Next Generation Sequencing Technology Research and Clinical applications Ron M. Kerkhoven, PhD Genomics Core Facility Toen en nu 2013 Aantal bedden 1915 17 180 Aantal nieuwe patiënten 1915 17 6000 Aantal bestralingen 1915 2747 70000 Aantal medewerkers 1916 10 3000 Aantal wetenschappelijk publicaties 1917 11 508 Aantal wetenschappers 1913 2 720 Aantal leden van de medische staf 1913 2 155 98 5000 Aantal operaties 1915 The Netherlands Cancer Institute Technology development at the NKI-AvL Next Gen Sequencing numbers Microarrays 2001 Mammaprint 2006 2009 2013 200.000.000 per experiment 50 - 300 bp each Clusters 8 lanes 1 lane HiSeq2000 200 milion reads/lane MiSeq 20 milion reads/lane Sheared genomic DNA Genomic DNA library preparation end repair, A-overhang A A T adapter ligation PCR 7 cycles P5 fragments of 200-400 bp P7 High Coverage Sequencing window IGV (integrative genomics viewer) www.broadinstitute.org/igv Chromosome 17 scale 0-490 start end reads piled up bases Gene:TP53 amino acids (exon) Reference sequence intron Cumulative output of sequence data (bp) by the GCF 20.000.000.000.000 bp 385 samples p.m. Cancer Clinic Research Lab RNAseq CNVseq ChIP Capture approaches -Exome, Kinome -Full Genome -T-Cell Repertoire -Fanconi family Haploscreen MeDIP small RNA Haloplex Nextera FAIRE shRNA Mate Pair TRIP Cell barcoding CNVseq BRCA1 on FFPE samples BRCA2 on FFPE samples Next Generation Sequencing Amplicon seq Illumina Cancer Panel on FFPE Scientific investigation Patient Capture approaches Diagnostics 178 gene Cancer Panel DNA and RNA capture on FFPE Actionable mutations Het Nederlandse Kanker InstituutAntoni van Leeuwenhoek Ziekenhuis, ErasmusMC Daniel den Hoed Oncologisch Centrum UMC Utrecht RNA-Seq (gene expression profiling) Degraded RNA (FFPE) Fresh total RNA 200 ng – 1 ug total RNA 500 ng - 1 ug total RNA mRNA selection rRNA removal ( RiboZero) fragmentation SciClone cDNA synthesis (random priming) End repair, A overhang, Adapter ligation, PCR 1 HiSeq lane, 10 samples Stranded RNA-Seq Stranded RNA-Seq RNA-Seq is very reproducible 1 2 3 4 5 6 1 2 3 4 5 6 Research Lab Cancer Clinic RNAseq CNVseq ChIP Capture approaches -Exome, Kinome -Full Genome -T-Cell Repertoire -Fanconi family Haploscreen MeDIP small RNA Haloplex Nextera FAIRE shRNA Mate Pair TRIP Cell barcoding CNVseq BRCA1 BRCA2 Next Generation Sequencing Amplicon seq Illumina Cancer Panel on FFPE Scientific investigation Patient Capture approaches Diagnostics 178 gene Cancer Panel DNA and RNA capture on FFPE ChIPseq Chromatin Immuno Precipitation to map “DNA binding sites.” FAIRE Formaldehyde Assisted Isolation of Regulatory Elements to map “open chromatin.” FAIRE signal at AR peaks Human prostate cancer specimen – 14G biopsy FAIRE Androgen Receptor ChIP XBP1 FAIRE Androgen Receptor ChIP KLK3 KLK2 KLKP1 FAIRE Androgen Receptor ChIP UMODL1 -5kb +5kb Wilbert Zwart, Suzan Stelloo, Gaetano Gargiulo, Bauxu Pang Cancer Clinic Research Lab RNAseq CNVseq ChIP Capture approaches -Exome, Kinome -Full Genome -T-Cell Repertoire -Fanconi family Haploscreen MeDIP small RNA Haloplex Nextera FAIRE shRNA Mate Pair TRIP Cell barcoding CNVseq BRCA1 BRCA2 Next Generation Sequencing Amplicon seq Illumina Cancer Panel on FFPE Scientific investigation Patient Capture approaches Diagnostics 178 gene Cancer Panel DNA and RNA capture on FFPE Carsten Linnemann High-throughput identification of antigen-specific TCRs by TCR gene capture Carsten Linnemann, Bianca Heemskerk, Pia Kvistborg, Roelof J C Kluin, Dmitriy A Bolotin, Xiaojing Chen, Kaspar Bresser, Marja Nieuwland, Remko Schotte, Samira Michels, Raquel Gomez-Eerland, Lorenz Jahn, Pleun Hombrink, Nicolas Legrand, Chengyi Jenny Shu, Ilgar Z Mamedov, Arno Velds, Christian U Blank, John B A G Haanen, Maria A Turchaninova, Ron M Kerkhoven, Hergen Spits, Sine Reker Hadrup, Mirjam H M Heemskerk, Thomas Blankenstein et al. Nature Medicine (2013 )doi:10.1038/nm.3359 http://www.biology.arizona.edu/immunology/tutorials/immunology Rearrangement of the T-cell receptor locus T- cell repertoire analysis Research Lab Cancer Clinic RNAseq CNVseq ChIP Capture approaches -Exome, Kinome -Full Genome -T-Cell Repertoire -Fanconi family Haploscreen MeDIP small RNA Haloplex Nextera FAIRE shRNA Mate Pair TRIP Cell barcoding CNVseq BRCA1 BRCA2 Next Generation Sequencing Amplicon seq Illumina Cancer Panel on FFPE Scientific investigation Patient Capture approaches Diagnostics 178 gene Cancer Panel DNA and RNA capture on FFPE How does genome organization influence gene regulation? Waseem Akthar Aleksey Pindyurin same gene in different genomic neighborhoods results in different phenotype TRIP MiSeq SR 76 bp Sample index vector barcode vector Reporter expression patterns reflect chromatin domain organization permissive non-permissive Research Lab Cancer Clinic RNAseq CNVseq ChIP Capture approaches -Exome, Kinome -Full Genome -T-Cell Repertoire -Fanconi family Haploscreen MeDIP small RNA Haloplex Nextera FAIRE shRNA Mate Pair TRIP Cell barcoding CNVseq BRCA1 BRCA2 Next Generation Sequencing Amplicon seq Illumina Cancer Panel on FFPE Scientific investigation Patient Capture approaches Diagnostics 178 gene Cancer Panel DNA and RNA capture on FFPE Translational results Patient Treatment Short hairpin RNA screens Roderick Beijersbergen, Sid Huang http://www.uni-konstanz.de/FuF/chemie/jhartig/ Reagent collections at the NKI screening facility NKI shRNA libraries Open Biosystems TRC 1.0 TRC 2.0 TransOmic 24.000 vectors 28.000 vectors against 8.000 human genes against 14.000 mouse genes ~ 85.000 miR-shRNA vectors against 20.000 genes ~ 85.000 vectors against 16.000 mouse genes ~ 130.000 shRNA vectors against 20.000 human genes ~ 2.600 miR-shRNA vectors against human kinome (~ 700 genes) Subcollections of shRNA libraries: Kinome DNA damage DeUbiquitinating enzymes Phosphatases Chromatin Modifiers PI3K & MAPK interaction network WNT signalling (human & mouse) (human & mouse) (human) (human) (human) (human) (mouse) Roderick Beijersbergen / René Bernards Short hairpin RNA screens Genomic DNA from cell cultures with integrated viral DNA Single Read 51 bp sequencing Seq primer P5 index Roderick Beijersbergen, Sid Huang, Gijs van Haaften P7 Targeted agents: dramatic, but short-lived responses Melanoma treated with Vemurafenib Progression Free Survival Chapman et al., N Engl J Med 2011 Constitutive active kinase of mutant BRAFV600E is inhibited by Vemurafenib Differential response of BRAF inhibition in BRAF mutant melanoma versus colon cancer Vemurafenib (PLX4032) - A selective BRAFV600E inhibitor response rate in BRAFV600E-positive tumors 90% 80% 81% 70% N Engl J Med. 2010 363:809-19 Kopetz et al., ASCO 2010 60% 50% 40% 30% 20% 5.20% 10% 0% Melanoma Colon cancer Inhibition of which kinase synergizes with PLX in BRAF mutant CRC (colorectal carcinoma)? (Colon cancer cells) shRNA drop out screen Synthetic Lethality: two pathways blocked to kill cancer cells VACO cells (Colorectal carcinoma cells) undergo apoptosis due to synergy of two treatments VACO cells (BRAFV600E) Vemurafenib EGFR inhibitors Cetuximab 1.25 mg/ml (MoAb) Gefitinib 0.125 µM (TKI) EGFR and BRAF inhibition synergize to induce apoptosis and suppress BRAFV600E CRC tumor growth Prahallad et al, Nature 2012 Cleaved PARP is a hallmark of apoptosis Timeline BRAF colon cancer project Publication March 2012 2011 Start funding research project January 2011 37 First clinical responses March 2013 2012 First patient in trial at NKI November 2012 2013 AvL Patient samples Pathology & Molecular Diagnostics clinical samples Treatment decision RNA/DNA FF / FFPE Tumor sequencing board 178 cancer genes capture on tumor DNA (mutations) on tumor RNA (translocations) Sample preparation Sequencing on HiSeq or MiSeq Genomics Core Facility Primary Analysis Pipelines Sequence data Variant calls Agent 1 Ado-trastuzumab emtansine (Kadcyla) 2 Axitinib (Inlyta) 3 Bevacizumab (Avastin) Target(s) HER2 (ERBB2/neu) KIT, PDGFRβ, VEGFR1/2/3 VEGF ligand 4 Bosutinib (Bosulif) 5 Cabozantinib (Cometriq) 6 Cetuximab (Erbitux) ABL FLT3, KIT, MET, RET, VEGFR2 EGFR (HER1/ERBB1) 7 Crizotinib (Xalkori) 8 Dasatinib (Sprycel) ALK, MET ABL 9 Erlotinib (Tarceva) EGFR (HER1/ERBB1) 10 Everolimus (Afinitor) mTOR 11 Gefitinib (Iressa) 12 Imatinib (Gleevec) EGFR (HER1/ERBB1) KIT, PDGFR, ABL 13 Ipilimumab (Yervoy) 14 Lapatinib (Tykerb) 15 Nilotinib (Tasigna) 16 Panitumumab (Vectibix) 17 Pazopanib (Votrient) 18 Ponatinib (Iclusig) CTLA-4 HER2 (ERBB2/neu), EGFR (HER1/ERBB1) ABL EGFR (HER1/ERBB1) VEGFR, PDGFR, KIT ABL, FGFR1-3, FLT3, VEGFR2 19 Regorafenib (Stivarga) KIT, PDGFRβ, RAF, RET, VEGFR1/2/3 20 Ruxolitinib (Jakafi) 21 Sorafenib (Nexavar) JAK1/2 VEGFR, PDGFR, KIT, RAF 22 Sunitinib (Sutent) VEGFR, PDGFR, KIT, RET 23 Temsirolimus (Torisel) 24 Tofacitinib (Xeljanz) 25 Trastuzumab (Herceptin) mTOR JAK3 HER2 (ERBB2/neu) 26 Vandetanib (Caprelsa) 27 Vemurafenib (Zelboraf) EGFR (HER1/ERBB1), RET, VEGFR2 BRAF FDA-approved indication(s) Breast cancer (HER2+) Renal cell carcinoma Colorectal cancer Glioblastoma Non-small cell lung cancer Renal cell carcinoma Chronic myelogenous leukemia (Philadelphia chromosome positive) Medullary thyroid cancer Colorectal cancer (KRAS wild type) Squamous cell cancer of the head and neck Non-small cell lung cancer (with ALK fusion) Chronic myelogenous leukemia (Philadelphia chromosome positive) Acute lymphoblastic leukemia (Philadelphia chromosome positive) Non-small cell lung cancer Pancreatic cancer Pancreatic neuroendocrine tumor Renal cell carcinoma Nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis Non-small cell lung cancer with known prior benefit from gefitinib (limited approval) GI stromal tumor (KIT+) Dermatofibrosarcoma protuberans Multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML Melanoma Breast cancer (HER2+) Chronic myelogenous leukemia (Philadelphia chromosome positive) Colorectal cancer (KRAS wild type) Renal cell carcinoma Chronic myelogenous leukemia Acute lymphoblastic leukemia (Philadelphia chromosome positive) Colorectal cancer Gastrointestinal stromal tumors Myelofibrosis Hepatocellular carcinoma Renal cell carcinoma GI stromal tumor Pancreatic neuroendocrine tumor Renal cell carcinoma Renal cell carcinoma Rheumatoid arthritis Breast cancer (HER2+) Gastric cancer (HER2+) Medullary thyroid cancer Melanoma (with BRAF V600 mutation) FDA approved clinical samples AvL Patient samples Pathology & Molecular Diagnostics Serum Amplicon Panel 48 genes RNA/DNA FF / FFPE 178 cancer genes capture ctDNA AKL low coverage CNV-Seq Sample preparation Sequencing on HiSeq or MiSeq Genomics Core Facility Primary Analysis Pipelines Sequence data Variant calls Tumor sequencing board CNV-Seq Copy number variation sequencing 250-500 ng of Fresh or FFPE DNA 1 HiSeq lane, 10 samples, coverage ~0.2x Costs per sample: 150 Euro BRCA1,2 classifier Full genome 3300 Mb 7x Exonic sequences 34 Mb 700x ChIP Kinome 2.6 Mb 7000 x RNA DNA gene expression analysis 178 gene NKI cancer panel libraries 10.000 x <1 Mb BAIT SIZE coverage Gene Capture Variant calling exome, kinome, 1 sample in 1 lane PE 2x50 HiSeq TCR Single bp Mutations Amplifications Deletions Inversions Insertions Multiplexing 16 samples/lane on MiSeq Agilent XT2 system RNA Expression levels Multiplex XT2 capture with178 genes NKI Capture Cancer panel Normal FFPE Tumor FFPE Tumor FF Normal (FFPE) Tumor (FFPE + FF) 18/49 Lungscape series (biopsies) M. van den Heuvel FF libraries cluster more efficient than FFPE libraries Raw read counts (x10^6) 50 FFPE RNA libararies FF RNA libararies 40 30 20 10 0 FFPE DNA captures FF Lungscape series (biopsies) M. van den Heuvel Changing views of Non Small Cell Lung Cancer Traditional view Current view Conclusions: -NGS is a revolutionary technology producing a wealthy amount of data. -NGS is used in the NKI in a routine setting with many types of applications. -NGS is investigated for clinical application using FF and FFPE samples - 1) The Amplicon panel (mutation hotspots) - 2) a 178 gene Capture Cancer Panel - 3) ctDNA analysis -Standard clinical application will still require optimalization, validation and testing -Expectation is that we will routinely apply NGS clinically in June 2014. Acknowledgements Genomics Core Facility Marja Nieuwland Wim Brugman Arno Velds Iris de Rink Shan Baban Erwin Bekema Janneke Kruizinga Roel Kluin Bernd van der Veen Daan Vessies Mariëlle Kreté Stefan Lehnert Ron Kerkhoven NGS project group Clinical implementation Steering group Sandra Timmermans, Sanne Heinzbergen, Michel vd Heuvel, Michiel vd Heijden, Jelle Cool, Ron Kerkhoven, Henri van Luenen, Petra Nederlof, Maartje Vogel Also involved: René Bernards, Annegien Broeks, Lizet vd Kolk, Jeroen de Jong, Esther Lips, Els Verhoeven, Efraim Rosenberg, Arno Velds, Marja Nieuwland, Wim Brugman, Iris de Rink, Frans Hogervorst, Valesca Retel, Paula Hoekstra, Daan Vessies, Mariëlle Kreté. NKI research Departments Dept Pathology Dept Radiology Family Cancer Clinic Clinic Core Facility Molecular Pathology & Biobanking Molecular Diagnostics Alg. Klinisch Lab Mol. Carcinogenesis NKI collegues: Roderick Beijerbergen, Wilbert Zwart, Waseem Akthar, Lorenza Mittemperher, Carsten Linnemann.
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