Cancer Genome Anatomy Project
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The Cancer Genome Anatomy Project (CGAP), created by the National Cancer Institute in 1997 and introduced by Al Gore, is an online database on normal, pre-cancerous and cancerous genomes. It also provides tools for viewing and analysis of the data, allowing for identification of genes involved in various aspects of tumor progression. The goal of CGAP is to decipher the molecular anatomy of the cancerous cell, with all data generated readily available to the public with no restrictions. There is also a focus on development of software tools that improve the usage of large and complex datasets.[1][2] The project is directed by Daniela S. Gerhard, and includes sub-projects, with notable ones including the Cancer Chromosome Aberration Project (CCAP) and the Genetic Annotation Initiative (GAI).
The eventual outcomes of CGAP include establishing a correlation between a particular cancer's progression with its therapeutic outcome, improved evaluation of treatment and development of novel techniques for prevention, detection and treamtent. This is achieved by characterisation of biological tissue mRNA products.
Research
Background
The fundamental cause of cancer is the inability for a cell to regulate its gene expression. To characterise a specific type of cancer, the proteins that are produced from the altered gene expression or the mRNA precursor to the protein can be examined. CGAP works to associate a particular cell's expression profile, or molecular signature, which is essentially the cell's fingerprint, with the cell's phenotype. Therefore expression profiles exist with consideration to cancer type and stage of progression.[3]
Techniques
CGAP's initial goal was to establish a Tumor Gene Index (TGI) to store the expression profiles. This would have contributions to both new and existing databases.[4] This produced two sets of libraries, the dbEST and later SAGE. This was performed in a series of steps:[5]
- Cell contents are washed over plates with poly T sequences. This will bind Poly A tails that exist only on mRNA molecules, therefore selectively keeping mRNA.
- The isolated mRNA is processed into a cDNA transcript through reverse transcription and DNA polymerisation reactions.
- The resulting double stranded DNA is then incorporated into E.coli plasmids. Each bacterium now contains one unique cDNA and is replicated to produce clones with the same genetic information. This is termed a cDNA library.
- The library can then sequenced by high-throughput sequencing techniques. This can characterise both the different genes expressed by the original cell and the amount of expression of each gene.
The TGI focused on prostate, breast, ovarian, lung and colon cancers at first, and CGAP extended to other cancers in its research. Practically, issues arose which CGAP accounted for as new technologies became available.
Many cancers occur in tissues with multiple cell types. Traditional techniques took the whole tissue sample and produced bulk tissue cDNA libraries. This cellular heterogeneity made gene expression information in terms of cancer biology less accurate. An example is prostate cancer tissue, where epithelial cells have been shown to give rise to cancer, only consist 10% of the cell count. This lead to development of laser capture microdissection (LCM), which can isolate individual cell types individual cells, which gave rise to cDNA libraries of specific cell types.[4]
The sequencing of cDNA will produce the entire mRNA transcript that generated it. Practically, only part of the sequence is required to uniquely identify the mRNA or protein associated. The resultant part of the sequence was termed the expressed sequence tag (EST) and is always at the end of the sequence close to the poly A tail. ESTs only need to be around 400 bases long, but with NGS sequencing techniques this will still produce low quality reads. Therefore, another method called serial analysis of gene expression(SAGE) is used. This method identifies, for each cDNA transcript molecule produced from a cell's gene expression, region only 10-14 bases long anywhere along the read sequence, sufficient to uniquely identify that cDNA transcript. These bases are cut out and linked together, then incorporated into bacterial plasmids as mentioned above. SAGE libraries have better read quality and generate a larger amount of data when sequenced. [4][6]
See also
- Cancer Genome Project at the Wellcome Trust Sanger Institute
- International Cancer Genome Consortium
- The Cancer Genome Atlas
References
- ^ Riggins, G. J. (2001). "Genome and genetic resources from the Cancer Genome Anatomy Project". Human Molecular Genetics. 10 (7): 663–667. doi:10.1093/hmg/10.7.663. ISSN 1460-2083.
- ^ Strausberg, Robert L.; Buetow, Kenneth H.; Emmert-Buck, Michael R.; Klausner, Richard D. (2000). "The Cancer Genome Anatomy Project: building an annotated gene index". Trends in Genetics. 16 (3): 103–106. doi:10.1016/S0168-9525(99)01937-X. ISSN 0168-9525.
- ^ "Understanding Cancer". Retrieved 2014-09-04.
- ^ a b c Krizman, David B.; Wagner, Lukas; Lash, Alex; Strausberg, Robert L.; Emmert-Buck, Michael R. (1999). "The Cancer Genome Anatomy Project: EST Sequencing and the Genetics of Cancer Progression". Neoplasia. 1 (2): 101–106. doi:10.1038/sj.neo.7900002. ISSN 1476-5586.
- ^ "Understanding Cancer". Retrieved 2014-09-04.
- ^ "Description of SAGE". Retrieved 2014-09-06.
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