Target Discovery and Validation Reviews and Protocols

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Fig. 2. (Continued) the right identify the locations of the inserts displayed in 1c–j. (B) Dendrogram representing similarities in the expression patterns between experimental samples. All tumor pairs that clustered together on terminal branches are highlighted in red, the two primary tumor/lymph node metastasis pairs are highlighted in light blue, and the three clustered normal breast samples are highlighted in light green. The branches representing the four breast luminal epithelial cell lines are displayed in dark blue, breast basal epithelial cell lines are displayed in orange, the endothelial cell lines in dark yellow, the mesynchemal-like cell lines in dark green, and the lymphocyte-derived cell lines in brown. (C) Endothelial cell gene expression cluster. (D) Stromal/fibroblast cluster. (E) Breast basal epithelial cluster. (F) B-cell cluster. (G) Adipose-enriched/normal breast. (H) Macrophage. (I) T-cell. (J) Breast luminal epithelial cell. Adapted from ref. 14.
98 Sørlie are not internally heterogeneous, when millions of cells are sampled. The finding that the two primary tumors and the corresponding metastases were similar in their overall pattern of gene expression suggests that the molecular program of a primary tumor may be retained in its metastases. This idea has been explored by other investigators and suggests that metastatic capacity is an intrinsic feature of the primary tumor and that the fate of the tumor might have been programmed at an early stage of the disease (21). Hence, specific expression patterns in the primary tumors may help identifying genes, pathways, or both that are important for the metastatic process and for tumor progression in general. 3.2. Subclassification To explore the possibilities for further refining these distinctions among subtypes of breast tumors, we took advantage of the paired tumor samples. The specific features of a gene expression pattern that are to be used as the basis for classifying tumors should be similar in any sample taken from the same tumor, and they should vary among different tumors. The paired samples therefore provided a unique opportunity for a deliberate and systematic search for genes whose expression levels reflected such intrinsic characteristics of the tumors. Using well measured expression data from the paired samples, a subset of genes termed “intrinsic gene set” was selected that consisted of genes with significantly greater variation in expression between different tumors than between paired samples from the same tumor (14). The rationale behind this list of genes is that it is enriched for those genes whose expression patterns were characteristic of each tumor as opposed to those that varied as a function of tissue sampling; hence, they would be ideally suited for classification. Our extended analyses have since included an intrinsic gene set of 540 genes selected from expression data of 45 tumor pairs (including two primary lymph node pairs) and approx 8000 common genes for all samples (22,23). Together, 122 tissue samples were included in the clustering analysis: 115 carcinomas and 7 nonmalignant tissues. Most of the tumors were sampled as part of two independent studies evaluating response to chemotherapy of locally advanced breast cancer in an adjuvant setting. From the first cohort of patients treated with doxorubicin monotherapy (19), 55 tumor samples were analyzed, and from the second cohort of patients treated with 5-fluorouracil and mitomycin C (20), 34 tumor samples were analyzed. The remaining 26 samples were primary tumor specimens collected either at Stanford or in Norway. The overall expression patterns showed that the tumors could be classified into five distinct subtypes, and the main distinction was between tumors that expressed genes characteristic of luminal epithelial cells (two groups), including the estrogen receptor (ER) and those that were negative for these genes (three groups) (Fig. 3). Among the luminal epithelial-type tumors, the largest group,
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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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44 Imoto et al. β k (k = 1,…,q j
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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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222 Vijayaraj, Söhl, and Magin 1.2
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226 Vijayaraj, Söhl, and Magin gen
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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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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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