Automated Quality of Results Software analysis; comprehensive examination of calls Inputs and outputs easily coordinated with existing LIS (mtilda) Least number of genotype ambiguities) Six Licensed Technologists fully trained: 2-3 weeks for training and validation 2-4 runs of 22 samples per week TAT: 5-10 days Working in 3-4 days for TAT (now being validated) Server in Blood Center F Drive XML file is processed HLA types are extracted mtilda Sample File generated for Pre- Robot XML file output Sample Sheet Linux server (Research) Linux server (Clinical) M03821 (Windows7)M02601 (Windows 7)M02390 (Windows 7 Data Storage server (Linux) hidpl-data02 (~110 TB, mirrored) Data Storage server (Linux) hidpl-data01 (M Drive) (~110 TB) Backup server (Linux) hidpl-archive (~180 TB) Data Flow 9

10 Receive sample & enter data into mtilda DNA set up in plate (epmotion) set up (NX) DNA extraction (QIAsymphony) Enter DNA Generates Conc. data Work list mtilda Size fractionation (BluePippin) Sequencing library Library preparation (4000) Sequencing Data DNA quantitation Data Storage server (Linux) hidpl-data01 (M Drive) (~110 TB) Quantitation Mia For a NGS Work Flow Software Features (two) Differences in Logic Multiple Algorithms Central Reads and pair ends Ease to find and characterize novel alleles Typing of less well characterized genes (DRB3/4/5, DPB1) Full characterization of rare or novel alleles Implementation of NGS in the Clinical laboratory Important Considerations Development of Laboratory Procedures (Alpha and Beta testing were useful) Adopting Quality Metrics Development of Automation procedures Validation methods of Instruments and Robots Develop schedule for Preventive Maintenance Develop Technologists Training Plans: Test Performance Analysis Software outputs Electronic Input to LIS Considerations of what to report to clinical services: Two field (four digit) for HSC Serologic Equivalent for Solid Organ Transplantation with molecular results for DQA1, DP loci Variable for Disease associations, vaccines amd clinical trials (e.g. A*02:01 positive subjects ) Inputs and outputs easily coordinated with existing LIS (mtilda) Comments: Once you implement NGS, no one in the lab wants to do High Resolution by Sanger Sequencing or SSP Once you implement automation, no one in the lab wants to do the procedure manually. Training for manual tests is lost rapidly underscoring the need of equipment redundancy and effective preventive maintenance 10