Target Discovery and Validation Reviews and Protocols

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Molecular Profiling of Breast Cancer 99 Fig. 3. Hierarchical clustering of 115 tumor tissues and seven nonmalignant tissues by using the intrinsic gene set. (A) A scaled-down representation of the entire cluster of 540 genes and 122 tissue samples based on similarities in gene expression. (B) Experimental dendrogram showing the clustering of the tumors into five subgroups. Branches corresponding to tumors with low correlation to any subtype are shown in gray. (C) Gene cluster showing the ERBB2 oncogene and other coexpressed genes. (D) Gene cluster associated with luminal subtype B. (E) Gene cluster associated with the basal subtype. (F) A gene cluster relevant for the normal breast-like group. (G) Cluster of genes including the ER (ESR1) highly expressed in luminal subtype A tumors. Scale bar represents -fold change for any given gene relative to the median level of expression across all samples. Adapted from ref. 22.
100 Sørlie termed luminal subtype A (dark blue branches), demonstrated the highest expression of ER, estrogen-regulated protein LIV-1, and the transcription factors hepatocyte nuclear factor 3, alpha (HNF3A), X-box binding protein 1, and GATA-binding protein 3 (GATA3) (Fig. 3G). The second, smaller group of tumors with luminal epithelial characteristics, termed luminal subtype B (light blue branches), showed low expression of the ER cluster, but it was further distinguished from luminal subtype A by the high expression of a novel set of genes such as GGH, LAPTMB4, NSEP1, and CCNE1, whose coordinated function is unknown (Fig. 3D). Among the three ER-negative groups, the basal epithelial-like subtype (red branches) was characterized by high expression of KRT5 and KRT17, annexin 8, CX3CL1, and TRIM29 and was completely negative for the luminal/ER cluster of genes (Fig. 3E), whereas the ERBB2+ subtype (pink) was characterized by high expression of several genes in the ERBB2 amplicon at 17q22.24, including ERBB2, GRB7, and TRAP100 (Fig. 3C), suggesting that much of the genetic influences are because of gene amplification. A normal breast tissue-like group (green branches) was identified that showed the highest expression of many genes known to be expressed by adipose tissue and other nonepithelial cell types (Fig. 3F). These tumors also showed strong expression of basal epithelial genes and low expression of luminal epithelial genes. However, it is unclear whether these tumors represent poorly sampled tumor tissue or a distinct, clinically important group. In conclusion, the molecular portraits defined by gene expression patterns seem to be stable and homogenous and represent the tumor itself, not merely the particular tumor sample, because we could recognize the distinctive expression profile of a tumor in independent samples. The inherent properties of the tumors thus seem to be sustained throughout chemotherapy as well as between a primary tumor and its lymph node metastasis, and these properties could be represented by a relatively small number of genes whose variation in expression formed a platform for classification. 3.3. Validation The robustness of the tumor subtypes has been validated in independent data sets by us (22) as well as by others (24–29). When conducting a similar analysis of a breast tumor data set published by van’t Veer et al. (30), to display the expression pattern of 460 of the intrinsic genes previously mentioned across their 97 tumor samples, the same subtypes were noted. In particular, the basal-like subtype was the most homogenous group of tumors, tightly clustered together with high correlation to the intrinsic expression centroid representing this phenotype (22). This relationship has been true for all data sets analyzed this far, even though they have comprised different patient populations, indicating that the substantial differences in the characteristics of
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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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46 Imoto et al. Fig. 7. Partial res
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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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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 285 expre
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Overview of Immune System 289 solub
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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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