NEXT GENERATION SEQUENCING. R. Piazza (MD, PhD) Dept. of Medicine and Surgery, University of Milano-Bicocca
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1 NEXT GENERATION SEQUENCING R. Piazza (MD, PhD) Dept. of Medicine and Surgery, University of Milano-Bicocca
2 SANGER SEQUENCING Capillary Electrophoresis DNA
3 NEXT GENERATION SEQUENCING SOLEXA-ILLUMINA TECHNOLOGY Flowcell
4 NEXT-GEN SEQUENCING DNA LIBRARY Genomic DNA Single-Read Paired-End ~100bp ~100bp
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6 NEXT GENERATION SEQUENCING Clusters At the end of the amplification step millions of double-stranded DNA clusters are generated in each lane of the flowcell
7 NEXT GENERATION SEQUENCING All 4 nucleotides are added together Sequencing Primer Blocked, fluorescent nucleotides
8 NEXT GENERATION SEQUENCING IMAGE ACQUISITION 3 BLOCK REMOVAL FLUOROPHORE REMOVAL
9 HIGH-THROUGHPUT SEQUENCING The sequence is read in each cluster through multiple cycles of nucleotide incorporation
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11 NEXT GENERATION SEQUENCING
12 HiSeq Lane/Flowcell 2 FLOWCELLS 8 Lane/Flowcell 250 * 10^6 Cluster/Lane 250 * 10^6 Cluster/Lane 125bp 125bp 2 x 125bp/Cluster (Pair-end) Throughput = 2 * 8 * 250 * 10^6 * 2 * 125bp = bp!! = 1 Tb (Terabase)
13 SANGER SEQ vs. NGS THROUGHPUT COST Allele #1 Allele #2 C A G C G A C A G C A G C A T T G G G A C C A G C G A C A G C G G C A T T G G G A C Coverage = 5 NGS Read #5 NGS Read #4 NGS Read #3 NGS Read #2 NGS Read #1 Allele #1 Allele #2 C A G C G A C A G C G G C A T T G G G A C C A G C G A C A G C A G C A T T G G G A C C A G C G A C A G C A G C A T T G G G A C C A G C G A C A G C G G C A T T G G G A C C A G C G A C A G C G G C A T T G G G A C C A G C G A C A G C A G C A T T G G G A C C A G C G A C A G C G G C A T T G G G A C
14 HIGH-THROUGHPUT SEQUENCING: APPLICATIONS DNA RNA GENOMIC DNA SEQUENCING RESEQUENCING DE NOVO SEQUENCING WHOLE-EXOME SEQUENCING ChIP-Seq DEEP SEQUENCING METHYL-SEQ mrna SEQUENCING (RNA-Seq) TRANSCRIPTOME SEQUENCING (RNA-SEQ) TAG SEQUENCING (DITAG) MICRO-RNA STUDIES
15 WHOLE-GENOME, WHOLE-EXOME AND ULTRADEEP SEQUENCING COVERAGE COVERAGE WHOLE-GENOME ULTRADEEP-SEQ
16 WHOLE-GENOME SEQUENCING: WHY? BY USING WHOLE-GENOME SEQUENCING WE ARE ABLE TO SEQUENCE AN ENTIRE GENOME, LEADING TO AN ALMOST UNBIASED ANALYSIS OF ALL THE VARIANTS THAT ARE PRESENT IN THAT SAMPLE HOWEVER, SEQUENCING IS STILL VERY EXPENSIVE, ANALYSIS COMPLEX AND FINAL COVERAGE IS USUALLY LIMITED, WHICH MEANS THAT THERE COULD BE SEQUENCING HOLES (REGIONS WITH POOR OR NO COVERAGE)
17 ULTRADEEP SEQUENCING: WHY? SUBCLONAL MUTATIONS M M ABL kinase domain
18 SONICATION M M
19 WHOLE-EXOME SEQUENCING
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22 SINGLE NUCLEOTIDE POLYMORPHISM VARIANT CALLING MUTATION OR SNP? SOMATIC VARIANT GACAGACTACAACAGCACTTCTGACCAAGC GGGACAGACAACAACAGAACTTCTGACCAA TGGGACAGACTACAACAGCACTTCTGACCA ATTGGGACAGACAACAACAGAACTTCTGAC GCATTGGGACAGACTACAACAGCACTTCTGA GGCATTGGGACAGACTACAACAGCACTTCTG GGCATTGGGACAGACAACAACAGAACTTCTG CGGCATTGGGACAGACAACAACAGAACTTCT..CGGCATTGGGACAGACAACAACAGCACTTCTGACCAAGCGGAGAAGAGCT.. CASE SAMPLE GACAGACAACAACAGCACTTCTGACCAAGC GGGACAGACAACAACAGCACTTCTGACCAA TGGGACAGACTACAACAGCACTTCTGACCA ATTGGGACAGACAACAACAGCACTTCTGAC GCATTGGGACAGACTACAACAGCACTTCTGA GGCATTGGGACAGACAACAACAGCACTTCTG CONTROL SAMPLE GGCATTGGGACAGACAACAACAGCACTTCTG CGGCATTGGGACAGACTACAACAGCACTTCT..CGGCATTGGGACAGACAACAACAGCACTTCTGACCAAGCGGAGAAGAGCT..
23
24 SOMATIC VARIANT: DRIVER OR PASSENGER?
25 CASE CONTROL DELETIONS? AMPLIFICATIONS?
26 CASE CONTROL LOSS OF HETEROZYGOSITY ALLELIC IMBALANCE A A T T A A T
27 WHOLE-EXOME SEQUENCING GOES DIGITAL: CEQer COMPARATIVE EXONIC QUANTIFICATION ANALYZER Piazza R. et al., PLoS One Oct 4;8(10):e74825
28 Statistical module Wilcoxon Signed-Rank test Test statistic W W N r ( case ) ( control ) sgn x x R i i i i 1 As sample size increases (Nr > 10) the Z-Score converges to a Gaussian distribution! Estimating the error function of the normal distribution of W.. Wilcoxon Signed-Rank test erf ( x )..using the Abramowitz and Stegun approximation equation a 1 t a 2 t 2 a 3 t 3 a 4 t 4 a 5 t 5 e x 2
29 SOMATIC VARIANTS DETECTION WHOLE-GENOME WHOLE-EXOME ULTRADEEP SEQ WHICH ONE?
30 CASE STUDY #1 PROF. X ASKS YOU TO DEVELOP A PROTOCOL IN ORDER TO IDENTIFY THE SOMATIC MUTATIONS RESPONSIBLE FOR A RARE TYPE OF CANCER TOTAL NUMBER OF MATCHED (TUMOR + GERMLINE) CASES AVAILABLE: 9 TOTAL NUMBER OF TUMOR-ONLY CASES (gdna) AVAILABLE: 30 DATA AVAILABLE IN LITERATURE: NONE
31 CASE STUDY #2 PROF. X ASKS YOU TO DEVELOP A PROTOCOL IN ORDER TO IDENTIFY THE SOMATIC MUTATIONS RESPONSIBLE FOR A COMMON TYPE OF CANCER TOTAL NUMBER OF MATCHED (TUMOR + GERMLINE) CASES AVAILABLE: 35 TOTAL NUMBER OF TUMOR-ONLY CASES (gdna) AVAILABLE: 160 DATA AVAILABLE IN LITERATURE: EXTENSIVE KNOWLEDGE OF A SET OF 6 SOMATIC VARIANTS RESPONSIBLE FOR 80-90%OF CASES
32 CASE STUDY #3 PROF. X ASKS YOU TO DEVELOP A PROTOCOL TO STUDY THE PRESENCE OF MINIMAL RESIDUAL DISEASE IN A LEUKEMIC PATIENT WHO UNDERWENT ALLOGENEIC BONE MARROW TRANSPLANTATION TOTAL NUMBER OF CASES AVAILABLE: 1 DATA AVAILABLE: SOMATIC MUTATIONS RESPONSIBLE FOR THE ONSET OF THE DISEASE ARE KNOWN
33 RNA-Seq LOW EXPRESSION HIGH EXPRESSION RNA-SEQ READ EXON EXON
34 TRASLOCAZIONI ONCOGENICHE
35 DETECTION OF ONCOGENIC FUSIONS EXOME SEQUENCING? FRAGMENTATION?
36 RNA-Seq DRIVER FUSION TRANSCRIPTS IDENTIFICATION Junction reads Bridge reads 76bp 76bp Piazza R. et al., Nucleic Acids Res Sep;40(16):e123
37 XVI-XVII secolo: anatomia umana XIX secolo: microbiologia XX secolo: biochimica e biologia molecolare : rivoluzione genetica
38
39 SOMATIC VARIANTS Time 0 P P P * Time 2 * * P P * * * P * * P P * P ** * P P = PASSENGER VARIANTS * = DRIVER VARIANT * = DRIVER VARIANT P * Time 1 P *
40 DNA POLIMERASI
41 LIBRO: 300 PAGINE 1500 CARATTERI PER PAGINA CARATTERI DNA POLIMERASI DNA GENOMICO UMANO: DI CARATTERI (BASI)! L EQUIVALENTE DI LIBRI!! TURN-OVER CELLULARE: MILIARDI DI CELLULE AL GIORNO LE DNA POLIMERASI DEVONO LEGGERE E COPIARE IN MEDIA 600 MILIARDI DI MILIARDI DI BASI AL GIORNO: OLTRE 1 MILIONE DI MILIARDI DI LIBRI AL GIORNO
42
43 Piazza R. et al., Recurrent SETBP1 mutations in atypical chronic myeloid leukemia. Nat Genet Jan;45(1):18-24 Gambacorti-Passerini C. et al., Recurrent ETNK1 mutations in atypical chronic myeloid leukemia. Blood Jan 15;125(3):
44 SUBCLONAL EVOLUTION - METHYLCELLULOSE ASSAY Methylcellulose Medium CLONES/PATIENT MATCHED EXOME SEQUENCING ULTRADEEP SEQUENCING TARGETED AMPLIFICATION
45 PATIENT CMLPh-019 Somatic Variants ETNK1 p.n244s SETBP1 p.g870s ASXL1 p.r412* CBL p.c384y Coordinates Chr12: , A/G Chr18: , G/A Chr20: , C/T Chr11: , G/A
46 PATIENT CMLPh-013 Somatic Variants ETNK1 p.n244s SETBP1 g.g870s ASXL1 g.y586* NRAS g.g12d Coordinates Chr12: , A/G Chr18: , G/A Chr20: , C/G Chr1: , C/T
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