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A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
Open accessAbstract
BACKGROUND: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. METHODS: Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. RESULTS: Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome. CONCLUSIONS: The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.
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Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Self-citationProportional hazards tests and diagnostics based on weighted residuals
GRAMBSCH, P. M., & THERNEAU, T. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.515 · Cited by 5,885
Cited by
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
Genes that mediate breast cancer metastasis to lung
Minn, A. J., Gupta, G. P., Siegel, P. M., Bos, P. D., Shu, W., Giri, D. D., Viale, A., Olshen, A. B., Gerald, W. L., & Massagué, J. (2005) · Nature · doi:10.1038/nature03799 · Cited by 2,922
Breast cancer metastasis: markers and models
Weigelt, B., Peterse, J. L., & van't Veer, L. J. (2005) · Nature Reviews Cancer · doi:10.1038/nrc1670 · Cited by 2,232
Self-citationDirect multiplexed measurement of gene expression with color-coded probe pairs
Geiss, G. K., Bumgarner, R. E., Birditt, B., Dahl, T., Dowidar, N., Dunaway, D. L., Fell, H. P., Ferree, S., George, R. D., Grogan, T., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1385 · Cited by 2,066
Functional genomics reveal that the serine synthesis pathway is essential in breast cancer
Possemato, R., Marks, K. M., Shaul, Y. D., Pacold, M. E., Kim, D., Birsoy, K., Sethumadhavan, S., Woo, H.-K., Jang, H. G., Jha, A. K., et al. (2011) · Nature · doi:10.1038/nature10350 · Cited by 1,683
Uno, H., Cai, T., Pencina, M. J., D'Agostino, R. B., & Wei, L. J. (2011) · Statistics in Medicine · doi:10.1002/sim.4154 · Cited by 1,535
Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation
Jain, M., Nilsson, R., Sharma, S., Madhusudhan, N., Kitami, T., Souza, A. L., Kafri, R., Kirschner, M. W., Clish, C. B., & Mootha, V. K. (2012) · Science · doi:10.1126/science.1218595 · Cited by 1,403
Network‐based classification of breast cancer metastasis
Chuang, H., Lee, E., Liu, Y., Lee, D., & Ideker, T. (2007) · Molecular Systems Biology · doi:10.1038/msb4100180 · Cited by 1,431
The Hippo transducer TAZ confers cancer stem cell-related traits on breast cancer cells
Cordenonsi, M., Zanconato, F., Azzolin, L., Forcato, M., Rosato, A., Frasson, C., Inui, M., Montagner, M., Parenti, A. R., Poletti, A., et al. (2011) · Cell · doi:10.1016/j.cell.2011.09.048 · Cited by 1,381
Genetic landscape and biomarkers of hepatocellular carcinoma
Zucman-Rossi, J., Villanueva, A., Nault, J.-C., & Llovet, J. M. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.05.061 · Cited by 1,325
Molecular characterization of the tumor microenvironment in breast cancer
Allinen, M., Beroukhim, R., Cai, L., Brennan, C., Lahti-Domenici, J., Huang, H., Porter, D., Hu, M., Chin, L., Richardson, A., et al. (2004) · Cancer Cell · doi:10.1016/j.ccr.2004.06.010 · Cited by 1,299
Pepe, M. S. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh101 · Cited by 1,276
Chang, H. Y., Nuyten, D. S. A., Sneddon, J. B., Hastie, T., Tibshirani, R., Sørlie, T., Dai, H., He, Y. D., van't Veer, L. J., Bartelink, H., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409462102 · Cited by 981
Self-citationFoxA1 Translates Epigenetic Signatures into Enhancer-Driven Lineage-Specific Transcription
Lupien, M., Eeckhoute, J., Meyer, C. A., Wang, Q., Zhang, Y., Li, W., Carroll, J. S., Liu, X. S., & Brown, M. (2008) · Cell · doi:10.1016/j.cell.2008.01.018 · Cited by 983
Glinsky, G. V., Berezovska, O., & Glinskii, A. B. (2005) · Journal of Clinical Investigation · doi:10.1172/jci23412 · Cited by 916
Chromosome aberrations in solid tumors
Albertson, D. G., Collins, C., McCormick, F., & Gray, J. W. (2003) · Nature Genetics · doi:10.1038/ng1215 · Cited by 812
MicroRNAs accurately identify cancer tissue origin
Rosenfeld, N., Aharonov, R., Meiri, E., Rosenwald, S., Spector, Y., Zepeniuk, M., Benjamin, H., Shabes, N., Tabak, S., Levy, A., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1392 · Cited by 812
Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., & Stuart, J. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq182 · Cited by 786
Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology
Cox, J., & Mann, M. (2011) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061308-093216 · Cited by 685
Revisiting Global Gene Expression Analysis
Lovén, J., Orlando, D. A., Sigova, A. A., Lin, C. Y., Rahl, P. B., Burge, C. B., Levens, D. L., Lee, T. I., & Young, R. A. (2012) · Cell · doi:10.1016/j.cell.2012.10.012 · Cited by 616
Decoding global gene expression programs in liver cancer by noninvasive imaging
Segal, E., Sirlin, C. B., Ooi, C., Adler, A. S., Gollub, J., Chen, X., Chan, B. K., Matcuk, G. R., Barry, C. T., Chang, H. Y., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1306 · Cited by 629
Nilsson, R., Jain, M., Madhusudhan, N., Sheppard, N. G., Strittmatter, L., Kampf, C., Huang, J., Asplund, A., & Mootha, V. K. (2014) · Nature Communications · doi:10.1038/ncomms4128 · Cited by 607
Vanharanta, S., & Massagué, J. (2013) · Cancer Cell · doi:10.1016/j.ccr.2013.09.007 · Cited by 563
Reliability and reproducibility issues in DNA microarray measurements
DRAGHICI, S., KHATRI, P., EKLUND, A., & SZALLASI, Z. (2006) · Trends in Genetics · doi:10.1016/j.tig.2005.12.005 · Cited by 590
Cox, T. R., Rumney, R. M. H., Schoof, E. M., Perryman, L., Høye, A. M., Agrawal, A., Bird, D., Latif, N. A., Forrest, H., Evans, H. R., et al. (2015) · Nature · doi:10.1038/nature14492 · Cited by 482
RetractedFerroportin and Iron Regulation in Breast Cancer Progression and Prognosis
Pinnix, Z. K., Miller, L. D., Wang, W., D’Agostino, R., Kute, T., Willingham, M. C., Hatcher, H., Tesfay, L., Sui, G., Di, X., et al. (2010) · Science Translational Medicine · doi:10.1126/scitranslmed.3001127 · Cited by 448
Classification and prognosis of invasive breast cancer: from morphology to molecular taxonomy
Schnitt, S. J. (2010) · Modern Pathology · doi:10.1038/modpathol.2010.33 · Cited by 419
MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis
Terunuma, A., Putluri, N., Mishra, P., Mathé, E. A., Dorsey, T. H., Yi, M., Wallace, T. A., Issaq, H. J., Zhou, M., Killian, J. K., et al. (2014) · Journal of Clinical Investigation · doi:10.1172/jci71180 · Cited by 428
Yang, Y., Han, L., Yuan, Y., Li, J., Hei, N., & Liang, H. (2014) · Nature Communications · doi:10.1038/ncomms4231 · Cited by 423
Relation of a hypoxia metagene derived from head and neck cancer to prognosis of multiple cancers
Winter, S. C., Buffa, F. M., Silva, P., Miller, C., Valentine, H. R., Turley, H., Shah, K. A., Cox, G. J., Corbridge, R. J., Homer, J. J., et al. (2007) · Cancer Research · doi:10.1158/0008-5472.can-06-3322 · Cited by 410
Evaluating Prediction Rules for t -Year Survivors With Censored Regression Models
Uno, H., Cai, T., Tian, L., & Wei, L. J. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000000149 · Cited by 413
Lung metastasis genes couple breast tumor size and metastatic spread
Minn, A. J., Gupta, G. P., Padua, D., Bos, P., Nguyen, D. X., Nuyten, D., Kreike, B., Zhang, Y., Wang, Y., Ishwaran, H., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701138104 · Cited by 400
Rajski, M., Zanetti-Dällenbach, R., Vogel, B., Herrmann, R., Rochlitz, C., & Buess, M. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-1 · Cited by 355
Sun, D., Wang, M., & Li, A. (2019) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2806438 · Cited by 338
Nelson, C. M., & Bissell, M. J. (2005) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2005.05.001 · Cited by 332
Small-sample precision of ROC-related estimates
Hanczar, B., Hua, J., Sima, C., Weinstein, J., Bittner, M., & Dougherty, E. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq037 · Cited by 323
Triple‐negative breast cancer: Present challenges and new perspectives
Podo, F., Buydens, L. M. C., Degani, H., Hilhorst, R., Klipp, E., Gribbestad, I. S., Van Huffel, S., W.M. van Laarhoven, H., Luts, J., Monleon, D., et al. (2010) · Molecular Oncology · doi:10.1016/j.molonc.2010.04.006 · Cited by 299
Oberthuer, A., Berthold, F., Warnat, P., Hero, B., Kahlert, Y., Spitz, R., Ernestus, K., König, R., Haas, S., Eils, R., et al. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2006.06.1879 · Cited by 291
Segman, R. H., Shefi, N., Goltser-Dubner, T., Friedman, N., Kaminski, N., & Shalev, A. Y. (2005) · Molecular Psychiatry · doi:10.1038/sj.mp.4001636 · Cited by 281
High expression of DNA repair pathways is associated with metastasis in melanoma patients
Kauffmann, A., Rosselli, F., Lazar, V, Winnepenninckx, V, Mansuet-Lupo, A., Dessen, P., van den Oord, J. J., Spatz, A., & Sarasin, A. (2008) · Oncogene · doi:10.1038/sj.onc.1210700 · Cited by 274
Eustace, A., Mani, N., Span, P. N., Irlam, J. J., Taylor, J., Betts, G. N. J., Denley, H., Miller, C. J., Homer, J. J., Rojas, A. M., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-0542 · Cited by 240
Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach
Liang, M., Li, Z., Chen, T., & Zeng, J. (2015) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2377729 · Cited by 231
An integrative genomics approach to the reconstruction of gene networks in segregating populations
Zhu, J., Lum, P. Y., Lamb, J., GuhaThakurta, D., Edwards, S. W., Thieringer, R., Berger, J. P., Wu, M. S., Thompson, J., Sachs, A. B., et al. (2004) · Cytogenetic and Genome Research · doi:10.1159/000078209 · Cited by 221
Bauer, M., Eickhoff, J. C., Gould, M. N., Mundhenke, C., Maass, N., & Friedl, A. (2008) · Breast Cancer Research and Treatment · doi:10.1007/s10549-007-9619-3 · Cited by 215
Peng, J., Zhu, J., Bergamaschi, A., Han, W., Noh, D.-Y., Pollack, J. R., & Wang, P. (2010) · The Annals of Applied Statistics · doi:10.1214/09-aoas271 · Cited by 211
Oestrogen signalling inhibits invasive phenotype by repressing RelB and its target BCL2
Wang, X., Belguise, K., Kersual, N., Kirsch, K. H., Mineva, N. D., Galtier, F., Chalbos, D., & Sonenshein, G. E. (2007) · Nature Cell Biology · doi:10.1038/ncb1559 · Cited by 209
Transcriptional characterizations of differences between eutopic and ectopic endometrium
Wu, Y., Kajdacsy-Balla, A., Strawn, E., Basir, Z., Halverson, G., Jailwala, P., Wang, Y., Wang, X., Ghosh, S., & Guo, S.-W. (2006) · Endocrinology · doi:10.1210/en.2005-0426 · Cited by 208
Marlow, G., Ellett, S., Ferguson, I. R., Zhu, S., Karunasinghe, N., Jesuthasan, A. C., Han, D. Y., Fraser, A. G., & Ferguson, L. R. (2013) · Human Genomics · doi:10.1186/1479-7364-7-24 · Cited by 200
Nam, J.-M., Onodera, Y., Bissell, M. J., & Park, C. C. (2010) · Cancer Research · doi:10.1158/0008-5472.can-09-2319 · Cited by 196
Inhibition of matrix metalloproteinase 14 (MMP-14)-mediated cancer cell migration.
Zarrabi, K., Dufour, A., Li, J., Kuscu, C., Pulkoski-Gross, A., Zhi, J., Hu, Y., Sampson, N. S., Zucker, S., & Cao, J. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.256644 · Cited by 188
Lightbody, G., Haberland, V., Browne, F., Taggart, L., Zheng, H., Parkes, E., & Blayney, J. K. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby051 · Cited by 173
Tang, W., Zhou, M., Dorsey, T. H., Prieto, D. A., Wang, X. W., Ruppin, E., Veenstra, T. D., & Ambs, S. (2018) · Genome Medicine · doi:10.1186/s13073-018-0602-x · Cited by 162
Meta-analytic support vector machine for integrating multiple omics data
Kim, S., Jhong, J.-H., Lee, J., & Koo, J.-Y. (2017) · BioData Mining · doi:10.1186/s13040-017-0126-8 · Cited by 159
Volinia, S., & Croce, C. M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1304977110 · Cited by 149
Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy
Andre, F., & Pusztai, L. (2006) · Nature Clinical Practice Oncology · doi:10.1038/ncponc0636 · Cited by 134
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02643-7 · Cited by 137
Regulation of nucleotide metabolism by mutant p53 contributes to its gain-of-function activities
Kollareddy, M., Dimitrova, E., Vallabhaneni, K. C., Chan, A., Le, T., Chauhan, K. M., Carrero, Z. I., Ramakrishnan, G., Watabe, K., Haupt, Y., et al. (2015) · Nature Communications · doi:10.1038/ncomms8389 · Cited by 127
Gruson, D., Helleputte, T., Rousseau, P., & Gruson, D. (2019) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2019.04.013 · Cited by 125
TSPYL5 suppresses p53 levels and function by physical interaction with USP7.
Epping, M. T., Meijer, L. A. T., Krijgsman, O., Bos, J. L., Pandolfi, P. P., & Bernards, R. (2011) · Nature Cell Biology · doi:10.1038/ncb2142 · Cited by 117
Self-citationLaverty, H. G., Wakefield, L. M., Occleston, N. L., O’Kane, S., & Ferguson, M. W. J. (2009) · Cytokine & Growth Factor Reviews · doi:10.1016/j.cytogfr.2009.07.002 · Cited by 110
Kersten, K., Coffelt, S. B., Hoogstraat, M., Verstegen, N. J. M., Vrijland, K., Ciampricotti, M., Doornebal, C. W., Hau, C.-S., Wellenstein, M. D., Salvagno, C., et al. (2017) · OncoImmunology · doi:10.1080/2162402x.2017.1334744 · Cited by 109
Al-Ejeh, F., Simpson, P. T., Sanus, J. M., Klein, K., Kalimutho, M., Shi, W., Miranda, M., Kutasovic, J., Raghavendra, A., Madore, J., et al. (2014) · Oncogenesis · doi:10.1038/oncsis.2014.14 · Cited by 107
Cell-type selective chromatin remodeling defines the active subset of FOXA1-bound enhancers
Eeckhoute, J., Lupien, M., Meyer, C. A., Verzi, M. P., Shivdasani, R. A., Liu, X. S., & Brown, M. (2009) · Genome Research · doi:10.1101/gr.084582.108 · Cited by 106
Schlereth, K., Heyl, C., Krampitz, A.-M., Mernberger, M., Finkernagel, F., Scharfe, M., Jarek, M., Leich, E., Rosenwald, A., & Stiewe, T. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003726 · Cited by 87
Reuter, J. A., Ortiz-Urda, S., Kretz, M., Garcia, J., Scholl, F. A., Pasmooij, A. M. G., Cassarino, D., Chang, H. Y., & Khavari, P. A. (2009) · Cancer Cell · doi:10.1016/j.ccr.2009.04.002 · Cited by 88
Jézéquel, P., Campion, L., Spyratos, F., Loussouarn, D., Campone, M., Guérin‐Charbonnel, C., Joalland, M., André, J., Descotes, F., Grenot, C., et al. (2012) · International Journal of Cancer · doi:10.1002/ijc.26397 · Cited by 85
Torres-Roca, J. F. (2012) · Personalized Medicine · doi:10.2217/pme.12.55 · Cited by 81
MammaPrint guides treatment decisions in breast Cancer: results of the IMPACt trial
Soliman, H., Shah, V., Srkalovic, G., Mahtani, R., Levine, E., Mavromatis, B., Srinivasiah, J., Kassar, M., Gabordi, R., Qamar, R., et al. (2020) · BMC Cancer · doi:10.1186/s12885-020-6534-z · Cited by 65
Gloyeske, N. C., Dabbs, D. J., & Bhargava, R. (2014) · American Journal of Clinical Pathology · doi:10.1309/ajcp34cysatwfdpq · Cited by 63
Wang, C., Uray, I. P., Mazumdar, A., Mayer, J. A., & Brown, P. H. (2012) · Breast Cancer Research and Treatment · doi:10.1007/s10549-011-1925-0 · Cited by 59
MRAS: A Close but Understudied Member of the RAS Family
Young, L. C., & Rodriguez-Viciana, P. (2018) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a033621 · Cited by 53
Prediction of chemo-response in serous ovarian cancer
Gonzalez Bosquet, J., Newtson, A. M., Chung, R. K., Thiel, K. W., Ginader, T., Goodheart, M. J., Leslie, K. K., & Smith, B. J. (2016) · Molecular Cancer · doi:10.1186/s12943-016-0548-9 · Cited by 46
Boudreau, A., van't Veer, L. J., & Bissell, M. J. (2012) · Cell Adhesion & Migration · doi:10.4161/cam.20880 · Cited by 43
Self-citationMotoori, M., Takemasa, I., Yano, M., Saito, S., Miyata, H., Takiguchi, S., Fujiwara, Y., Yasuda, T., Doki, Y., Kurokawa, Y., et al. (2005) · International Journal of Cancer · doi:10.1002/ijc.20808 · Cited by 39
Screening for Cancer: Considerations for Low- and Middle-Income Countries
Sullivan, T., Sullivan, R., & Ginsburg, O. M. (2015) · Disease Control Priorities, Third Edition (Volume 3): Cancer · doi:10.1596/978-1-4648-0349-9_ch12 · Cited by 38
Guidelines for using sigQC for systematic evaluation of gene signatures
Dhawan, A., Barberis, A., Cheng, W.-C., Domingo, E., West, C., Maughan, T., Scott, J. G., Harris, A. L., & Buffa, F. M. (2019) · Nature Protocols · doi:10.1038/s41596-019-0136-8 · Cited by 31
Bombard, Y., Rozmovits, L., Trudeau, M., Leighl, N. B., Deal, K., & Marshall, D. A. (2014) · Current Oncology · doi:10.3747/co.21.1782 · Cited by 30
Castro, A. F., Campos, T., Babcock, J. T., Armijo, M. E., Martínez‐Conde, A., Pincheira, R., & Quilliam, L. A. (2012) · Journal of Cellular Biochemistry · doi:10.1002/jcb.23458 · Cited by 22
Sparano, J. A., Goldstein, L. J., Childs, B. H., Shak, S., Brassard, D., Badve, S., Baehner, F. L., Bugarini, R., Rowley, S., Perez, E. A., et al. (2011) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-3357 · Cited by 22
Stability selection for lasso, ridge and elastic net implemented with AFT models
Khan, M. H. R., Bhadra, A., & Howlader, T. (2019) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2017-0001 · Cited by 11
Metadata
- Authors
- van de Vijver, M. J., He, Y. D., van 't Veer, L. J., Dai, H., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E. T., Friend, S. H., & Bernards, R.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2002-12-19
- Volume
- 347
- Issue
- 25
- Pages
- 1999-2009
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2160450758
- Cited by
- 6,488
- References
- 3
- FWCI
- 98.04
- Type
- journal article
- Language
- en
- Open access
- bronze
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