Target Discovery and Validation Reviews and Protocols

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Molecular Profiling of Breast Cancer 101 the patients are less important determinants of tumor expression phenotypes than intrinsic biology. Our studies also show that although there is variability between microarray platforms, the gene expression profiles emerging from using different technologies are highly correlated to the biological variation in the data, and the same tumor subtype pattern is identified (13a). 4. Clinical Implications A major goal in the field of oncogenomics is to try to answer the clinically important questions about which tumors will behave aggressively, which tumors will remain dormant, which patients do and do not require systemic therapy, and what type of drugs should be used. 4.1. Prognosis To investigate whether the five different tumor subtypes identified by gene expression profiling may represent clinically distinct and relevant groups of patients, univariate survival analyses comparing the subtypes with respect to overall survival (OS) and relapse-free survival (RFS) were performed. (For these analyses, only the patients who were enrolled in the two prospective studies on locally advanced disease were included.) The Kaplan–Meier curves based upon four subclasses (excluding the normal-like group) showed a highly significant difference in OS between the patients belonging to the different subclasses (p < 0.01) (Fig. 4). Specifically, the basal-like and ERBB2+ subtypes were associated with the shortest survival time. Overexpression of the ERBB2 oncoprotein is a well-known prognostic factor associated with poor survival in breast cancer (31), which also was found for the ERBB2+ group defined in this study. Perhaps the most intriguing result was the considerable difference in outcome observed between tumors classified as luminal A versus luminal B; the luminal B group showed a much worse prognosis compared with the luminal A group. Notably, each patient included in the survival analysis and harboring ER-α positive tumors were exposed to tamoxifen subsequent to preoperative chemotherapy and surgery (19,20). The luminal subtype B tumors may represent a clinically distinct group with a different and worse disease course, in particular with respect to relapse, and that may have little effect of tamoxifen treatment. The potential clinical significance of this molecular subtype is further highlighted by the similarities in expression of some of the luminal B genes with the ER-negative tumors in the basal-like and ERBB2+ subtypes, which suggests that high level of expression of these genes is associated with poor disease outcomes. Most microarray studies of breast tumors to date have investigated the prognostic value of the identified gene signatures (26,30,32–37). Together, they reveal not only interesting information but also contradictions with respect to prognosis. Hence, the real value of the prognosis based on gene expression
102 Sørlie Fig. 4. Kaplan–Meier analysis of disease outcome in two patient cohorts. (A) Overall survival for 72 patients with locally advanced breast cancer in the Norway cohort. The normal-like tumor subgroups were omitted from both data sets in this analysis. (B) Time to development of distant metastasis in the 97 sporadic cases from ref. 30. (Adapted from ref. 22.) profiling needs to be further addressed. In a recent study, Eden et al. (38) reported that an optimal combination of “conventional” prognostic factors was of similar value as published gene signatures. The great advantage of gene profiles as opposed to conventional prognostic factors is the ability of the biological causes, beyond the mere empirical observations, to occur, and to be able to dissect the molecular biology discriminating different tumor forms (1).
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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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48 Imoto et al. Fig. 8. Strategy to
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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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224 Vijayaraj, Söhl, and Magin Fig
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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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