Traditional (3 bias amp

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Traditional (3 bias amp

Demystifying Microarrays….?Mark W. Geraci, M.D.Professor and Head,Division of Pulmonary Sciences and Critical Care MedicineDirector, Genomics FacilityCo-Director, Colorado CTSIUniversity it of Coolorado Denver


Processing cDNA clones96 well glycerol stock plateBarcode/ScanDATABASESource PlateStored-80 freezerPlate Replicategrow/add glycerolAmplification96 wellResuspendSSCCloneTracker/PADSoftwareAssists design ofPrint pattern, spot lociInoculate LB Broth96 well blocks/15ml tubesQiagen BioRobot 8000If poor yield DNA IsolationQiagen BioRobot 8000noRun gelAmplified?yesPurificationPPT/ lyophilizeRe-array into96/384 well(Spotter source plates)BioRobot 8000QCpurity/sizeby agarose gelBioRobot 8000QCSpot-checkyield by O.D.Spotting now?fail pass Freeze -20pass/fail?noyesArray Spotting


Common Commercial ExpressionArraysAffymetrixAgilentIllumina


Chemoprevention - The GoalBenchCageBedside


Gross Inspection of Lung Adenomas


HumanMouseComparison of OrthologousGene Expression Changes:Human and MurineHuman and MurineAdenocarcinoma


Lung Metagene Model79 Genes


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


All ExonArraysHu Ex 1.0ST1.4 million exons assayed


WTA Considerations• Data intensive: Scans takeone hour each• 100 MB data per array• Previous data gave one“average” expressionvalue per “gene”• Now moves to expressionlevel per exon• Strategy t to use “universalexons” as expression“standard” per “gene”


• P I: Signal Estimation– Exon signal: PLIER algorithhm• A robust M-estimator that uses a multi-chip analysis to fit amodel for feature response and target response for eachsample– Gene signal: IterPLEIR-Exhibit different expression patterncompare to the constitute exon-Will have down weighted effect inoverall gene level value-Iteratively discards exons that do notcorrespond well with overall gene level


Gene and Exon Level of PAH andNormal


Exon Array and SuperiorCorrelation withQuant Peptide


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


Probe density incrreases sensitivity:Unbiased randomamplificationLow density covers 25 of 35basesHigh density covers every 25 bases with 20 BP overlap


Probe density increases sensitivity


Predicted Distribution of theTranscriptomeExon extension Intron= 5% expression =33%Intergenicexpression =62%


New terms• Dark Matter : Formerly called “junk DNA”• Transcriptional Forests and Deserts• ncRNA: non-coding RNA• miRNA: small noncoding micro RNA• si RNA: Small interfering RNA• ta-siRNA: trans-acting acting siRNA• snoRNA: small nucleolar RNA (sourced fromintrons)• TAR: transcriptionally active region• Transfrag: transcribed fragment• TUF: Transcript of unknown function


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


Arrays for MethylationAssessmentAffymetrixAgilentIllumina


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


ChIP on chipprotocolsusingPromoterarrays


Chip-Seq Workflow


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


Distinctions between Affymetrixand IlluminaSNP Assays


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


SNP Arrays for CNVN=371 Tumors


26 of 39 Autosomal armsshow large-scale gains orlosses31 Recurrent Local Events:24 Amplicifications, and 7Homozygous Deletionsonly 6 previously known


Copy Number Variations in PAH• Endothelial cells isolated from explanted lungs• These cells have aberrantgrowth properties• Perform SNP arrays and analyze data for copy number• Confirm findings by FISH incell cultures• Confirm FISH in the explannted lung tissue, plexiformlesion


Mosaic chromosome 13 deletion inBMPR2 mut PAH PAECsPA AEC/Blo oodLo og 2 Ra atiop < 0.01 for reduction in chromosome 13Magnitude indicates monosomy 13 in 11% of PAECs


FISH confirmation of chr 13monosomyin PAECs• Chr 13q1414 RB (red)• Chr 13q34 (green)• Chr 8 (aqua)2R2G2A2R1G2A• Monosomy 13 observedin 5% of interphasenuclei• Background loss ofchromosome 8 in 0.39%• Chi-squared p=0.0181R1G2A1R1G2A1R1G2A


FISH confirmation of chr 13 monosomyin plexiform lesion222222222222222 222 222222111111112222 222112112222222 112112 112


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


High-Throughhput tSequencing• High-Throughput g sequencing• First human genome = 13 years, $ 3billion• James Watson = 2 months, $1 million,2007• Newer technologies– Roche 454– Illumina / Solexa– ABI SOLiD– Helicos Pac Bio = single molecule– Towards the $1,000 genome


Pacific Biosystems SMRT


Overviewof Arrays1. Gene ExpressionTraditional (3’ bias ampplification) Exon- level (whole transcriptome amplification, WTA) Tiling Arrays (the true transcriptome)2. Regulation of Transcription Epigenomics and the “methylome” miRNA Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes es (DNA variation)at SNPs (single nucleotide polymorphisms) CNVs (copy number variation) Nex Gen Sequencing4. Putting it All Together


The Case of EGFR Targeted Therapy inLung Cancer• 170-kDa (1186aa), one of 90 TKs• EGFR can act as oncogene– Anderson, 1980; Vennstrom, 1982• EGFR in multiple cancers (Nicholson, 2001)– Bladder, H/N, breast, cervical, NSCLC• Monoclonal Ab can inhibit EGFR activity– Gill, 1984


Identification of EGFR Mutations in Lung Cancer• Lynch et al., NEJM, 2004– No mutations in EC domain,13 of 14 responders had somaticmutations in selected exons• Paez et al., Science, 2004– Sequenced all exons of 47 of 58 known TKs. Only mutationsfound in EGFR. Larger cohort sequenced.• Pao et al., PNAS, 2004– Looked at responders to either gefitinib or erlotininb– 25 of 31 tumors show mutations vs. none in 29 samples fromrefractory patientst


Typical and Heterogeneous NSCLC Cell-lineResponse to Gefitinibib120100Gefitinib CytotoxicityCalu3H322H358% Control8060402000006 0.00.030.10.6310Gefitinib uM20H1648H2122H441Colo699A549H1264H157H520


Supervised Analysis: 6 resistant vs 5 sensitiveNSCLC cell lines defines 415 genesSens ResOverabundance AnalysissuggestsTrue Discovery


Prediction of UTSWcell line sensitivityRed spots: HG U133Adata from John Minna(UTSW)SensitiveIC50 7µMGreen spots: Ourresistant cell linesBlue spots: Our sensitivecell lines


Summmary1. Gene ExpressionTraditional (3’ bias amplificcation) can be used for hypothesisgeneration and Diagnosis / Prognosis Exon- level (whole transcriptome amplification, WTA). Exon levelanalysis predicts protein expression (Miller). Important forProteomic analysis in lung disease Tiling Arrays (the true transcriptome, Gingeras)2. Regulation of Transcription Epigenomics and the “methylome” (Schwartz and Irizarry) miRNA (Spira) Nucleosome positioning (ChIP on chip, ChIP-Seq)3. Comparing Genomes (DNA variation) SNPs (single nucleotide polymorphisms) CNVs (copy number variation) (Coldren, Geraci) Nex Gen Sequencing4. Putting it All Together (Kaminski and Schadt)


Many, Many Thanks to the TeamGene Expression Facility• Bifeng Gao, Ph.D.• Tzulip Phang, Ph.D.• Todd Woessner, B.S.• Okyung Cho, B.S.• Ted Shade, M.S.• Mark W. Geraci, M.D.SPORE in LungCancer• Paul Bunn, M.D.• York Miller, M.D.• Bob Keith, M.D. .• Wilbur Franklin, MD M.D.• Raphael Nemenoff, Ph.D.• Al Malkinson, Ph.D.• Jack Dempsey,M.D.• Fred Hirsch, M. D., Ph.D.• Anna Baron, Ph.D.Collaborators from afar• Steve Dubinett - UCLA• David Carbone - Vanderbilt University• Ray DuBois – MD Anderson Cancer Ctr• John Minna – Univ of Texas Southwestern• Shu Narumiya - KyotoGeraci Lab• Bifeng Gao, PhD• Christopher Coldren, PhD• Michael Edwards, PhD• Steve Glidewell, PhD• Bob Stearman, PhD• Mark D.Moore, B.A.• Ryan Oyer, M.D.• Todd Bull, M.D.• Rebecca Doebele, M.D.• Michael Risbano, MD• Yasushi Hoshikawa, M.D., Ph.D.• Heiko Golpon, M.D.• Sylk Soto, B.S.• Bob Keith, M.D.• Qam Choudhury, Ph.D.• Michael Gruber, M.D.• Patrick Nana-Sinkam, M.D.

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