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Ovarian Cancer: How Basic Research Can Lead to New Opportunities for Early Detection and Treatment.

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Presentation on theme: "Ovarian Cancer: How Basic Research Can Lead to New Opportunities for Early Detection and Treatment."— Presentation transcript:

1 Ovarian Cancer: How Basic Research Can Lead to New Opportunities for Early Detection and Treatment

2 The Human Genome Project 3 billion bases of DNA Completed in 2003 Medicine of the Future

3 Central Dogma of Molecular Biology DNA RNA Protein

4 Complexity of Gene Expression 40,000-50,000 genes (over 100,000 gene products, and Probably over 1 million different proteins) Hundreds of Tissues Thousands of disease states Environmental factors

5 Auto Parts Warehouse

6 Gene Expression Analysis Techniques cDNA RDA Subtractive hybridization Differential display Microarrays SAGE EST } differentially expressed cDNA fragments } Global expression profiles Northern Blotting RT-PCR } “gene-by-gene” techniques

7 Northern Blot Blot Tissue Gel Gene of Interest Probe RNA Label

8 Quantitative PCR Methods PCR Cycles Fluorescence Real-time RT-PCR Allow 96-well format

9 Methods for Large Scale Studies of Gene Expression Microarrays EST sequencing SAGE

10 Microarrays

11 EST Sequencing AAAAAAAAAA TTTTTTTTTTT Reverse Transcription Sequence large number of clones Prepare RNA Tissue of interest

12 Serial Analysis of Gene Expression (SAGE) Principle A short sequence tag (10-14bp) contains sufficient information to uniquely identify a transcript provided that the tag is obtained from a unique position within each transcript

13 SAGE Methodology 1) Sequence tags obtained from a cDNA library can be linked together to form concatemers that can be cloned and sequenced 2) A count of the number of times a particular tag is observed provides the expression level of the corresponding transcript

14 Serial Analysis of Gene Expression (SAGE) Prepare RNA AAAAAAAAAA Create Tags Tissue of interest Ligate Tags Sequence concatemers and analyze tag frequency

15 Results From a SAGE Experiment Transcripts NormalDisease Abs. Levels

16 Cancer Research

17 Cancer and Aging Life expectancy Roman empire: 25 years Middle ages: 33 years 1850: 45 years U.S. in 2000: 75 years

18 Ovarian Cancer Believed to originate from a single layer of epithelial cells covering the ovaries 25,000 new cases in the U.S. in ,000 will die of the disease Most cases diagnosed as advanced disease No reliable sensitive markers for early detection

19 Prognosis Early disease: >90% survival Advanced disease: <20% survival Only 20% of women diagnosed early Early detection would have a significant impact on mortality from ovarian cancer

20 Ovarian Cancer Staging Stage 1Stage III

21 Serous Adenocarcinoma Tumor

22 Therapy Standard chemotherapy: cisplatin and taxol Half the cases intrinsically resistant Many tumors develop resistance to cisplatin Mechanisms of drug resistance are unknown

23 Use of SAGE to Identify Genes Differentially Expressed in Ovarian Cancer May help identify reliable markers May provide targets for therapy Better understanding of the disease

24 Strategy Perform SAGE on: 1) Normal ovarian epithelium 2) Ovarian tumors

25 Summary of SAGE Libraries Library Sequence Tags Unique tags Genes > 2 tags HOSE 2,290 47,881 16,034 12,778 4,532 IOSE 1,912 47,549 18,004 14,771 5,681 ML10 1,935 55,700 18,727 14,939 6,637 OVT6 2,104 41,620 18,476 15,646 4,799 OVT7 2,089 53,898 19,523 15,858 5,669 OVT8 2,076 32,494 16,363 14,153 3,815 OV1063 2,146 37,862 15,231 12,656 4,746 A2780 1,332 21,587 10,717 9,249 2,761 ES2 1,775 35,352 14,739 12,335 3,952 POOL 2,201 10,554 5,956 5,238 1,627 TOTAL 19, ,497 82,533 56,387 28,219 Hough et al. (2000) Cancer Res.

26 Top 12 Genes Expressed in ES-2 1 TGCAGTCACT 1.25%Collagenase 2 TGTGTTGAGA 0.95 %EF-1  3 CCCATCGTCC 0.63 %Cytochrome C oxidase II 4 ATGGCTGGTA 0.59 %Ribosomal S2 5 GTGAAACCCC 0.52 %GM-CSF receptor  CD82 6 AAGACAGTGG 0.49 %Ribosomal L37a 7 AGCACCTCCA 0.48 %EF-2 8 GCCGGGTGGG 0.43 %Collagenase Stimul. Fact. 9 GGATTTGGCC 0.43%Qip1 10 CCTGTAATCC 0.40 %Gz-selective GTPase-act prt 11 TCCAAATCGA 0.39 %Vimentin 12 CCCGTCCGGA 0.38 %Ribosomal L13 (EST) TagExp. LevelGeneRank

27 Normal ovary ovarian cancer Gene expression differences Identifying Gene Differentially Expressed in Ovarian Cancer

28 Genes Consistently Up-regulated up-regulated gene Fold Function HLA-DR  chain 289 Major histocompatibility complex, class II Cysteine-rich protein LIM/double zinc finger Claudin Tight junction barrier function ESTs 101 Unknown Surface marker 1 93 Tumor Ag/ Ca 2+ signal transducer Claudin-3 83 Tight junction barrier function Ceruloplasmin 79 Secreted metalloprotein/ antioxidant HE4 72 Secreted protease inhibitor GPX3 69 Secreted selenoprotein/ peroxidase SLPI 60 Secreted serine protease inhibitor ESTs 56 Unknown IFN-Induced protein 1 49 Receptor for interferon signaling Ep-CAM 48 Tumor Ag/ Ca 2+ -independent CAM/ proliferation Mucin 1 43 Tumor Ag/ Type- I membrane glycoprotein

29 Immunostaining Allows specific staining of the tumor for the expression of a protein of interest

30 Staining of Ovarian Cancer with an Ep-CAM antibody

31 Immunotherapy

32 Blood Test For Early Detection

33 Conclusions SAGE can be used to identify the thousands of genes expressed in a given tissue This information can be used to improve our understanding of biological phenomena such as development, disease, etc We have identified several genes differentially expressed in ovarian cancer that may be useful as early markers or as therapeutic targets


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