Computational methods for genomics-guided immunotherapy Sahar Al Seesi and Ion Măndoiu Computer Science & Engineering Department University of Connecticut.
Download ReportTranscript Computational methods for genomics-guided immunotherapy Sahar Al Seesi and Ion Măndoiu Computer Science & Engineering Department University of Connecticut.
Computational methods for genomics-guided immunotherapy Sahar Al Seesi and Ion Măndoiu Computer Science & Engineering Department University of Connecticut Class I endogenous antigen presentation Somatic rearrangement of T-cell receptor genes Potential TCR repertoire diversity: 1015 T-cell selection in thymus Estimated TCR repertoire diversity after selection: ~2x107 T-cell activation and proliferation T-cell activation and proliferation T-cell activation and proliferation The immune system and cancer Cutting the brakes: PD1 and CTLA-4 blockade Stepping on the gas: vaccination with neoepitopes Combined approach Ton N. Schumacher, and Robert D. Schreiber Science 2015;348:69-74 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Sequencing QC and Mapping Tumor DNA Calling SNVs Epitope Prediction Normal DNA Clonality Analysis Vaccine Design Tumor RNA Whole Genome or Nextera Rapid Library prep Capture Exome Whole Transcriptome Library prep Illumina HiSeq Sequencing TCR Sequencing Sequencing Tumor DNA QC and Mapping Calling SNVs Normal DNA Whole Genome or Exome Library prep AmpliSeq Ion Proton Sequencing Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Tumor RNA Whole Transcriptome Library prep Ion PGM Sequencing QC and Mapping Sequencing Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing ION-Torrent Proton Runs: read statistics # of reads # of bases Mean of reads lengths std of reads lengths PB 25,031,340 3,272,787,408 130.74 66.88 Melanoma 1T Patient 2T 19,252,932 2,589,624,915 134.5 68.67 28,400,728 4,147,914,801 146.04 66.91 3T 26,039,006 3,800,446,471 145.95 67.02 Normal 20,726,352 3,353,732,704 161.81 63.35 20,726,352 3,360,840,809 162.15 63.14 20,726,352 3,367,827,877 162.49 62.92 Synthetic TumorAF10 Tumor TumorAF20 Sequencing QC and Mapping Calling SNVs Epitope Prediction Tumor Exome Reads Human reference Normal Exome Reads Human reference Tumor RNA-Seq Reads Clonality Analysis Vaccine Design TCR Sequencing Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis • fastq QC Tools Tools to analyze and preprocess fastq files – FASTX (http://hannonlab.cshl.edu/fastx_toolkit/) • Charts quality statistics • Filters sequences based on quality • Trims sequences based on quality • Collapses identical sequences into a single sequence Vaccine Design TCR Sequencing Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing • fastq QC Tools Tools to analyze and preprocess fastq files – PRINSEQ (http://prinseq.sourceforge.net/) • Generates read length and quality statistics • Filters reads based on length, quality, GC content and other criteria • Trims reads based on length/position or quality scores Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design • Mapping decisions – What is the best mapper for your data? TCR Sequencing Tumor Exome Reads Human reference – End-to-end unspliced alignments vs. spliced or local alignments Normal Exome Reads Human reference – Unique vs. non-unique alignments Tumor RNA-Seq Reads Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing ION-Torrent Proton read mapping comparison Bowtie2 Melanoma Patient Synthetic Tumor TMAP Segemehl % of aligned bases Aligned reads length mean % of aligned bases Aligned reads length mean % of aligned bases Aligned reads 2 length mean2 PB 89% 138.8 100% 130.7 99% 135.4 1T 90% 143.3 100% 134.5 99% 140.4 2T 90% 153.3 100% 146 99% 150.3 3T 91% 153.4 100% 146 99% 150.4 Normal 89% 172.89 100% 161.81 98% 167.66 Tumor_AF10 89% 173.03 100% 162.15 99% 167.87 Tumor_AF20 89% 173.17 100% 162.49 99% 168.08 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Tumor Exome Reads * Human reference * * * * * * * * * Normal Exome Reads Vaccine Design TCR Sequencing Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design Tumor Exome Reads Somatic Variant Callers • • • • • Mutect (Broad Inst.) VarScan2 (Wash. U.) SomaticSniper (Wash. U) Strelka (Illumina) SNVQ w/ subtraction (UConn) TCR Sequencing * Human reference * * * * * * * * * Normal Exome Reads QC and Mapping Sequencing Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Coverage distribution of exome vs. SNV calls 40% 35% % of variants called 30% 25% CCDS HCC1954BL CCDS HCC1954 Mutect 20% SNVQ Sniper 15% Strelka Varscan2 10% 5% 0% 0 20 40 60 80 100 Read depth 120 140 160 180 200 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Comparing Somatic Variant Callers • Synthetic Tumors – ION Torrent Proton exome sequencing of two 1K Genomes individual (mutations known) – Downloaded from the public Torrent server – Both exomes were sequenced on the same Proton chip – Subset of the NA19240 sample was used as the normal sample – Mixtures of NA19240 and NA12878 samples were used as the tumor samples – Reads were mixed in different proportions to simulate allelic fractions, 0.1, 0.2, 0.3, 0.4 and 0.5. Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Comparing Somatic Variant Callers TP mixture_AF_10 FP FN TP mixture_AF_20 FP FN TP mixture_AF_30 FP FN TP mixture_AF_40 FP FN TP mixture_AF_50 FP FN SNVQ Strelka Mutect Bowtie2 TMAP Semegehl Bowtie2 TMAP Semegehl Bowtie2 TMAP Semegehl Bowtie2 464 476 470 1,573 1,738 402 440 360 22 1,232 341 295 331 218 85 80 80 3,050 11,823 11,811 11,817 10,714 10,549 11,885 11,847 11,927 12,265 2,506 2,605 2,668 4,220 4,475 1,094 1,183 994 35 1,224 341 286 302 191 55 54 54 3,178 9,781 9,682 9,619 8,067 7,812 11,193 11,104 11,293 12,252 4,608 4,842 4,910 5,776 6,089 1,526 1,637 1,379 61 1,219 364 307 317 199 43 39 46 3,235 7,679 7,445 7,377 6,511 6,198 10,761 10,650 10,908 12,226 6,138 6,404 6,465 6,660 7,005 1,797 1,907 1,614 89 1,259 378 333 362 241 40 40 46 3,338 6,149 5,883 5,822 5,627 5,282 10,490 10,380 10,673 12,198 7,018 7,309 7,359 7,232 7,592 1,937 2,037 1,732 163 1,322 394 357 388 286 41 49 49 3,375 5,269 4,978 4,928 5,055 4,695 10,350 10,250 10,555 12,124 Sniper Varscan2 TMAP Semegehl Bowtie2 TMAP Semegehl 10 17 92 91 7 1,469 1,344 1,971 1,056 20 12,277 12,270 12,195 12,196 12,280 15 20 797 836 837 1,548 1,422 1,978 1,015 447 12,272 12,267 11,490 11,451 11,450 31 29 1,922 1,994 1,994 1,618 1,471 1,952 1,013 476 12,256 12,258 10,365 10,293 10,293 48 44 3,198 3,298 3,312 1,661 1,528 2,018 1,071 505 12,239 12,243 9,089 8,989 8,975 104 41 4,284 4,466 4,489 1,763 558 2,162 1,161 590 12,183 12,246 8,003 7,821 7,798 QC and Mapping Sequencing Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Comparing Somatic Variant Callers 120 100 80 PPV Strelka SNVQ 60 Mutect Sniper 40 Varscan2 20 0 0 10 20 30 40 Sensitivity 50 60 70 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing The ICGC-TCGA DREAM Somatic Mutation Calling Challenge • Initial Goal: Find the Best WGS Analysis Methods • Challenge 1 Data: 10 Real Tumor/Normal pairs – 5 from pancreatic tumors and 5 from prostate tumors – Sequenced to ~50x/30x • Up to 10K candidates will be validated • re-sequencing to ~300x coverage using AmpliSeq primers on an IonTorrent Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing • Criteria for selecting candidate epitopes 1) Gene harboring the SNV must be expressed (FPKM estimation) • IsoEM (Nicolae et. al., Algorithms for Molecular Biology, 2011) http://dna.engr.uconn.edu/?page_id=105 • RSEM (Li et. al., BMC Bioinformatics, 2011) http://deweylab.biostat.wisc.edu/rsem/ Not Expressed X Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing • Criteria for selecting candidate epitopes 1) Gene harboring the SNV must be expressed 2) Peptide will be generated inside the cell upon protein being cleaved by the proteasome 3) Peptide will bind to an MHC molecule that will chaperon it to the cell surface • NetChop Predicts cleavage sites of the human proteasome http://www.cbs.dtu.dk/services/NetChop/ • SYFPEITHI Predicts MHC I, MHC II binding http://www.syfpeithi.de/ • NETMHC Predicts MHC I binding http://www.cbs.dtu.dk/services/NetMHC/ • NetCTL Combined cleavage and MHC biding predictions http://www.cbs.dtu.dk/services/NetCTL/ Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Candidate neo-epitopes statistics for two mouse cell lines Duan et. al., JEM 2014 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design Epi-Seq pipeline for neo-epitope prediction on local Galaxy server TCR Sequencing QC and Mapping Sequencing Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing • Rational vaccine design requires info on the clonal structure of the tumor – Not all cells harbor all candidate epitopes • Approaches to clonality analysis 1) Computational inference from sequencing depth • SNV allelic fractions only 2) Targeted amplicon sequencing of selected mutations at single cell level • More noisy data, potentially biased by capture protocols Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design Cell capture & pre-amp TCR Sequencing Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design PCR on Access Array TCR Sequencing Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design PCR on Access Array TCR Sequencing QC and Mapping Sequencing Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Captured cells in pilot run 1 2 3 4 5 6 7 8 9 10 11 12 A 1_C03 1_C02 1_C01 1_C49 1_C50 1_C51 1_C06 3_C05 1_C04 1_C52 1_C53 1_C54 B 2_C09 1_C08 1_C07 1_C55 1_C56 1_C57 1_C12 1_C11 1_C10 1_C58 1_C59 1_C60 C 1_C15 2_C14 2_C13 1_C61 1_C62 1_C63 1_C18 2_C17 1_C16 1_C64 1_C65 1_C66 D 1_C21 2_C20 1_C19 1_C67 1_C68 1_C69 2_C24 2_C23 4_C22 1_C70 1_C71 1_C72 E bulk 2_C26 1_C27 1_C75 1_C74 1_C73 0_C28 0_C29 0_C30 1_C78 1_C77 2_C76 F 1_C31 0_C32 1_C33 1_C81 1_C80 1_C79 0_C34 1_C35 0_C36 1_C84 0_C83 1_C82 G 0_C37 1_C38 0_C39 1_C87 1_C86 1_C85 1_C40 1_C41 1_C42 1_C90 1_C89 1_C88 H 1_C43 1_C44 1_C45 1_C93 1_C92 1_C91 1_C46 1_C47 1_C48 1_C96 1_C95 1_C94 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Analysis Pipeline Barcode list Fastx Barcode Splitter 96 fastq files: one per well pooled fastq file tmap mm9 BALBc genome Generate Referece List of SNV Locations 96 sam files: one per well fasta with +/300 bases around each SNV compute coverage 96x48 with total and fwd/rev variant coverage for each well/SNV Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Target Aligned Reads Unaligned Reverse Forward A1 A2 A3 A4 A5 A6 A7 A8_3 A9 A10 A11 A12 B1_2 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 C1 C2_2 C3_2 C4 C5 C6 C7 C8_2 C9 C10 C11 C12 D1 D2_2 D3 D4 D5 D6 D7_2 D8_2 D9_4 D10 D11 D12 180,000 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 - 180,000 160,000 Unaligned Reverse Forward 140,000 120,000 100,000 80,000 60,000 40,000 20,000 E1_bulk E2_2 E3 E4 E5 E6 E7_0 E8_0 E9_0 E10 E11 E12_2 F1 F2_0 F3 F4 F5 F6 F7_0 F8 F9_0 F10 F11_0 F12 G1_0 G2 G3_0 G4 G5 G6 G7 G8 G9 G10 G11 G12 H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 unmatched - chr10:20059582 chr10:57242972 chr11:101613424 chr11:35513793 chr11:6296832 chr11:78096592 chr1:191675045 chr12:111888098 chr12:114383174 chr12:17287415 chr12:73884772 chr13:42278980 chr13:55554430 chr15:25925906 chr1:57463849 chr15:81535979 chr15:85222168 chr16:17018488 chr17:27717774 chr17:46814692 chr17:53645675 chr1:78467742 chr19:24175196 chr19:8946517 chr2:104271486 chr2:165875637 chr2:166776842 chr2:91612587 chr3:146082306 chr4:115980807 chr4:62083556 chr5:28098614 chr6:17257915 chr6:29349553 chr6:71927367 chr7:133941041 chr7:30151735 chr7:31097709 chr8:26879828 chr9:110453160 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design 100000 50000 0 TCR Sequencing Per SNV Coverage 250000 200000 150000 alt_rev alt_fwd ref_rev ref_fwd QC and Mapping Sequencing Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing SNV Support Matrix Cells A1 chr10:20059582 chr10:57242972 chr11:101613424 chr11:35513793 chr11:6296832 chr11:78096592 chr1:191675045 chr12:111888098 chr12:114383174 chr12:17287415 chr12:73884772 chr13:42278980 chr13:55554430 chr15:25925906 chr1:57463849 chr15:81535979 SNVs chr15:85222168 Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse Alt in forward Alt in reverse chr16:17018488 chr17:27717774 chr17:46814692 chr17:53645675 chr1:78467742 chr19:24175196 chr19:8946517 chr2:104271486 chr2:165875637 chr2:166776842 chr2:91612587 chr3:146082306 chr4:115980807 chr4:62083556 chr5:28098614 chr6:17257915 chr6:29349553 chr6:71927367 chr7:133941041 chr7:30151735 chr7:31097709 chr8:26879828 chr9:110453160 - - - A2 00020 4 01000 0 2 3 01- - - A3 0 0 016 0 0 0 1001- - A4 1239 3189 0 3 3 5 1408 2390 1 1 0 1 1 1 - 1 2 0 1 1 0 0 0 - 1634 2536 0 1 0 0 0 0 00- 1060 2638 435 2760 945 212 7 5 3 3 - 0 0 A5 0 7 1 0 0 5 06462 638 01351 1284 132 5 1852 1974 001361 1440 0 2 0 0 151520 1005 1348 1581 1982 2282 1207 1405 0 0 0 0 12000 0 040 2 0 0 000 0 0 0 0 5 960 4 656 818 0 1 2 0 337 80 0 3 0 0 3 3 A6 0 1 652651 4018 291 471 A7 1892 1312 - 0 0 889 1342 0 2 0 0 708 1035 0 0 1 6 0 0 1757 2340 0 1 0 1 25 50 47 79 1367 1183 1388 1110 2340 1146 332 207 0 0 - 0 3 1 0 0 0 1 0 0 6 0 0 0 1 0 1 815 153 - 8 6 0 0 1175 1591 1 0 174 218 0 0 873 1102 606 1215 0 1 307 1212 1742 1590 0 0 0 1 627 1057 0 0 0 0 A8_3 1 2 103 144 338 545 262 442 338 394 164 539 1 0 170 238 301 394 0 0 906 1085 680 833 521 757 1225 1229 225 238 47 46 0 0 00183 109 1082 890 0 0 001177 3 2140 2188 0 0 1902 1629 9 14 0 0 1646 1748 18 8 1956 1433 0 0 84 124 0 0 00- A9 610 759 11 12 688 1216 258 382 1 0 173 1097 2 1 1201 1692 4 6 1124 717 142 180 510 742 0 0 22375 228 759 1292 0 0 15 19 708 1387 18 21 332 1227 0 0 10- 24 27 310 407 286 678 375 88 991 241 330 550 237 211 0 1 1534 2090 3 6 85 218 183 273 1 0 0 0 1227 2073 0 0 5 1 2 1 1121 1478 859 1063 0 1 0 4 14 63 0 1 A11 24 21 1 1 986 1245 520 638 719 678 0 0 1115 1377 22- 1119 1135 0 0 182 232 84 56 1342 1709 0 0 1 0 332 733 00682 803 - 0 0 605 732 2 3 0 0 0 0 0 0 0 0 1 0 541 8651 990 A10 10550 596 1 2 266 380 37 83 558 580 325 785 3 2 1 1 0 1 00966 940 0 0 974 1259 - 52 31 27 11 26 688 1376 1303 2 1 0 0 375 354 12 7 1285 1101 1293 502 1078 867 0 0 0 0 1 1 0 0 0 0 000 0 311 375 0 12 00- 108 152 451476 2225 675 854 0 0 342 58 001049 1583 0 0 1 1 A12 0 0 958 759 748 942 0 2 1 0 5 9 002 8 307 220 2 6 0 1 600 440 0 139 0011 13 0016 86 00- 3 2 449 251 0 0 B1_2 0 0 132 117 1345 1964 562 629 0 0 972 657 16 609 850 1050 659 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1 0 0 0 2 0 0 0 1 0 3 0 1 4 4 0 0 0 0 B6 79 98 2 0 3 1 0 2 3 1 773 725 424 1465 1153 1416 6 6 1739 1720 0 0 744 715 0 1 1944 2043 358 522 3 1 26 35 3 5 0 2 3 4 0 0 363 1000 0 0 0 0 2 2 856 903 5 2 0 413 001425 10 1708 2098 4 2 626 1137 1630 1691 477 918 911 159 0 0 1 6 011103 1178 B7 0 1 1345 604 2641 1971 21 16 0 0 1190 482 0 0 638 721 2939 1726 0 0 1166 355 1648 664 134 64 0 4 903 486 0 1 279 147 1903 989 2202 1027 581 512 0 0 351 580 0 2 0 0 0 0 1164 497 798 458 0 89 1628 2 0 0 0 0 2089 1313 0 3 1414 1248 1627 141 0 0 3033 1926 4 2 B8 2 0 529 668 285 519 632 1001 180 288 1 0 227 1142 786 1138 3 4 0 0 233 247 303 422 1508 1979 569 849 0 0 652 1019 0 0 251 344 1 1 178 421 0 1 0 0 281 489 178 282 1 0 3 2 0 148 1 0 0 0 422 536 0 6 201 521 426 547 181 431 1 0 1 0 6 16 220 183 308 369 B9 828 1093 618 826 666 1967 1 0 34 62 0 0 98 672 46 63 834 1245 689 1023 717 438 567 798 489 839 203 315 337 597 210 146 567 1241 0 1 1029 62 1 0 95 391 0 0 5 8 123 185 39 62 23 11 0 234 1 0 788 28 326 444 1 3 553 1818 640 960 158 398 25 28 756 87 11 22 29 25 784 1210 B10 B11 419 0461 0547 548 1472 1879 143 210 39 1 47 0 430 1 336 0 79 194 218 518 42 2 43 4 126 1 166 0 505 336 710 736 574 543 131 3381 135 3807 695 810 260 5 278 0 187 0 189 0 128 0 132 0 238 1680 496 2550 234 0 449 1 22 100 582 703 705 934 463 1 507 0 313 0 323 1 14020 455 515 103 1 0 0 767 0 1002 0 4 0 3 0 45 0 63 0 658 1709 0406 0 597 0 365 1 70 0 533 122 1 3 1 5 88 73 0 1 1 0 B12 1354 1170 20282 327 1094 490 222 0 1 3 1015 547 331 250 10630 370 118 190 001 1 2 1 0002397 429 164 159 000 1 128 165 068 374 401 10260 307 2401123 197 962 104 1262 266 05345 225 1378 1330 - C1 - C2_2 - C3_2 0 1 00- - 7 2 00- 0 0 - - 000 2 000 0 - 1 0 0 0 3 2 0 0 3 7 1809 1341 103188 4677 0000140 1 1528 2488 - 0 1 0 1 0 1 1 2 0 0 - 0 0 12 9 10- - 0 1 1 4 - 0 0 2384 2539 9 6 0 0 C4 913 1083 1050 1262 299 430 432 673 143 182 10 9 14 564 553 769 842 1197 68 108 512 384 326 405 586 673 1785 2215 302 416 361 233 760 1003 440 577 694 967 506 868 511 738 28 87 1 6 385 672 574 742 507 772 306 359 0 154 0 0 1098 10 904 1272 1 2 173 812 465 584 190 750 574 512 585 136 291 363 144 125 220 277 C5 1668 1992 2172 2345 0 1 03011799 1949 051397 1731 4 12 2130 2245 0 1 1996 2160 0007031984 2662 0 10 1819 2188 0 6 01040 3 83 128 1953 2140 0 3 3 1 010 0 0 4 6 18 0 4 0 11 0 5 334 71 27 59 0013- C6 2 0 0 1 5981 1942 0 1 7 4 2 0 1 0 0 0 0 0 0 0 2 2 1 0 1 1 0 0 0 0 2 0 0 0 1 1 1 0 0 1 C7 1218 1413 456 428 1059 2553 553 777 41 65 969 980 0 0 012 1 982 1142 00200 233 0 0 2710 3487 000 0 139 201 926 1225 65 73 0 0 444 1138 51 163 001263 1717 00887 1110 1 1 0308 0 4 0 0 246 355 46309 864 1205 1661 0 3 - 17 5 920 155 1060 1686 880 714 556 663 - C8_2 - 415 645 993 980 354 892 - 4 7 302 271 44 56 572 626 1128 1453 968 1045 117 179 415 1187 838 1134 0131001003 10 490 704 00675 1206 279 68 2300- C9 0000180 159 101588 2433 555 793 - - - - - - - C10 - C11 0 0 0 3 - 1 7 0 0 80009 0 10000 0 103 3 - - 11- - 0 0 - - 4 8 0 0 C12 248 224 1099 1436 370 518 56 50 163 870 937 994 20619 652 10831 779 1064 1180 1955 2033 111537 1774 381 379 474 411 511 835 00134 145 00832 850 500180 130 140 883 11874 2176 380051 115 1764 298 177 229 0016- D1 D2_2 0295- 000 0 - - 490 4 3044 3930 0 0 1563 2184 0 0 - 1909 3151 0013 10 - D3 1 1 577 889 - 201 777 710 1004 1909 2092 001051 1323 002 0 0 1 1178 1591 010 0 0 1 0 0 0 0 1 2 001 0 - 1 0 0 0 1 0 0 1 0 0 0 0 0 0 - 1 0 8 9 2 0 10- - 0 1 0 0 0 0 641 989 6 8 - - D4 1112 1285 1184 1499 2209 3522 57 41 28 933 83 133 1005 1321 1977 2623 263 531 2 0 488 677 1041 1344 469 917 0 0 0 1 0 0 0 1 911 1324 26 41 875 855 0 0 0 0 320 60 0 4 9 1 1 858 1218 13 59 1363 1290 0 0 1270 1906 4 4 1192 1863 D5 0 1 0 1 0 5 130202004 6 0 9 22110 021 0 04010 2 0 3 1 8 0202100 0 0 4 0 1 0 0 01013200071303107 9 0 0 01- D6 00100 0 - 5 3 00- D7_2 14 19 0 0 4 9 1 10 3 11 6 3 0 0 1 0 0 0 - 0 0 10- 0 0 10 19 712 2 3 0 0 - 0 0 0 0 0 0 0 1 00- 0 0 0 0 1 0 0 0 000 0 0 1 0 0 6077 1465 3 7 - 3 3 0 0 2 1 3 5 0 0 0 0 Low D8_2 D9_4 D10 0516 0728 807 1762 598 1104 2102 746 157 2 780 362 0 1240 362 0 234 633 0 362 83 133 117 269 1 1 296 0 0 370 120 0 172 808 0 503 0 4 172 0 2 423 1 6 706 4 6 1108 223 1 341 330 0 471 400 0 394 320 0 287 501 1620 452 725 2097 536 0327 0467 718 0 736 1026 1 1005 282 482 311 4 133 184 1 217 439 1 472 879 0 848 0200 0354 552 805 2 1 216 0 2 457 38 680 193 125 1653 410 259 81 895 288 39 242 46 369 1 0 194 1 0 366 537 201 682 241 280 2 293 428 0 382 852 1 201 784 0 294 00122 81 171 228 245 2 702 2 0 556 2 449 104 0 791 0 0 31 0 629 0 497 89 0 752 892 0 505 1188 0 703 145 0 189 319 1 405 376 58 61 616 13 167 650 2 394 1248 3 667 139 0 186 90 0 189 0 5 248 0 0 355 - D11 D12 0 0 935 1101 1325 2478 0 0 371 499 885 741 33 1227 460 725 86 155 723 994 206 140 68 88 23 28 1 0 0 0 1 0 1 5 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 5 8 317 189 1324 1575 405 567 511 642 140 339 0 0 0 0 110 141 1 4 0 0 671 879 22 11 0 76 996 1410 29 1 3 1 0 0 34 268 299 472 56 206 174 174 558 141 973 1306 0 0 108 246 E1_bulk E2_2 E3 349 505 362 623 587 1157 318 1486 624 2574 258 452 263 303 25 1021 383 745 664 3 1327 6 368 1 698 0 448 0 486 0 353 0 488 0 299 0 463 0 749 0 1389 1 413 808 320 256 492 421 0 356 786 1 281 443 712 475 471 939 0 204 2 1 549 1 28 230 0 37 421 2 63 98 1 166 322 0 149 137 837 139 247 1151 0 167 0 0 396 0 73 414 72 760 556 179 0 474 354 1 509 290 0 361 418 0 0 04 158 0 169 0 287 882 686 8 16 1301 284 3548 1241 584 4377 3 2 0 2 3 0 2 140 12 867 844 479 745 1060 137 113 1 313 642 0 579 362 333 535 781 519 113 214 934 440 4 805 741 7 311 136 167 145 173 159 186 424 351 266 1213 919 662 589 811 730 279 221 229 134 40 507 43 52 1160 1155 494 486 766 309 563 431 759 514 2068 1741 217 198 E4 000 1 1171 1889 0 0 199 317 466 388 54 159 238 528 1188 1414 701 624 82 55 1134 1206 585 640 1808 2130 262 333 582 586 260 379 452 510 1221 1343 148 277 0 0 251 708 1 2 359 509 408 440 395 408 491 597 0 147 16 25 899 6537 835 2 2 1744 2897 717 889 407 775 930 115 936 232 97 154 60 52 457 481 E5 E6 0 1 396 418 2475 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187 1211 137 630 791 786 497 785 0 1 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing • Which Epitopes go into the vaccine? 1) Diversify across HLA alleles 2) Select mutations with balanced allele specific expression 3) Maximize (expected) clone coverage 4) Include epitopes with high MHC binding affinity 5) Include epitopes with high Differential Agretopic Index (DAI): difference in MHC affinity between mutant epitope and its wild type counter part Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Duan et. al., JEM 2014 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing • Expitope (Haase et. al., Bioinformatics 2014) – Checks for cross-reactivity based on ENCODE RNA-Seq from normal tissues – http://webclu.bio.wzw.tum.de/expitope/ • OptiTope (Toussaint and Kohlbacher, Nucleic Acid Research 2009) – Optimizes allele coverage – http://etk.informatik.uni-tuebingen.de/optitope Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing Toussaint and Kohlbacher, Nucleic Acid Research 2009 Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing • T Cell Receptor sequencing – Compare the TCR repertoire before and after immunization to determine response against used epitope(s) • Primary analysis of TCR sequencing data – IMSEQ (Kuchenbecker et. al., Bioinformatics 2015) http://www.imtools.org/ – HTJoinSolver (Russ et. al., BMC Bioinformatics 2015) https://dcb.cit.nih.gov/HTJoinSolver/ Sequencing QC and Mapping Calling SNVs Epitope Prediction Clonality Analysis Vaccine Design TCR Sequencing • tcR (Nazarov et. al., BMC Bioinformatics, 2015) – R package for downstream analysis, including diversity measures, shared T cell receptor sequences identification http://imminfo.github.io/tcr/ THANK YOU! • QC • FASTX: http://hannonlab.cshl.edu/fastx_toolkit/) • PRINSEQ: http://prinseq.sourceforge.net/) • Epitope prediction • • • • NetChop: http://www.cbs.dtu.dk/services/NetChop/ SYFPEITHI: http://www.syfpeithi.de/ NETMHC: http://www.cbs.dtu.dk/services/NetMHC/ NetCTL: http://www.cbs.dtu.dk/services/NetCTL/ • Tumor Specific epitope predicton pipeline • Epi-Seq: http://dna.engr.uconn.edu/?page_id=470 Also available on out galaxy server: http://mhc1.engr.uconn.edu:8080/ • Vaccine design • Expitope: http://webclu.bio.wzw.tum.de/expitope/ • OptiTope: http://etk.informatik.uni-tuebingen.de/optitope • TCR sequencing analysis • Epi-Seq: http://imminfo.github.io/tcr/