Molecular Profiling of Breast Cancer 111 15. Perou, C. M., Jeffrey, S. S., van de Rijn, M., et al. (1999) Distinctive gene expression patterns in human mammary epithelial cells <strong>and</strong> breast cancers. Proc. Natl. Acad. Sci. USA 96, 9212–9217. 16. Ross, D. T., Scherf, U., Eisen, M. B., et al. (2000) Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet. 24, 227–235. 17. Whitfield, M. L., Sherlock, G., Saldanha, A. J., et al. (2002) Identification of genes periodically expressed in the human cell cycle <strong>and</strong> their expression in tumors. Mol. Biol. Cell 13, 1977–2000. 18. Aas, T., Borresen, A. L., Geisler, S., et al. (1996) Specific P53 mutations are associated with de novo resistance to doxorubicin in breast cancer patients. Nat. Med. 2, 811–814. 19. Geisler, SI., Lonning, P. E., Aas, T., et al. (2001) Influence of TP53 gene alterations <strong>and</strong> c-erbB-2 expression on the response to treatment with doxorubicin in locally advanced breast cancer. Cancer Res. 61, 2505–2512. 20. Geisler, S., Borresen-Dale, A. L., Johnsen, H., et al. (2003) TP53 Gene Mutations Predict the Response to Neoadjuvant Treatment with 5-Fluorouracil <strong>and</strong> Mitomycin in Locally Advanced Breast Cancer. Clin. Cancer Res. 9, 5582–5588. 21. Weigelt, B., Glas, A. M., Wessels, L. F., Witteveen, A. T., Peterse, J. L., <strong>and</strong> van’t Veer, L. J. (2003) Gene expression profiles of primary breast tumors maintained in distant metastases. Proc. Natl. Acad. Sci. USA 100, 15, 901–15, 905. 22. Sorlie, T., Tibshirani, R., Parker, J., et al. (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl. Acad. Sci. USA 100, 8418–8423. 23. Sorlie, T., Perou, C. M., Tibshirani, R., et al. (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 98, 10, 869–10, 874. 24. Bertucci, F., Finetti, P., Rougemont, J., et al. (2005) Gene expression profiling identifies molecular subtypes of inflammatory breast cancer. Cancer Res. 65, 2170–2178. 25. Chang, H. Y., Nuyten, D. S., Sneddon, J. B., et al. (2005) Robustness, scalability, <strong>and</strong> integration of a wound-response gene expression signature in predicting breast cancer survival. Proc. Natl. Acad. Sci. USA 102, 3738–3743. 26. Sotiriou, C., Neo, S. Y., McShane, L. M., et al. (2003) Breast cancer classification <strong>and</strong> prognosis based on gene expression profiles from a population-based study. Proc. Natl. Acad. Sci. USA 100, 10,393–10,398. 27. Wang, Z. C., Lin, M., Wei, L. J., et al. (2004) Loss of heterozygosity <strong>and</strong> its correlation with expression profiles in subclasses of invasive breast cancers. Cancer Res. 64, 64–71. 28. Yu, K., Lee, C. H., Tan, P. H., <strong>and</strong> Tan, P. (2004) Conservation of breast cancer molecular subtypes <strong>and</strong> transcriptional patterns of tumor progression across distinct ethnic populations. Clin. Cancer Res. 10, 5508–5517. 29. Zhao, H., Langerod, A., Ji, Y., et al. (2004) Different gene expression patterns in invasive lobular <strong>and</strong> ductal carcinomas of the breast. Mol. Biol. Cell 15, 2523–2536. 30. van’t Veer, L. J., Dai, H., van de Vijver, M. J., et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536.
112 Sørlie 31. Slamon, D. J., Clark, G. M., Wong, S. G., Levin, W. J., Ullrich, A., <strong>and</strong> McGuire, W. L. (1987) Human breast cancer: correlation of relapse <strong>and</strong> survival with amplification of the HER-2/neu oncogene. Science 235, 177–182. 32. Ahr, A., Karn, T., Solbach, C., et al. (2002) Identification of high risk breast-cancer patients by gene expression profiling. Lancet 359, 131–132. 33. Huang, E., Cheng, S. H., Dressman, H., et al. (2003) Gene expression predictors of breast cancer outcomes. Lancet 361, 1590–1596. 34. Ma, X. J., Wang, Z., Ryan, P. D., et al. (2004) A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5, 607–616. 35. van de Vijver, M. J., He, Y. D., van’t Veer, L. J., et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009. 36. Onda, M., Emi, M., Nagai, H., et al. (2004) Gene expression patterns as marker for 5–year postoperative prognosis of primary breast cancers. J. Cancer Res. Clin. Oncol. 130, 537–545. 37. Nagahata, T., Onda, M., Emi, M., et al. (2004) Expression profiling to predict postoperative prognosis for estrogen receptor-negative breast cancers by analysis of 25, 344 genes on a cDNA microarray. Cancer Sci. 95, 218–225. 38. Eden, P., Ritz, C., Rose, C., Ferno, M., <strong>and</strong> Peterson, C. (2004) “Good Old” clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers. Eur. J. Cancer 40, 1837–1841. 39. Sasco, A. J. (2003) Breast cancer <strong>and</strong> the environment. Horm. Res. 60 Suppl 3, 50. 40. Early Breast Cancer Trialists’ Collaborative Group (1998) Tamoxifen for early breast cancer: an overview of the r<strong>and</strong>omised trials. Early Breast Cancer Trialists’ Collaborative Group. Lancet 351, 1451–1467. 41. Citron, M. L., Berry, D. A., Cirrincione, C., et al. (2003) R<strong>and</strong>omized trial of dose-dense versus conventionally scheduled <strong>and</strong> sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer <strong>and</strong> Leukemia Group B Trial 9741. J. Clin. Oncol. 21, 1431–1439. 42. Ayers, M., Symmans, W. F., Stec, J., et al. (2004) Gene Expression Profiles Predict Complete Pathologic Response to Neoadjuvant Paclitaxel <strong>and</strong> Fluorouracil, Doxorubicin, <strong>and</strong> Cyclophosphamide Chemotherapy in Breast Cancer. J. Clin. Oncol. 22, 2284–2293. 43. Chang, J. C., Wooten, E. C., Tsimelzon, A., et al. (2003) Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362, 362–369. 44. Iwao-Koizumi, K., Matoba, R., Ueno, N., et al. (2005) Prediction of docetaxel response in human breast cancer by gene expression profiling. J. Clin. Oncol. 23, 422–431. 45. Jansen, M. P., Foekens, J. A., van Staveren, I. L., et al. (2005) Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J. Clin. Oncol. 23, 732–740.
- Page 1 and 2:
METHODS IN MOLECULAR BIOLOGY 360 T
- Page 3 and 4:
M E T H O D S I N M O L E C U L A R
- Page 5 and 6:
© 2007 Humana Press Inc. 999 River
- Page 7 and 8:
vi Preface get. Approaches to targe
- Page 9 and 10:
viii Contents 10 Transgenic Animal
- Page 11 and 12:
x Contributors SAHOHIME MATSUMOTO
- Page 13 and 14:
xii Contents of Volume 2 12 Validat
- Page 15 and 16:
2 Sioud disease pathogenesis and pe
- Page 17 and 18:
4 Sioud A more fruitful approach fo
- Page 19 and 20:
6 Sioud both transient and permanen
- Page 21 and 22:
8 Sioud serum biomarkers. This syne
- Page 23 and 24:
10 Sioud Fig. 2. Schematic of targe
- Page 25 and 26:
12 Sioud 13. Magin, T. M. (1998) Le
- Page 28 and 29:
Harnessing the Human Genome 15 The
- Page 30 and 31:
Harnessing the Human Genome 17 Fig.
- Page 32 and 33:
Harnessing the Human Genome 19 Fig.
- Page 34 and 35:
Harnessing the Human Genome 21 Fig.
- Page 36 and 37:
Harnessing the Human Genome 23 The
- Page 38 and 39:
Harnessing the Human Genome 25 Fig.
- Page 40 and 41:
Harnessing the Human Genome 27 repr
- Page 42 and 43:
Harnessing the Human Genome 29 14.
- Page 44:
Harnessing the Human Genome 31 45.
- Page 47 and 48:
34 Imoto et al. (4,5), and Bayesian
- Page 49 and 50:
36 Imoto et al. 2. Materials Two ty
- Page 51 and 52:
38 Imoto et al. Fig. 3. Graphical v
- Page 53 and 54:
40 Imoto et al. Fig. 5. Result of t
- Page 55 and 56:
42 Imoto et al. p(D |G) = ∫ p(D,
- Page 57 and 58:
44 Imoto et al. β k (k = 1,…,q j
- Page 59 and 60:
46 Imoto et al. Fig. 7. Partial res
- Page 61 and 62:
48 Imoto et al. Fig. 8. Strategy to
- Page 63 and 64:
50 Imoto et al. Fig. 9. (continued)
- Page 65 and 66:
52 Imoto et al. Table 3 List of Roo
- Page 67 and 68:
54 Imoto et al. 5. De Hoon, M. J. L
- Page 69 and 70:
56 Imoto et al. 39. Kamimura, T., S
- Page 71 and 72:
58 Beaty et al. cytotoxic drugs and
- Page 73 and 74: 60 Beaty et al. Fig. 1. Principles
- Page 75 and 76: 62 Beaty et al. oligonucleotide mic
- Page 77 and 78: 64 Beaty et al. 3. The 12% polyacry
- Page 79 and 80: 66 Beaty et al. 2.3.3. Protein Stai
- Page 81 and 82: 68 Beaty et al. 3.1.3. Labeling of
- Page 83 and 84: 70 Beaty et al. 1 µL of the anneal
- Page 85 and 86: 72 Beaty et al. 4. Phenol:cholorfor
- Page 87 and 88: 74 Beaty et al. 19. Wash twice with
- Page 89 and 90: 76 Beaty et al. The Following Volum
- Page 91 and 92: 78 Beaty et al. using a protein ass
- Page 93 and 94: 80 Beaty et al. 6. Shake the gel fo
- Page 95 and 96: 82 Beaty et al. Fig. 2. Preparation
- Page 97 and 98: 84 Beaty et al. search in. A widely
- Page 99 and 100: 86 Beaty et al. 3. Kern, S. E., Hru
- Page 101 and 102: 88 Beaty et al. 34. Peters, D. G.,
- Page 104 and 105: 5 Molecular Classification of Breas
- Page 106 and 107: Molecular Profiling of Breast Cance
- Page 108 and 109: Molecular Profiling of Breast Cance
- Page 110 and 111: Fig. 2. (Continued) the right ident
- Page 112 and 113: Molecular Profiling of Breast Cance
- Page 114 and 115: Molecular Profiling of Breast Cance
- Page 116 and 117: Molecular Profiling of Breast Cance
- Page 118 and 119: Molecular Profiling of Breast Cance
- Page 120 and 121: Molecular Profiling of Breast Cance
- Page 122 and 123: Molecular Profiling of Breast Cance
- Page 126 and 127: Molecular Profiling of Breast Cance
- Page 128 and 129: 6 Discovery of Differentially Expre
- Page 130 and 131: Gene Target Discovery 117 In the fi
- Page 132 and 133: Gene Target Discovery 119 sequences
- Page 134 and 135: Gene Target Discovery 121 There is
- Page 136 and 137: Gene Target Discovery 123 digestion
- Page 138 and 139: Gene Target Discovery 125 Fig. 1. P
- Page 140 and 141: Gene Target Discovery 127 5. Sargen
- Page 142: Gene Target Discovery 129 41. Berry
- Page 145 and 146: 132 Matsumoto, Miyagishi, and Taira
- Page 147 and 148: 134 Matsumoto, Miyagishi, and Taira
- Page 149 and 150: 136 Matsumoto, Miyagishi, and Taira
- Page 151 and 152: 138 Matsumoto, Miyagishi, and Taira
- Page 153 and 154: 140 Matsumoto, Miyagishi, and Taira
- Page 155 and 156: 142 Matsumoto, Miyagishi, and Taira
- Page 157 and 158: 144 Fig. 1. The ribozyme-expression
- Page 159 and 160: 146 Sano and Taira 5. 10X L (low-sa
- Page 161 and 162: 148 Sano and Taira ribozymes may cl
- Page 163 and 164: 150 Fig. 3. A system for the identi
- Page 165 and 166: 152 Sano and Taira 4. Notes 1. We r
- Page 168 and 169: 9 Production of siRNA- and cDNA-Tra
- Page 170 and 171: Transfected Cell Arrays 157 Fig. 1.
- Page 172 and 173: Transfected Cell Arrays 159 2. The
- Page 174:
Transfected Cell Arrays 161 5. Simp
- Page 177 and 178:
164 Houdebine Fig. 1. Different met
- Page 179 and 180:
166 Houdebine concentrations become
- Page 181 and 182:
168 Houdebine Fig. 2. Major methods
- Page 183 and 184:
170 Houdebine integrate into the me
- Page 185 and 186:
172 Houdebine 2.2.3. Knock-In Into
- Page 187 and 188:
174 Houdebine as insulators. The co
- Page 189 and 190:
176 Houdebine Fig. 5. Different met
- Page 191 and 192:
178 Houdebine systems. Moreover, st
- Page 193 and 194:
180 Houdebine contribute to generat
- Page 195 and 196:
182 Houdebine Fig.7. Example of a v
- Page 197 and 198:
184 Houdebine more extensively. Tra
- Page 199 and 200:
186 Houdebine and stroma. Several o
- Page 201 and 202:
188 Houdebine explained at the mole
- Page 203 and 204:
190 Houdebine of the intestinal epi
- Page 205 and 206:
192 Houdebine interference of the g
- Page 207 and 208:
194 Houdebine 17. Whitelaw, C. B. A
- Page 209 and 210:
196 Houdebine 51. Bell, A. C., West
- Page 211 and 212:
198 Houdebine 86. Carmichael, G. G.
- Page 213 and 214:
200 Houdebine 121. Pailhoux, E., Vi
- Page 215 and 216:
202 Houdebine 156. Auerbach, A. B.,
- Page 217 and 218:
204 Vijayaraj, Söhl, and Magin 1.
- Page 219 and 220:
206 Vijayaraj, Söhl, and Magin Fig
- Page 221 and 222:
208 Vijayaraj, Söhl, and Magin fil
- Page 223 and 224:
210 Table 1 Orthologous Human and M
- Page 225 and 226:
212 Table 2 Orthologous Human and M
- Page 227 and 228:
214 Vijayaraj, Söhl, and Magin ulc
- Page 229 and 230:
216 Vijayaraj, Söhl, and Magin of
- Page 231 and 232:
218 Vijayaraj, Söhl, and Magin sug
- Page 233 and 234:
220 Vijayaraj, Söhl, and Magin abs
- Page 235 and 236:
222 Vijayaraj, Söhl, and Magin 1.2
- Page 237 and 238:
224 Vijayaraj, Söhl, and Magin Fig
- Page 239 and 240:
226 Vijayaraj, Söhl, and Magin gen
- Page 241 and 242:
228 Vijayaraj, Söhl, and Magin the
- Page 243 and 244:
230 Vijayaraj, Söhl, and Magin f.
- Page 245 and 246:
232 Vijayaraj, Söhl, and Magin 3.
- Page 247 and 248:
234 Vijayaraj, Söhl, and Magin b.
- Page 249 and 250:
236 Vijayaraj, Söhl, and Magin 4.
- Page 251 and 252:
238 Vijayaraj, Söhl, and Magin 1.
- Page 253 and 254:
240 Vijayaraj, Söhl, and Magin alt
- Page 255 and 256:
242 Table 3 Time Schedule For Aggre
- Page 257 and 258:
244 Vijayaraj, Söhl, and Magin 7.
- Page 259 and 260:
246 Vijayaraj, Söhl, and Magin 37.
- Page 261 and 262:
248 Vijayaraj, Söhl, and Magin 68.
- Page 263 and 264:
250 Vijayaraj, Söhl, and Magin 101
- Page 266 and 267:
12 The HUVEC/Matrigel Assay An In V
- Page 269 and 270:
256 Skovseth, Küchler, and Haralds
- Page 271 and 272:
258 Skovseth, Küchler, and Haralds
- Page 273 and 274:
260 Skovseth, Küchler, and Haralds
- Page 275 and 276:
262 Skovseth, Küchler, and Haralds
- Page 277 and 278:
264 Skovseth, Küchler, and Haralds
- Page 279 and 280:
266 Skovseth, Küchler, and Haralds
- Page 281 and 282:
268 Skovseth, Küchler, and Haralds
- Page 283 and 284:
270 Iversen and Sørensen witnessed
- Page 285 and 286:
272 Iversen and Sørensen Fig. 1. P
- Page 287 and 288:
274 Iversen and Sørensen the core
- Page 290 and 291:
14 An Overview of the Immune System
- Page 292 and 293:
Overview of Immune System 279 diffe
- Page 294 and 295:
Overview of Immune System 281 Fig.
- Page 296 and 297:
Overview of Immune System 283 then
- Page 298 and 299:
Overview of Immune System 285 expre
- Page 300 and 301:
Overview of Immune System 287 Once
- Page 302 and 303:
Overview of Immune System 289 solub
- Page 304 and 305:
Overview of Immune System 291 amino
- Page 306 and 307:
Overview of Immune System 293 (unre
- Page 308 and 309:
Overview of Immune System 295 Fig.
- Page 310 and 311:
Overview of Immune System 297 the c
- Page 312 and 313:
Overview of Immune System 299 (44).
- Page 314 and 315:
Overview of Immune System 301 demon
- Page 316 and 317:
Overview of Immune System 303 Fig.
- Page 318 and 319:
Overview of Immune System 305 Fig.
- Page 320 and 321:
Overview of Immune System 307 some
- Page 322 and 323:
Overview of Immune System 309 sera
- Page 324 and 325:
Overview of Immune System 311 Fig.
- Page 326 and 327:
Overview of Immune System 313 measu
- Page 328 and 329:
Overview of Immune System 315 42. H
- Page 330 and 331:
Overview of Immune System 317 74. D
- Page 332 and 333:
15 Potential Target Antigens for Im
- Page 334 and 335:
Tumor Antigen Discovery 321 develop
- Page 336 and 337:
Tumor Antigen Discovery 323 panel b
- Page 338 and 339:
Tumor Antigen Discovery 325 16. Joc
- Page 340 and 341:
16 Identification of Tumor Antigens
- Page 342 and 343:
Immunoproteomics 329 by “shot gun
- Page 344 and 345:
Immunoproteomics 331 isoelectric fo
- Page 346 and 347:
Immunoproteomics 333 3. 2D-PAGE is
- Page 348 and 349:
17 Protein Arrays A Versatile Toolb
- Page 350 and 351:
Protein Arrays 337 Table 1 Classifi
- Page 352 and 353:
Protein Arrays 339 fractions from c
- Page 354 and 355:
Protein Arrays 341 combinatorial sc
- Page 356 and 357:
Protein Arrays 343 The ideal proteo
- Page 358 and 359:
Protein Arrays 345 18. Cekaite, H.
- Page 360 and 361:
Protein Arrays 347 49. Yuko Kawahas
- Page 362 and 363:
Index 349 Index A Acetyl-CoA carbox
- Page 364 and 365:
Index 351 conditional by using FRT
- Page 366 and 367:
Index 353 Recombinant tumor antigen