High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates

Cancer Research

Volumn 67, Issue 22, 2007, Pages 10669-10676

  Alexe, Gabriela ^a,b   Dalgin, Gul S ^c   Scanfeld, Daniel ^a   Tamayo, Pablo ^a   Mesirov, Jill P ^a   DeLisi, Charles ^d   Harris, Lyndsay ^e   Barnard, Nicola ^g   Martel, Maritza ^f   Levine, Arnold J ^b,h   Ganesan, Shridar ^h   Bhanote, Gyan ^b,d,h,i,j  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b INSTITUTE FOR ADVANCED STUDY   (United States)

^c BOSTON UNIVERSITY   (United States)

^d Boston University   (United States)

^e YALE CANCER CENTER   (United States)

^f YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^g ROBERT WOOD JOHNSON UNIVERSITY HOSPITAL   (United States)

^h RUTGERS CANCER INSTITUTE OF NEW JERSEY   (United States)

ⁱ RUTGERS UNIVERSITY   (United States)

^j RUTGERS UNIVERSITY   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

EPIDERMAL GROWTH FACTOR RECEPTOR 2;

ARTICLE; BREAST CANCER; CANCER RADIOTHERAPY; CANCER RECURRENCE; CANCER SURGERY; CANCER SURVIVAL; CONTROLLED STUDY; CORRELATION ANALYSIS; GENE AMPLIFICATION; GENE EXPRESSION; HUMAN; MAJOR CLINICAL STUDY; MICROARRAY ANALYSIS; PRIORITY JOURNAL; UPREGULATION;

BREAST NEOPLASMS; CELL PROLIFERATION; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; LYMPHOCYTES; MULTIGENE FAMILY; NEOPLASM INVASIVENESS; PRINCIPAL COMPONENT ANALYSIS; RECEPTOR, ERBB-2; RECURRENCE; RNA, MESSENGER;

EID: 36348993857     PISSN: 00085472     EISSN: None     Source Type: Journal    
DOI: 10.1158/0008-5472.CAN-07-0539     Document Type: Article

References (36)

1. Anim JT, John B, Abdulsathar SS, et al. Relationship between the expression of various markers and prognostic factors in breast cancer. Acta Histochem 2005;107:87-93.
2. Gruvberger S, Ringner M, Chen Y, et al. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res 2001;61:5979-84.
3. Diermeier S, Horvath G, Knuechel-Clarke R, Hofstaedter F, Szollosi J, Brockhoff G. Epidermal growth factor receptor coexpression modulates susceptibility to Herceptin in HER2/neu overexpressing breast cancer cells via specific erbB-receptor interaction and activation. Exp Cell Res 2005;304:604-19.
4. van de Vijver MJ, He YD, van't Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347:1999-2009.
5. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:2817-26.
6. Wang Y, Klijn JG, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005;365:671-9.
7. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000;406:747-52.
8. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001;98:10869-74.
9. Alexe G, Dalgin GS, Ramaswamy R, DeLisi C, Bhanot G. Data perturbation independent diagnosis and validation of breast cancer subtypes using clustering and patterns. Cancer Informatics 2006;2:243-74.
10. Sorlie T, Wang Y, Xiao C, et al. Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms. BMC Genomics 2006;7:127.
11. Sotiriou C, Neo SY, McShane LM, et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A 2003;100:10393-8.
12. Sotiriou C, Powles TJ, Dowsett M, et al. Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer. Breast Cancer Res 2002;4:R3.
13. Bertucci F, Eisinger F, Houlgatte R, Viens P, Birnbaum D. Gene-expression profiling and identification of patients at high risk of breast cancer. Lancet 2002;360:173-4; author reply 4.
14. Gosset WS. The probable error of a mean. Biometrika 1908;6:1-25.
15. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98:5116-21.
16. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science (New York, NY) 1999;286:531-7.
17. Lyons-Weiler J, Patel S, Becich MJ, Godfrey TE. Tests for finding complex patterns of differential expression in cancers: towards individualized medicine. BMC Bioinformatics 2004;5:110.
18. Alexe G, Hammer PL. Spanned patterns for the logical analysis of data discrete applied mathematics 2006;154:1039-49.
19. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning J 2003;52:91-118.
20. Strehl A, Ghosh J. Cluster ensembles: a knowledge reuse framework for combining partitionings. Eighteenth National Conference on Artificial Intelligence; 2002 July 28-August 01, 2002; Edmonton, Alberta, Canada; 2002. p. 93-8.
21. Bertucci F, Borie N, Ginestier C, et al. Identification and validation of an ERBB2 gene expression signature in breast cancers. Oncogene 2004;23:2564-75.
22. Kauraniemi P, Kallioniemi A. Activation of multiple cancer-associated genes at the ERBB2 amplicon in breast cancer. Endocr Relat Cancer 2006;13:39-49.
23. Harris LN, You F, Schnitt SJ, et al. Predictors of resistance to preoperative trastuzumab and vinorelbine for HER2-positive early breast cancer. Clin Cancer Res 2007;13:1198-207.
24. Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a dataset via the Gap statistic. Journal of the Royal Statistics Society (Series B) 2001;63:411-23.
25. Kaufmann L, Rousseeuw PJ. Finding groups in data: an introduction to cluster analysis. 1st ed: John Wiley & Sons; 1990.
26. Mi H, Lazareva-Ulitsky B, Loo R, et al. The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res 2005;33:D284-8.
27. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545-50.
28. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006;355:560-9.
29. Pupa SM, Bufalino R, Invernizzi AM, et al. Macrophage infiltrate and prognosis in c-erbB-2-overexpressing breast carcinomas. J Clin Oncol 1996;14:85-94.
30. Rilke F, Colnaghi MI, Cascinelli N, et al. Prognostic significance of HER-2/neu expression in breast cancer and its relationship to other prognostic factors. Int J Cancer 1991;49:44-9.
31. Zhang L, Conejo-Garcia JR, Katsaros D, et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N Engl J Med 2003;348:203-13.
32. Pages F, Berger A, Camus M, et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med 2005;353:2654-66.
33. Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science (New York, NY) 2006;313:1960-4.
34. Wolf D, Wolf AM, Rumpold H, et al. The expression of the regulatory T cell-specific forkhead box transcription factor FoxP3 is associated with poor prognosis in ovarian cancer. Clin Cancer Res 2005;11:8326-31.
35. Dranoff G. The therapeutic implications of intratumoral regulatory T cells. Clin Cancer Res 2005;11:8226-9.
36. Gennari R, Menard S, Fagnoni F, et al. Pilot study of the mechanism of action of preoperative trastuzumab in patients with primary operable breast tumors over-expressing HER2. Clin Cancer Res 2004;10:5650-5.