Target Discovery and Validation Reviews and Protocols

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Molecular Profiling of Breast Cancer 95 Our initial studies of 65 surgical specimens of human breast tissue from 42 individuals by using microarrays representing 8102 genes showed that there was great molecular heterogeneity among the tumors, with multidimensional variation in the patterns of gene expression (14). To help provide a framework for interpreting the variation in expression patterns observed in the tumor samples, 17 cultured cell lines representing models for many of the cell types encountered in these tissue samples also were characterized. A subset of 1753 genes was selected, those genes whose transcripts varied in abundance by at least fourfold from their median abundance in this sample set in at least three samples. Hierarchical clustering was then used to group both genes and tissue samples together based on overall similarity in their expression patterns (Fig. 2). Clusters of genes with coherent expression patterns could be related to specific features of biological variation among the samples, for example, variation in proliferation rates: the largest distinct cluster of genes was the “proliferation cluster,” which is a group of genes whose levels of expression correlate with cellular proliferation rates (15–17). Another large cluster containing genes regulated by the type I and type II interferon signal transduction pathways showed substantial variation in expression among the tumors. Furthermore, clusters of coexpressed genes were identified whose expression patterns derived from different cell types within the grossly dissected tumors, including luminal and basal epithelium, stromal fibroblasts, and lymphocytes (Fig. 2C–J). These findings were corroborated by immunohistochemistry (14). A unique quality of this study was the availability of tumor pairs; 20 tumors were sampled twice as part of a larger prospective study on locally advanced breast cancer (T3/T4, N2 tumors, or both) (18–20). After an open surgical biopsy to obtain the “before” sample, each of these patients was treated with doxorubicin for an average of 15 wk, followed by resection of the remaining tumor, when the “after” sample was obtained. In addition, primary tumors from two patients were paired with a lymph node metastasis from the same individual. A striking result observed in the cluster dendrogram was that most of the tumor pairs (15 of the 20 before and after pairs and both primary tumor/lymph node metastasis pairs) clustered together on terminal branches in the accompanying dendrogram (Fig. 2B). That is, despite the potential confounding effects of an interval of 15 wk, cytotoxic chemotherapy, and different sample preparations, independent samples taken from the same tumor were in most cases recognizably more similar to each other than either was to any of the other samples. This finding implied that every tumor is unique and has a distinctive gene expression signature or portrait. It also implied that the type and numbers of nonepithelial cells in tumors is a remarkably consistent and enduring feature of each individual tumor. Breast tumors thus seem to be very diverse, but they
Fig. 2. Cluster analysis of a subset of genes in 84 experimental samples. Mediancentered hierarchical clustering analysis of cell lines (left) and tissue samples (right). (A) Scaled-down representation of the 1753 gene cluster diagram; the colored bars to
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METHODS IN MOLECULAR BIOLOGY 360 T
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M E T H O D S I N M O L E C U L A R
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© 2007 Humana Press Inc. 999 River
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vi Preface get. Approaches to targe
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viii Contents 10 Transgenic Animal
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x Contributors SAHOHIME MATSUMOTO
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xii Contents of Volume 2 12 Validat
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2 Sioud disease pathogenesis and pe
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4 Sioud A more fruitful approach fo
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6 Sioud both transient and permanen
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8 Sioud serum biomarkers. This syne
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10 Sioud Fig. 2. Schematic of targe
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12 Sioud 13. Magin, T. M. (1998) Le
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Harnessing the Human Genome 15 The
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Harnessing the Human Genome 17 Fig.
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Harnessing the Human Genome 23 The
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Harnessing the Human Genome 27 repr
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Harnessing the Human Genome 29 14.
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Harnessing the Human Genome 31 45.
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34 Imoto et al. (4,5), and Bayesian
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36 Imoto et al. 2. Materials Two ty
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38 Imoto et al. Fig. 3. Graphical v
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40 Imoto et al. Fig. 5. Result of t
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42 Imoto et al. p(D |G) = ∫ p(D,
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146 Sano and Taira 5. 10X L (low-sa
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148 Sano and Taira ribozymes may cl
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150 Fig. 3. A system for the identi
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152 Sano and Taira 4. Notes 1. We r
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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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Transfected Cell Arrays 159 2. The
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Transfected Cell Arrays 161 5. Simp
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164 Houdebine Fig. 1. Different met
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166 Houdebine concentrations become
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168 Houdebine Fig. 2. Major methods
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170 Houdebine integrate into the me
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172 Houdebine 2.2.3. Knock-In Into
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174 Houdebine as insulators. The co
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176 Houdebine Fig. 5. Different met
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178 Houdebine systems. Moreover, st
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180 Houdebine contribute to generat
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182 Houdebine Fig.7. Example of a v
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184 Houdebine more extensively. Tra
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186 Houdebine and stroma. Several o
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188 Houdebine explained at the mole
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190 Houdebine of the intestinal epi
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192 Houdebine interference of the g
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194 Houdebine 17. Whitelaw, C. B. A
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196 Houdebine 51. Bell, A. C., West
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198 Houdebine 86. Carmichael, G. G.
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200 Houdebine 121. Pailhoux, E., Vi
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202 Houdebine 156. Auerbach, A. B.,
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204 Vijayaraj, Söhl, and Magin 1.
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206 Vijayaraj, Söhl, and Magin Fig
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208 Vijayaraj, Söhl, and Magin fil
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210 Table 1 Orthologous Human and M
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212 Table 2 Orthologous Human and M
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214 Vijayaraj, Söhl, and Magin ulc
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216 Vijayaraj, Söhl, and Magin of
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218 Vijayaraj, Söhl, and Magin sug
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220 Vijayaraj, Söhl, and Magin abs
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222 Vijayaraj, Söhl, and Magin 1.2
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224 Vijayaraj, Söhl, and Magin Fig
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226 Vijayaraj, Söhl, and Magin gen
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228 Vijayaraj, Söhl, and Magin the
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230 Vijayaraj, Söhl, and Magin f.
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234 Vijayaraj, Söhl, and Magin b.
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240 Vijayaraj, Söhl, and Magin alt
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242 Table 3 Time Schedule For Aggre
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250 Vijayaraj, Söhl, and Magin 101
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12 The HUVEC/Matrigel Assay An In V
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256 Skovseth, Küchler, and Haralds
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270 Iversen and Sørensen witnessed
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272 Iversen and Sørensen Fig. 1. P
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274 Iversen and Sørensen the core
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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 281 Fig.
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Overview of Immune System 283 then
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Overview of Immune System 285 expre
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Overview of Immune System 287 Once
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Overview of Immune System 289 solub
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Overview of Immune System 291 amino
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Overview of Immune System 293 (unre
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Overview of Immune System 297 the c
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Overview of Immune System 299 (44).
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Overview of Immune System 301 demon
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Overview of Immune System 307 some
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Overview of Immune System 309 sera
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Overview of Immune System 313 measu
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Overview of Immune System 315 42. H
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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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