Target Discovery and Validation Reviews and Protocols

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Harnessing the Human Genome 15 The Cancer Gene Anatomy Project (CGAP) database (http://cgap.nci. nih.gov/) of the National Cancer Institute (NCI) is an attractive starting point for cancer-specific gene discovery (12). The Human Tumor Gene Index was initiated by the NCI in 1997 with a primary goal of identifying genes expressed during development of human tumors in five major cancer sites: (1) breast, (2) colon, (3) lung, (4) ovary, and (5) prostate. This database consists of expression information (mRNA) of thousands of known and novel genes in diverse normal and tumor tissues. By monitoring the electronic expression profile of many of these sequences, it is possible to compile a list of genes that are selectively expressed in the cancers. Data-mining tools are becoming available to extract expression information about the ESTs derived from various CGAP libraries (10,13–15). Currently, there are 1.5 million ESTs in the CGAP database, of which 73,000 are novel sequences. These sequences also are subclassified into those derived from libraries of normal, precancerous, or cancer tissues. The CGAP database uses UniGene-based gene clustering. UniGene (http://www.ncbi.nlm.nih.gov/ entrez/query.fcgi?db=unigene) is an experimental system for automatically partitioning GenBank sequences into a nonredundant set of gene-oriented clusters. Each UniGene cluster contains sequences that represent a unique gene. An essential weakness of the UniGene is that currently it does not allow for the identification of consensus or contig sequences of a gene. In addition, the UniGene database is continuously updated and hence the ESTs are often removed and reassigned to another UniGene. However, the contig or the consensus (Tentative Human Consensus, TC/THC) information can be obtained from other databases such as TIGR (http://www.tigr.org/). Multiple data-mining tools from the CGAP database can be used to facilitate the gene discovery. 2. Gene Discovery Strategy An overall strategy for cancer gene discovery by using bioinformatics approaches is shown in Fig. 1. Survey sequencing of mRNA gene products can provide an indirect means of generating gene expression fingerprints for cancer cells and their normal counterparts. The cancer specificity of an EST can be predicted using multiple data-mining tools from the CGAP database. These tools include X-profiling, digital differential display (DDD), serial analysis of gene expression (SAGE), electronic-PCR, GeneMap, and microarray databases (see Chapters 1, 5, 6, Volume 1). For details of these tools, see the CGAP website. Fig. 1. (Opposite page) Gene discovery strategy. A proposed approach to cancer gene discovery from the CGAP database is shown. Both novel and known ESTs are identified using multiple data-mining tools from this database. Further validation in the wet laboratory provides a rational for diagnostic and therapeutic target discovery.
16 Narayanan Recently, a tool called digital analysis of gene expression has replaced DDD in the CGAP database; however, the DDD tool can still be accessed from the UniGene page. Briefly, these tools allow prediction of ESTs specific to each cancer type by comparing the occurrence of an EST between two pools of cDNA libraries in a statistically significant manner. The approaches to doing follow-up studies on novel or known ESTs may differ. The novel ESTs can be subject to additional data-mining to identify functional motifs by using the protein databases from the ExPASy (http://au.expasy.org/) server. For a novel EST, a hint of its function also is obtained from homologue-related information from a model organism database such as Homologene (http://www.ncbi.nlm.nih.gov/ entrez/query.fcgi?db= homologene), whereas the known ESTs can be subjected to literature-mining to develop hints of relevance to specific cancer types. The chosen ESTs (hits) can be validated by reverse transcription (RT)-PCR by using cDNAs from normal and tumor tissues as well as appropriate cell lines for specificity and regulation. The expression specificity of the validated ESTs (leads) provides a rationale for developing a diagnostic application. The drug therapy use can be inferred using numerous techniques, such as antisense, small-interfering RNA, and so on. 3. Example of Cancer Gene Discovery To identify solid tumor-specific genes, the DDD tool of the CGAP database was used. Both novel and known ESTs that are selectively up- or downregulated in six major solid tumor types (breast, colon, lung, ovary, pancreas, and prostate) were identified. DDD takes advantage of the UniGene database by comparing the number of times ESTs from different libraries were assigned to a particular UniGene cluster. Six different solid tumor-derived EST libraries (breast, colon, lung, ovary, pancreas, and prostate) with corresponding normal tissue-derived libraries were chosen for DDD (N = 110). To identify tumorand organ-specific ESTs, all the other organ- and tumor-derived EST libraries (N = 327) were chosen for comparison with each of the six tumor types. The nature of the libraries (normal, pretumor, or tumor) was authenticated by comparison of the CGAP data with the UniGene database. Occasionally, the description of the EST libraries in CGAP and UniGene database do not show a match. Those few libraries showing discrepancies of definition between the two databases were excluded. The DDD was performed for each organ type individually. DDD was performed using ESTs from tumors (pool A) and corresponding normal organ (pool B) by using the online tool. The output provided a numerical value in each pool denoting the fraction of sequences within the pool that mapped to the UniGene cluster, providing a dot intensity. An example of DDD output for colon tumor-specific gene discovery by DDD is shown in Fig. 2.
Page 1 and 2: METHODS IN MOLECULAR BIOLOGY 360 T
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Page 7 and 8: vi Preface get. Approaches to targe
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5 Molecular Classification of Breas
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Molecular Profiling of Breast Cance
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Fig. 2. (Continued) the right ident
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6 Discovery of Differentially Expre
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Gene Target Discovery 117 In the fi
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Gene Target Discovery 119 sequences
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Gene Target Discovery 121 There is
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Gene Target Discovery 123 digestion
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Gene Target Discovery 125 Fig. 1. P
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Gene Target Discovery 127 5. Sargen
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Gene Target Discovery 129 41. Berry
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9 Production of siRNA- and cDNA-Tra
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Transfected Cell Arrays 157 Fig. 1.
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164 Houdebine Fig. 1. Different met
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12 The HUVEC/Matrigel Assay An In V
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14 An Overview of the Immune System
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Overview of Immune System 279 diffe
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Overview of Immune System 317 74. D
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15 Potential Target Antigens for Im
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Tumor Antigen Discovery 321 develop
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Tumor Antigen Discovery 323 panel b
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Tumor Antigen Discovery 325 16. Joc
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16 Identification of Tumor Antigens
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Immunoproteomics 329 by “shot gun
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Immunoproteomics 331 isoelectric fo
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Immunoproteomics 333 3. 2D-PAGE is
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17 Protein Arrays A Versatile Toolb
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Protein Arrays 337 Table 1 Classifi
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Protein Arrays 339 fractions from c
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Protein Arrays 341 combinatorial sc
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Protein Arrays 343 The ideal proteo
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Protein Arrays 345 18. Cekaite, H.
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Protein Arrays 347 49. Yuko Kawahas
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Index 349 Index A Acetyl-CoA carbox
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Index 351 conditional by using FRT
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Index 353 Recombinant tumor antigen
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