Breast cancer research has paved the way of personalized oncology with the introduction of hormonal therapy and the measurement of estrogen receptor as the first widely accepted clinical biomarker. The expression of another receptor—HER2/ERBB2/neu—was initially a sign of worse prognosis, but targeted therapy has granted improved outcome for these patients so that today HER2 positive patients have better prognosis than HER2 negative patients. Later, the introduction of multigene assays provided the pathologists with an unbiased assessment of the tumors’ molecular fingerprint. The recent FDA approval of complete microarray pipelines has opened new possibilities for the objective classification of breast cancer samples. Here we review the applications of microarrays for determining ER and HER2 status, molecular subtypes as well as predicting prognosis and grade for breast cancer patients. An open question remains the role of single genes within such signatures. Openly available microarray datasets enable the execution of an independent cross-validation of new marker and signature candidates. In summary, we review the current state regarding clinical applications of microarrays in breast cancer molecular pathology.
References
[1]
Davies, C.; Godwin, J.; Gray, R.; Clarke, M.; Cutter, D.; Darby, S.; McGale, P.; Pan, H.C.; Taylor, C.; Wang, Y.C.; et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: Patient-level meta-analysis of randomised trials. Lancet 2011, 378, 771–784.
[2]
Early Breast Cancer Trialists’ Collaborative Group. Tamoxifen for early breast cancer: An overview of the randomised trials. Lancet 1998, 351, 1451–1467, doi:10.1016/S0140-6736(97)11423-4.
[3]
Brufsky, A. Trastuzumab-based therapy for patients with HER2-positive breast cancer: From early scientific development to foundation of care. Am. J. Clin. Oncol. 2010, 33, 186–195.
[4]
Dawood, S.; Broglio, K.; Buzdar, A.U.; Hortobagyi, G.N.; Giordano, S.H. Prognosis of women with metastatic breast cancer by HER2 status and trastuzumab treatment: An institutional-based review. J. Clin. Oncol. 2010, 28, 92–98, doi:10.1200/JCO.2008.19.9844.
[5]
Layfield, L.J.; Goldstein, N.; Perkinson, K.R.; Proia, A.D. Interlaboratory variation in results from immunohistochemical assessment of estrogen receptor status. Breast J. 2003, 9, 257–259, doi:10.1046/j.1524-4741.2003.09325.x.
[6]
Rhodes, A.; Jasani, B.; Balaton, A.J.; Barnes, D.M.; Miller, K.D. Frequency of oestrogen and progesterone receptor positivity by immunohistochemical analysis in 7,016 breast carcinomas: Correlation with patient age, assay sensitivity, threshold value, and mammographic screening. J. Clin. Pathol. 2000, 53, 688–696, doi:10.1136/jcp.53.9.688.
[7]
Rhodes, A.; Jasani, B.; Balaton, A.J.; Barnes, D.M.; Anderson, E.; Bobrow, L.G.; Miller, K.D. Study of interlaboratory reliability and reproducibility of estrogen and progesterone receptor assays in Europe. Documentation of poor reliability and identification of insufficient microwave antigen retrieval time as a major contributory element of unreliable assays. Am. J. Clin. Pathol. 2001, 115, 44–58, doi:10.1309/H905-HYC1-6UQQ-981P.
[8]
Grabau, D.A.; Bendahl, P.O.; Ryden, L.; Stal, O.; Ferno, M. The prevalence of immunohistochemically determined oestrogen receptor positivity in primary breast cancer is dependent on the choice of antibody and method of heat-induced epitope retrieval—Prognostic implications? Acta Oncol. 2013, doi:10.3109/0284186X.2012.762994.
[9]
Atkinson, R.; Mollerup, J.; Laenkholm, A.V.; Verardo, M.; Hawes, D.; Commins, D.; Engvad, B.; Correa, A.; Ehlers, C.C.; Nielsen, K.V. Effects of the change in cutoff values for human epidermal growth factor receptor 2 status by immunohistochemistry and fluorescence in situ hybridization: A study comparing conventional brightfield microscopy, image analysis-assisted microscopy, and interobserver variation. Arch. Pathol. Lab. Med. 2011, 135, 1010–1016, doi:10.5858/2010-0462-OAR.
[10]
Paik, S.; Shak, S.; Tang, G.; Kim, C.; Baker, J.; Cronin, M.; Baehner, F.L.; Walker, M.G.; Watson, D.; Park, T.; et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 2004, 351, 2817–2826, doi:10.1056/NEJMoa041588.
[11]
Kennecke, H.F.; Speers, C.H.; Ennis, C.A.; Gelmon, K.; Olivotto, I.A.; Hayes, M. Impact of routine pathology review on treatment for node-negative breast cancer. J. Clin. Oncol. 2012, 30, 2227–2231, doi:10.1200/JCO.2011.38.9247.
[12]
Gyorffy, B.; Molnar, B.; Lage, H.; Szallasi, Z.; Eklund, A.C. Evaluation of microarray preprocessing algorithms based on concordance with RT-PCR in clinical samples. PLoS One 2009, 4, e5645, doi:10.1371/journal.pone.0005645.
[13]
Consortium, M.; Shi, L.; Reid, L.H.; Jones, W.D.; Shippy, R.; Warrington, J.A.; Baker, S.C.; Collins, P.J.; de Longueville, F.; Kawasaki, E.S.; et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 2006, 24, 1151–1161, doi:10.1038/nbt1239.
[14]
Li, Q.; Birkbak, N.J.; Gyorffy, B.; Szallasi, Z.; Eklund, A.C. Jetset: Selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics 2011, 12, 474, doi:10.1186/1471-2105-12-474.
[15]
Sotiriou, C.; Neo, S.Y.; McShane, L.M.; Korn, E.L.; Long, P.M.; Jazaeri, A.; Martiat, P.; Fox, S.B.; Harris, A.L.; Liu, E.T. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Natl. Acad. Sci. USA 2003, 100, 10393–10398, doi:10.1073/pnas.1732912100.
[16]
Sorlie, T.; Perou, C.M.; Tibshirani, R.; Aas, T.; Geisler, S.; Johnsen, H.; Hastie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 2001, 98, 10869–10874, doi:10.1073/pnas.191367098.
[17]
Sorlie, T.; Tibshirani, R.; Parker, J.; Hastie, T.; Marron, J.S.; Nobel, A.; Deng, S.; Johnsen, H.; Pesich, R.; Geisler, S.; et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl. Acad. Sci. USA 2003, 100, 8418–8423, doi:10.1073/pnas.0932692100.
[18]
Wang, Y.; Yin, Q.; Yu, Q.; Zhang, J.; Liu, Z.; Wang, S.; Lv, S.; Niu, Y. A retrospective study of breast cancer subtypes: The risk of relapse and the relations with treatments. Breast Cancer Res. Treat. 2011, 130, 489–498, doi:10.1007/s10549-011-1709-6.
[19]
Gyorffy, B.; Serra, V.; Jurchott, K.; Abdul-Ghani, R.; Garber, M.; Stein, U.; Petersen, I.; Lage, H.; Dietel, M.; Schafer, R. Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. Oncogene 2005, 24, 7542–7551, doi:10.1038/sj.onc.1208908.
[20]
Colombo, P.E.; Milanezi, F.; Weigelt, B.; Reis-Filho, J.S. Microarrays in the 2010s: The contribution of microarray-based gene expression profiling to breast cancer classification, prognostication and prediction. Breast Cancer Res. 2011, 13, 212, doi:10.1186/bcr2890.
[21]
Valentin, M.D.; da Silva, S.D.; Privat, M.; Alaoui-Jamali, M.; Bignon, Y.J. Molecular insights on basal-like breast cancer. Breast Cancer Res. Treat. 2012, 134, 21–30, doi:10.1007/s10549-011-1934-z.
[22]
Mackay, A.; Weigelt, B.; Grigoriadis, A.; Kreike, B.; Natrajan, R.; A’Hern, R.; Tan, D.S.; Dowsett, M.; Ashworth, A.; Reis-Filho, J.S. Microarray-based class discovery for molecular classification of breast cancer: Analysis of interobserver agreement. J. Natl. Cancer Inst. 2011, 103, 662–673, doi:10.1093/jnci/djr071.
[23]
Hu, Z.; Fan, C.; Oh, D.S.; Marron, J.S.; He, X.; Qaqish, B.F.; Livasy, C.; Carey, L.A.; Reynolds, E.; Dressler, L.; et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006, 7, 96, doi:10.1186/1471-2164-796.
[24]
Weigelt, B.; Horlings, H.M.; Kreike, B.; Hayes, M.M.; Hauptmann, M.; Wessels, L.F.; de Jong, D.; van de Vijver, M.J.; van’t Veer, L.J.; Peterse, J.L. Refinement of breast cancer classification by molecular characterization of histological special types. J. Pathol. 2008, 216, 141–150, doi:10.1002/path.2407.
[25]
Park, S.; Park, B.W.; Kim, T.H.; Jeon, C.W.; Kang, H.S.; Choi, J.E.; Hwang, K.T.; Kim, I.C. Lack of either estrogen or progesterone receptor expression is associated with poor survival outcome among luminal A breast cancer subtype. Ann. Surg. Oncol. 2013, 20, 1505–1513, doi:10.1245/s10434-012-2772-x.
[26]
Szasz, A.M.; Nemeth, Z.; Gyorffy, B.; Micsinai, M.; Krenacs, T.; Baranyai, Z.; Harsanyi, L.; Kiss, A.; Schaff, Z.; Tokes, A.M.; et al. Identification of a claudin-4 and E-cadherin score to predict prognosis in breast cancer. Cancer Sci. 2011, 102, 2248–2254, doi:10.1111/j.1349-7006.2011.02085.x.
[27]
Lu, S.; Singh, K.; Mangray, S.; Tavares, R.; Noble, L.; Resnick, M.B.; Yakirevich, E. Claudin expression in high-grade invasive ductal carcinoma of the breast: Correlation with the molecular subtype. Mod. Pathol. 2013, 26, 485–495, doi:10.1038/modpathol.2012.187.
[28]
Milde-Langosch, K.; Karn, T.; Muller, V.; Witzel, I.; Rody, A.; Schmidt, M.; Wirtz, R.M. Validity of the proliferation markers Ki67, TOP2A, and RacGAP1 in molecular subgroups of breast cancer. Breast Cancer Res. Treat. 2013, 137, 57–67, doi:10.1007/s10549-012-2296-x.
[29]
Gong, Y.; Yan, K.; Lin, F.; Anderson, K.; Sotiriou, C.; Andre, F.; Holmes, F.A., Valero; Booser, D.; Pippen, J.E., Jr.; et al. Determination of oestrogen-receptor status and ERBB2 status of breast carcinoma: A gene-expression profiling study. Lancet Oncol. 2007, 8, 203–211, doi:10.1016/S1470-2045(07)70042-6.
[30]
Roepman, P.; Horlings, H.M.; Krijgsman, O.; Kok, M.; Bueno-de-Mesquita, J.M.; Bender, R.; Linn, S.C.; Glas, A.M.; van de Vijver, M.J. Microarray-based determination of estrogen receptor, progesterone receptor, and HER2 receptor status in breast cancer. Clin. Cancer Res. 2009, 15, 7003–7011, doi:10.1158/1078-0432.CCR-09-0449.
[31]
Bastien, R.R.; Rodriguez-Lescure, A.; Ebbert, M.T.; Prat, A.; Munarriz, B.; Rowe, L.; Miller, P.; Ruiz-Borrego, M.; Anderson, D.; Lyons, B.; et al. PAM50 breast cancer subtyping by RT-qPCR and concordance with standard clinical molecular markers. BMC Med. Genomics 2012, 5, 44, doi:10.1186/1755-8794-5-44.
[32]
Zoubir, M.M.M.; Liedtke, C.; Bidard, F.; Delaloge, S.; Corley, L.; Spielmann, M.; Pusztai, L.; André, F.; Symmans, W.F. Predictive biomarkers for preoperative endocrine therapy of stage II-III breast cancer by tissue microarrays. J. Clin. Oncol. 2008, 26, 560.
[33]
Bartlett, J.M.; Thomas, J.; Ross, D.T.; Seitz, R.S.; Ring, B.Z.; Beck, R.A.; Pedersen, H.C.; Munro, A.; Kunkler, I.H.; Campbell, F.M.; et al. Mammostrat as a tool to stratify breast cancer patients at risk of recurrence during endocrine therapy. Breast Cancer Res. 2010, 12, R47, doi:10.1186/bcr2604.
[34]
Hilborn, E.S.T.; Kot, A.; Fornander, T.; Skoog, L.; Nordenskj？ld, B.; St？l, O.; Jansson, A. The importance of CXCL10 and CXCR3-A in breast cancer. Cancer Res. 2011, 71, doi:10.1158/0008-5472.SABCS11-P1-06-06.
[35]
Surowiak, P.; Matkowski, R.; Materna, V.; Gyorffy, B.; Wojnar, A.; Pudelko, M.; Dziegiel, P.; Kornafel, J.; Zabel, M. Elevated metallothionein (MT) expression in invasive ductal breast cancers predicts tamoxifen resistance. Histol. Histopathol. 2005, 20, 1037–1044.
[36]
Mihaly, Z.; Kormos, M.; Lanczky, A.; Dank, M.; Budczies, J.; Szasz, M.A.; Gyorffy, B. A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer. Breast Cancer Res. Treat. 2013, 140, 219–232, doi:10.1007/s10549-013-2622-y.
Magnani, L.; Stoeck, A.; Zhang, X.; Lanczky, A.; Mirabella, A.C.; Wang, T.L.; Gyorffy, B.; Lupien, M. Genome-wide reprogramming of the chromatin landscape underlies endocrine therapy resistance in breast cancer. Proc. Natl. Acad. Sci. USA 2013, 110, E1490–E1499, doi:10.1073/pnas.1219992110.
[39]
Aguilar, H.; Sole, X.; Bonifaci, N.; Serra-Musach, J.; Islam, A.; Lopez-Bigas, N.; Mendez-Pertuz, M.; Beijersbergen, R.L.; Lazaro, C.; Urruticoechea, A.; et al. Biological reprogramming in acquired resistance to endocrine therapy of breast cancer. Oncogene 2010, 29, 6071–6083, doi:10.1038/onc.2010.333.
[40]
Gyorffy, B.; Benke, Z.; Lanczky, A.; Balazs, B.; Szallasi, Z.; Timar, J.; Schafer, R. RecurrenceOnline: An online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res. Treat. 2012, 132, 1025–1034, doi:10.1007/s10549-011-1676-y.
[41]
Sotiriou, C.; Wirapati, P.; Loi, S.; Harris, A.; Fox, S.; Smeds, J.; Nordgren, H.; Farmer, P.; Praz, V.; Haibe-Kains, B.; et al. Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis. J. Natl. Cancer Inst. 2006, 98, 262–272, doi:10.1093/jnci/djj052.
[42]
Van de Vijver, M.J.; He, Y.D.; van’t Veer, L.J.; Dai, H.; Hart, A.A.; Voskuil, D.W.; Schreiber, G.J.; Peterse, J.L.; Roberts, C.; Marton, M.J.; et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 2002, 347, 1999–2009, doi:10.1056/NEJMoa021967.
[43]
Ma, X.J.; Wang, Z.; Ryan, P.D.; Isakoff, S.J.; Barmettler, A.; Fuller, A.; Muir, B.; Mohapatra, G.; Salunga, R.; Tuggle, J.T.; et al. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 2004, 5, 607–616, doi:10.1016/j.ccr.2004.05.015.
[44]
Parker, J.S.; Mullins, M.; Cheang, M.C.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 2009, 27, 1160–1167, doi:10.1200/JCO.2008.18.1370.
[45]
Wittner, B.S.; Sgroi, D.C.; Ryan, P.D.; Bruinsma, T.J.; Glas, A.M.; Male, A.; Dahiya, S.; Habin, K.; Bernards, R.; Haber, D.A.; et al. Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. Clin. Cancer Res. 2008, 14, 2988–2993, doi:10.1158/1078-0432.CCR-07-4723.
[46]
Habel, L.A.; Shak, S.; Jacobs, M.K.; Capra, A.; Alexander, C.; Pho, M.; Baker, J.; Walker, M.; Watson, D.; Hackett, J.; et al. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res. 2006, 8, R25, doi:10.1186/bcr1412.
[47]
Esteva, F.J.; Sahin, A.A.; Cristofanilli, M.; Coombes, K.; Lee, S.J.; Baker, J.; Cronin, M.; Walker, M.; Watson, D.; Shak, S.; et al. Prognostic role of a multigene reverse transcriptase-PCR assay in patients with node-negative breast cancer not receiving adjuvant systemic therapy. Clin. Cancer Res. 2005, 11, 3315–3319, doi:10.1158/1078-0432.CCR-04-1707.
[48]
Dowsett, C.W.; Forbes, J.; Mallon, L.; Salter, J.; Cuzick, J.; Wales, C.; Forbes, J.; Mallon, L.; Salter, J.; Quinn, E. Risk of distant recurrence using Oncotype DX in postmenopausal primary breast cancer patients treated with anastrozole or tamoxifen: A TransATAC study. Cancer Res. 2009, 69, 1059–1061.
[49]
Gyorffy, B.; Schafer, R. Meta-analysis of gene expression profiles related to relapse-free survival in 1079 breast cancer patients. Breast Cancer Res. Treat. 2009, 118, 433–441, doi:10.1007/s10549-008-0242-8.
[50]
Dabbs, D.J.; Klein, M.E.; Mohsin, S.K.; Tubbs, R.R.; Shuai, Y.; Bhargava, R. High false-negative rate of HER2 quantitative reverse transcription polymerase chain reaction of the Oncotype DX test: An independent quality assurance study. J. Clin. Oncol. 2011, 29, 4279–4285, doi:10.1200/JCO.2011.34.7963.
[51]
Ma, X.J.; Hilsenbeck, S.G.; Wang, W.; Ding, L.; Sgroi, D.C.; Bender, R.A.; Osborne, C.K.; Allred, D.C.; Erlander, M.G. The HOXB13:IL17BR expression index is a prognostic factor in early-stage breast cancer. J. Clin. Oncol. 2006, 24, 4611–4619, doi:10.1200/JCO.2006.06.6944.
[52]
Jerevall, P.L.; Brommesson, S.; Strand, C.; Gruvberger-Saal, S.; Malmstrom, P.; Nordenskjold, B.; Wingren, S.; Soderkvist, P.; Ferno, M.; Stal, O. Exploring the two-gene ratio in breast cancer—Independent roles for HOXB13 and IL17BR in prediction of clinical outcome. Breast Cancer Res. Treat. 2008, 107, 225–234, doi:10.1007/s10549-007-9541-8.
[53]
Reid, J.F.; Lusa, L.; de Cecco, L.; Coradini, D.; Veneroni, S.; Daidone, M.G.; Gariboldi, M.; Pierotti, M.A. Limits of predictive models using microarray data for breast cancer clinical treatment outcome. J. Natl. Cancer Inst. 2005, 97, 927–930, doi:10.1093/jnci/dji153.
[54]
Liu, R.; Wang, X.; Chen, G.Y.; Dalerba, P.; Gurney, A.; Hoey, T.; Sherlock, G.; Lewicki, J.; Shedden, K.; Clarke, M.F. The prognostic role of a gene signature from tumorigenic breast-cancer cells. N. Engl. J. Med. 2007, 356, 217–226, doi:10.1056/NEJMoa063994.
[55]
Staaf, J.; Ringner, M.; Vallon-Christersson, J.; Jonsson, G.; Bendahl, P.O.; Holm, K.; Arason, A.; Gunnarsson, H.; Hegardt, C.; Agnarsson, B.A.; et al. Identification of subtypes in human epidermal growth factor receptor 2—Positive breast cancer reveals a gene signature prognostic of outcome. J. Clin. Oncol. 2010, 28, 1813–1820, doi:10.1200/JCO.2009.22.8775.
[56]
Cobleigh, M.A.; Tabesh, B.; Bitterman, P.; Baker, J.; Cronin, M.; Liu, M.L.; Borchik, R.; Mosquera, J.M.; Walker, M.G.; Shak, S. Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes. Clin. Cancer Res. 2005, 11, 8623–8631, doi:10.1158/1078-0432.CCR-05-0735.
[57]
Tutt, A.; Wang, A.; Rowland, C.; Gillett, C.; Lau, K.; Chew, K.; Dai, H.; Kwok, S.; Ryder, K.; Shu, H.; et al. Risk estimation of distant metastasis in node-negative, estrogen receptor-positive breast cancer patients using an RT-PCR based prognostic expression signature. BMC Cancer 2008, 8, 339, doi:10.1186/1471-2407-8-339.
[58]
Chia, S.K.; Bramwell, V.H.; Tu, D.; Shepherd, L.E.; Jiang, S.; Vickery, T.; Mardis, E.; Leung, S.; Ung, K.; Pritchard, K.I.; et al. A 50-gene intrinsic subtype classifier for prognosis and prediction of benefit from adjuvant tamoxifen. Clin. Cancer Res. 2012, 18, 4465–4472, doi:10.1158/1078-0432.CCR-12-0286.
[59]
Harvell, D.M.; Spoelstra, N.S.; Singh, M.; McManaman, J.L.; Finlayson, C.; Phang, T.; Trapp, S.; Hunter, L.; Dye, W.W.; Borges, V.F.; et al. Molecular signatures of neoadjuvant endocrine therapy for breast cancer: Characteristics of response or intrinsic resistance. Breast Cancer Res. Treat. 2008, 112, 475–488, doi:10.1007/s10549-008-9897-4.
[60]
Szasz, A.M.; Li, Q.; Eklund, A.C.; Sztupinszki, Z.; Rowan, A.; Tokes, A.M.; Szekely, B.; Kiss, A.; Szendroi, M.; Gyorffy, B.; et al. The CIN4 chromosomal instability qPCR classifier defines tumor aneuploidy and stratifies outcome in grade 2 breast cancer. PLoS One 2013, 8, e56707, doi:10.1371/journal.pone.0056707.
[61]
Paik, S.; Tang, G.; Shak, S.; Kim, C.; Baker, J.; Kim, W.; Cronin, M.; Baehner, F.L.; Watson, D.; Bryant, J.; et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J. Clin. Oncol. 2006, 24, 3726–3734, doi:10.1200/JCO.2005.04.7985.
[62]
Wang, Y.; Klijn, J.G.; Zhang, Y.; Sieuwerts, A.M.; Look, M.P.; Yang, F.; Talantov, D.; Timmermans, M.; Meijer-van Gelder, M.E.; Yu, J.; et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 365, 671–679.
[63]
Miller, T.W.; Balko, J.M.; Ghazoui, Z.; Dunbier, A.; Anderson, H.; Dowsett, M.; Gonzalez-Angulo, A.M.; Mills, G.B.; Miller, W.R.; Wu, H.; et al. A gene expression signature from human breast cancer cells with acquired hormone independence identifies MYC as a mediator of antiestrogen resistance. Clin. Cancer Res. 2011, 17, 2024–2034, doi:10.1158/1078-0432.CCR-10-2567.
[64]
Roberts, C.G.; Millar, E.K.; O’Toole, S.A.; McNeil, C.M.; Lehrbach, G.M.; Pinese, M.; Tobelmann, P.; McCloy, R.A.; Musgrove, E.A.; Sutherland, R.L.; et al. Identification of PUMA as an estrogen target gene that mediates the apoptotic response to tamoxifen in human breast cancer cells and predicts patient outcome and tamoxifen responsiveness in breast cancer. Oncogene 2011, 30, 3186–3197, doi:10.1038/onc.2011.36.
[65]
Munkacsy, G.; Abdul-Ghani, R.; Mihaly, Z.; Tegze, B.; Tchernitsa, O.; Surowiak, P.; Schafer, R.; Gyorffy, B. PSMB7 is associated with anthracycline resistance and is a prognostic biomarker in breast cancer. Br. J. Cancer 2010, 102, 361–368, doi:10.1038/sj.bjc.6605478.
[66]
Wend, P.; Runke, S.; Wend, K.; Anchondo, B.; Yesayan, M.; Jardon, M.; Hardie, N.; Loddenkemper, C.; Ulasov, I.; Lesniak, M.S.; et al. WNT10B/β-catenin signalling induces HMGA2 and proliferation in metastatic triple-negative breast cancer. EMBO Mol. Med. 2013, 5, 264–279, doi:10.1002/emmm.201201320.
[67]
Gyorffy, B.; Surowiak, P.; Kiesslich, O.; Denkert, C.; Schafer, R.; Dietel, M.; Lage, H. Gene expression profiling of 30 cancer cell lines predicts resistance towards 11 anticancer drugs at clinically achieved concentrations. Int. J. Cancer 2006, 118, 1699–1712, doi:10.1002/ijc.21570.
Porter, D.C.; Farmaki, E.; Altilia, S.; Schools, G.P.; West, D.K.; Chen, M.; Chang, B.D.; Puzyrev, A.T.; Lim, C.U.; Rokow-Kittell, R.; et al. Cyclin-dependent kinase 8 mediates chemotherapy-induced tumor-promoting paracrine activities. Proc. Natl. Acad. Sci. USA 2012, 109, 13799–13804, doi:10.1073/pnas.1206906109.
[70]
Molina, A.; Velot, L.; Ghouinem, L.; Abdelkarim, M.; Bouchet, B.P.; Luissint, A.C.; Bouhlel, I.; Morel, M.; Sapharikas, E.; di Tommaso, A.; et al. ATIP3, a novel prognostic marker of breast cancer patient survival, limits cancer cell migration and slows metastatic progression by regulating microtubule dynamics. Cancer Res. 2013, 73, 2905–2915, doi:10.1158/0008-5472.CAN-12-3565.
[71]
Gene Expression Omnibus. Available online: http://www.ncbi.nlm.nih.gov/geo/ (accessed on 26 August 2013).
[72]
The European Genome-phenome Archive. Available online: https://www.ebi.ac.uk/ega/ (accessed on 26 August 2013).
[73]
Gyorffy, B.; Lanczky, A.; Eklund, A.C.; Denkert, C.; Budczies, J.; Li, Q.; Szallasi, Z. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res. Treat. 2010, 123, 725–731, doi:10.1007/s10549-009-0674-9.
[74]
Gyorffy, B.; Lanczky, A.; Szallasi, Z. Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1,287 patients. Endocr. Relat. Cancer 2012, 19, 197–208, doi:10.1530/ERC-11-0329.
[75]
Park, S.; Koo, J.S.; Kim, M.S.; Park, H.S.; Lee, J.S.; Lee, J.S.; Kim, S.I.; Park, B.W. Characteristics and outcomes according to molecular subtypes of breast cancer as classified by a panel of four biomarkers using immunohistochemistry. Breast 2012, 21, 50–57, doi:10.1016/j.breast.2011.07.008.
[76]
Pawitan, Y.; Bjohle, J.; Amler, L.; Borg, A.L.; Egyhazi, S.; Hall, P.; Han, X.; Holmberg, L.; Huang, F.; Klaar, S.; et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: Derived and validated in two population-based cohorts. Breast Cancer Res. 2005, 7, R953–R964, doi:10.1186/bcr1325.
[77]
Sabatier, R.; Finetti, P.; Cervera, N.; Lambaudie, E.; Esterni, B.; Mamessier, E.; Tallet, A.; Chabannon, C.; Extra, J.M.; Jacquemier, J.; et al. A gene expression signature identifies two prognostic subgroups of basal breast cancer. Breast Cancer Res. Treat. 2011, 126, 407–420, doi:10.1007/s10549-010-0897-9.
[78]
Hatzis, C.; Pusztai, L.; Valero, V.; Booser, D.J.; Esserman, L.; Lluch, A.; Vidaurre, T.; Holmes, F.; Souchon, E.; Wang, H.; et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA 2011, 305, 1873–1881, doi:10.1001/jama.2011.593.
[79]
Dedeurwaerder, S.; Desmedt, C.; Calonne, E.; Singhal, S.K.; Haibe-Kains, B.; Defrance, M.; Michiels, S.; Volkmar, M.; Deplus, R.; Luciani, J.; et al. DNA methylation profiling reveals a predominant immune component in breast cancers. EMBO Mol. Med. 2011, 3, 726–741, doi:10.1002/emmm.201100801.
[80]
Karn, T.; Pusztai, L.; Holtrich, U.; Iwamoto, T.; Shiang, C.Y.; Schmidt, M.; Muller, V.; Solbach, C.; Gaetje, R.; Hanker, L.; et al. Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures. PLoS One 2011, 6, e28403, doi:10.1371/journal.pone.0028403.
[81]
Sircoulomb, F.; Bekhouche, I.; Finetti, P.; Adelaide, J.; Ben Hamida, A.; Bonansea, J.; Raynaud, S.; Innocenti, C.; Charafe-Jauffret, E.; Tarpin, C.; et al. Genome profiling of ERBB2-amplified breast cancers. BMC Cancer 2010, 10, 539, doi:10.1186/1471-2407-10-539.
[82]
KM Plotter. Available online: http://www.kmplot.com (accessed on 26 August 2013).
[83]
Budczies, J.; Klauschen, F.; Sinn, B.V.; Gyorffy, B.; Schmitt, W.D.; Darb-Esfahani, S.; Denkert, C. Cutoff finder: A comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization. PLoS One 2012, 7, e51862, doi:10.1371/journal.pone.0051862.
