Ovarian Cancer: How Basic Research Can Lead to New

Opportunities for Early Detection and Treatment

The Human Genome Project

•3 billion bases of DNA•Completed in 2003

•Medicine of the Future

Central Dogma of Molecular Biology

DNA

RNA

Protein

Complexity of Gene Expression

40,000-50,000 genes (over 100,000 gene products, andProbably over 1 million different proteins)

Hundreds of TissuesThousands of disease states

Environmental factors

Auto Parts Warehouse

Gene Expression Analysis Techniques

cDNA RDA Subtractive hybridizationDifferential display

MicroarraysSAGEEST

}differentially expressed cDNA fragments

} Global expression profiles

Northern BlottingRT-PCR } “gene-by-gene”

techniques

Northern Blot

Blot

Tissue

Gel

Gene of Interest

Probe

RNA

Label

Quantitative PCR Methods

22 23PCR Cycles

Flu

ores

cen

ce

Real-time RT-PCR

Allow 96-well format

Methods for Large Scale Studies of Gene Expression

• Microarrays

• EST sequencing

• SAGE

Microarrays

EST Sequencing

AAAAAAAAAAAAAAAAAAAA

AAAAAAAAAA

AAAAAAAAAATTTTTTTTTTT

Reverse Transcription

Sequence large number of clones

Prepare RNA

Tissue of interest

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

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

Serial Analysis of Gene Expression (SAGE)Prepare RNA AAAAAAAAAA

AAAAAAAAAA

AAAAAAAAAA

Create Tags

Tissue of interest

Ligate Tags

Sequence concatemers and analyze tag frequency

Results From a SAGE Experiment

Transcripts Transcripts

Normal Disease

Ab

s.

Levels

Cancer Research

Cancer and Aging

Life expectancyRoman empire: 25 yearsMiddle ages: 33 years1850: 45 yearsU.S. in 2000: 75 years

Ovarian Cancer

• Believed to originate from a single layer of epithelial cells covering the ovaries

• 25,000 new cases in the U.S. in 2004• 15,000 will die of the disease• Most cases diagnosed as advanced disease• No reliable sensitive markers for early

detection

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

Ovarian Cancer Staging

Stage 1 Stage III

Serous Adenocarcinoma Tumor

Therapy

• Standard chemotherapy: cisplatin and taxol

• Half the cases intrinsically resistant

• Many tumors develop resistance to cisplatin

• Mechanisms of drug resistance are unknown

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

Strategy

Perform SAGE on:

1) Normal ovarian epithelium2) Ovarian tumors

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 19,860 384,497 82,533 56,387 28,219

Hough et al. (2000) Cancer Res.

Top 12 Genes Expressed in ES-2

1 TGCAGTCACT 1.25% Collagenase2 TGTGTTGAGA 0.95 % EF-1 3 CCCATCGTCC 0.63 % Cytochrome C oxidase II 4 ATGGCTGGTA 0.59 % Ribosomal S25 GTGAAACCCC 0.52 % GM-CSF receptor CD826 AAGACAGTGG 0.49 % Ribosomal L37a7 AGCACCTCCA 0.48 % EF-28 GCCGGGTGGG 0.43 % Collagenase Stimul. Fact.9 GGATTTGGCC 0.43% Qip110 CCTGTAATCC 0.40 % Gz-selective GTPase-act prt11 TCCAAATCGA 0.39 % Vimentin12 CCCGTCCGGA 0.38 % Ribosomal L13 (EST)

Tag Exp. Level GeneRank

Normal ovary

ovarian cancer

Gene expression differences

Identifying Gene Differentially Expressed in Ovarian Cancer

Genes Consistently Up-regulated

up-regulated gene Fold Function

HLA-DR chain 289 Major histocompatibility complex, class II Cysteine-rich protein 1 123 LIM/double zinc finger Claudin-4 109 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/ Ca2+- independent CAM/ proliferation Mucin 1 43 Tumor Ag/ Type- I membrane glycoprotein

Immunostaining

Allows specific staining of the tumor for the expression of aprotein of interest

Staining of Ovarian Cancer with an Ep-CAM antibody

Immunotherapy

Blood Test For Early Detection

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