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肿瘤基因组学 肿瘤整合组学. 中南大学肿瘤研究所 向娟娟 ( [email protected] ) 2012 年 5 月 18 日. A turning point in cancer research: sequencing the human genome. Science. 1986 7;231(4742):1055-6.

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肿瘤基因组学 肿瘤整合组学

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([email protected])

2012518


A turning point in cancer research sequencing the human genome science 1986 7 231 4742 1055 6

A turning point in cancer research: sequencing the human genome. Science. 1986 7;231(4742):1055-6

  • We have two options: either to try to discover the genes important in malignancy by a piecemeal approach, or to sequence the whole genome of a selected animal species.

  • I think that it will be far more useful to begin by sequencing the cellular genome.

  • In which species should this effort be made? If we wish to understand human cancer, it should be made in humans because the genetic control of cancer seems to be different in different species.


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From genetics to genomics

  • Genetics is the science of individual genes and their impact on inherited disease.

  • A Genome is an organism's complete set of DNA, including all of its genes. In humans, a copy of the entire genome-more than 3 billion DNA base pairs-is contained in all cells that have a nucleus.

  • Genomics is the study of the full collection of a person's genes and their interactions with each other and the internal and external environment they are exposed to.


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1

  • RNADNADNADNA


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2

  • DNARNA


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  • 4.8kb,46

  • DNA

  • DNA


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  • DNA


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1?

  • 1986Thomas Roderick(genomics)()


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2

  • 1972Walter fiersbacteriophage MS2 coat protein

  • Fred SangerDNA5368bp

  • 19951.8MB


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3


4 human genome project hgp

4(human genome project, HGP)


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HGP

  • 1984.12

    DNA

  • 1986 Science:

  • (DOE)

  • 1987 NIH

    550

  • 1989 Watson

  • 1990.10


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HGP

  • 15(19902005)$30

  • DNA


Hgp mapping sequencing

HGPmapping,sequencing)


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genetic map

DNAcM 1


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DNA


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DNA

  • DNA()


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  • 6000600050%

  • 50%0

  • 0


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1(RFLP)

RFLP


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  • 2(VNTR)

  • VNTRVNTR10RFLP15-65DNA2-6DNASTRSTRPSSLP


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  • DNASNP

  • SNP12

  • 300SNP

  • SNPRFLPSTRPDNA


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DNA(bp) (kb)(Mb)


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  • 30

  • ()


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2%5%mRNA


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HGP


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  • DNA


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  • 3

  • 171922XY418

  • SNP


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5 snp gwas

5. SNPGWAS


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SNP)

  • (single nucleotide polymorphismSNP),DNA

  • SNP 1000SNP 90%SNP50010001300

  • SNPs1%SNP

  • SNP SNP


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GWAS

  • Genome-wide association studyGWAS

  • --- SNPs


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1

  • oncogene

  • tumor suppressor geneantioncogenetumor susceptibility gene


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  • DNARNA

  • *virus oncogene,v-onc*)


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  • /


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2

  • Sanger


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PIK3CA

PIK3CA

TP53

TP53


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3

  • :

  • :

  • :


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4

  • DNA


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EGFR

EML4-ALK

  • ER(+)PR(+)

  • HER2

  • K-Ras

  • BRAF


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  • US National Cancer Institute


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5

  • 2006

  • ICGC200850

  • 100


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  • reads)

  • RNARNA-Seq


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  • SNP

  • Base calling:


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From sequence gene to expression functional genomics

From Sequence gene to expression (functional genomics)


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  • Functuional genomicsPostgenomics


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2

  • DNAGWAS


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3

  • serial analysis of gene expression,SAGE

  • DNA


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  • transcriptomicsRNARNA


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  • Oncotype DX)


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  • proteomeproteingenome


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  • (metabonomicsmetabolomics)


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  • :


Charting a course for genomic medicine from base pairs to bedside

Charting a course for genomic medicine from base pairs to bedside

  • Nature Volume: 470,Pages:

  • 204213

  • Date published:

    • (10 February 2011)


  • 3657440


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    1.


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    • (mRNA)


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    2


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    3


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    Citric acid cycle

    Transcriptome

    Metabolome

    Proteome

    Genome


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    • ,

    • , ,


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    4.


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    • DNA

    • miRNA


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    Networks in Cellular Systems

    To date, cellular networks are most available for the super-model organisms (Davis, 2004) yeast, worm, fly, and plant. High-throughput interactome mapping

    relies upon genome-scale resources such as ORFeome resources.


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    5.4


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    Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network

    Science, 2001, 292:929


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    microarray20mRNA


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    16


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    • WT GAL(+)WT GAL(-)289


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    /mRNA

    289WT GAL(+)/WT GAL(-)/ mRNA


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    • 348mRNA


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    LEDA toolboxDNAAGAL4GAL(+)mRNAGAL


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    Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targetsBMC Syst Biol. 2008 Jun 30;2:56.

    Two human yeast two-hybrid data sets

    Online interactome databases

    Nonlinear stochastic model of dynamic protein-protein interactions


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    x [t + 1] = ax [t] +b1x1 [t] +b2x2 [t] + b3x3 [t] + c12x12 [t] + k + [t].


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    Global protein-protein interactions of apoptosis in cancerous and normal cells. (A) Apoptotic protein-protein interaction network in HeLa cells, showing 183 nodes and 552 edges. (B) Apoptotic protein-protein interaction network in normal primary lung fibroblasts, showing 175 nodes and 547 edges. Each interaction was calculated twice and only interactions with two '1' scores after AIC evaluation was considered 'true' interactions. All protein-protein interaction networks in this study were constructed with Osprey version 1.2.0.


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    Cancer-perturbed protein-protein interactions in the apoptosis network. (A) 'Gain-of-function' network, showing 140 nodes and 157 edges. (B) 'Loss-of-function' network, showing 126 nodes and 162 edges. Colors of nodes represent Gene Ontology annotations.


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    Contribution of genomics

    Contribution of Genomics

    • 30002000

    • ;


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    Thank you for your attention


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