Protein expression based multimarker analysis of breast cancer samples

BMC Cancer

Angela P Presson¹  Nam K Yoon⁴  Lora Bagryanova⁴  Vei Mah⁴  Mohammad Alavi⁴  Erin L Maresh⁴  Ayyappan K Rajasekaran²  Lee Goodglick³  David Chia³  Steve Horvath³ 

[1] Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA

[2] Nemours Center for Childhood Cancer Research, Wilmington, DE, USA

[3] Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA

[4] Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA

关键词: WGCNA;    prognostic marker;    tumor marker;    breast cancer;    Tissue microarray;   

Others  :  1080905
DOI  :  10.1186/1471-2407-11-230

 received in 2010-10-13, accepted in 2011-06-08,  发布年份 2011

【 摘 要 】

Background

Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.

Methods

We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.

Results

We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.

Conclusions

We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.

【 授权许可】

   
2011 Presson et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Kreike B, van Kouwenhove M, Horlings H, Weigelt B, Peterse H, Bartelink H, van de Vijver MJ: Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas. Breast Cancer Res 2007, 9(5):R65. BioMed Central Full Text
• [2]Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al.: Molecular portraits of human breast tumours. Nature 2000, 406(6797):747-752.
• [3]Siziopikou KP, Cobleigh M: The basal subtype of breast carcinomas may represent the group of breast tumors that could benefit from EGFR-targeted therapies. Breast 2007, 16(1):104-107.
• [4]Ryden L, Jirstrom K, Haglund M, Stal O, Ferno M: Epidermal growth factor receptor and vascular endothelial growth factor receptor 2 are specific biomarkers in triple-negative breast cancer. Results from a controlled randomized trial with long-term follow-up. Breast Cancer Res Treat
• [5]Brennan DJ, Gallagher WM: Prognostic ability of a panel of immunohistochemistry markers - retailoring of an 'old solution'. Breast Cancer Res 2008, 10(1):102. BioMed Central Full Text
• [6]Jacquemier J, Charafe-Jauffret E, Monville F, Esterni B, Extra JM, Houvenaeghel G, Xerri L, Bertucci F, Birnbaum D: Association of GATA3, P53, Ki67 status and vascular peritumoral invasion are strongly prognostic in luminal breast cancer. Breast Cancer Res 2009, 11(2):R23. BioMed Central Full Text
• [7]Abramovitz M, Leyland-Jones B: A systems approach to clinical oncology: focus on breast cancer. Proteome Sci 2006, 4:5. BioMed Central Full Text
• [8]Kay E, O'Grady A, Morgan JM, Wozniak S, Jasani B: Use of tissue microarray for interlaboratory validation of HER2 immunocytochemical and FISH testing. J Clin Pathol 2004, 57(11):1140-1144.
• [9]Kelleher MT, Fruhwirth G, Patel G, Ofo E, Festy F, Barber PR, Ameer-Beg SM, Vojnovic B, Gillett C, Coolen A, et al.: The potential of optical proteomic technologies to individualize prognosis and guide rational treatment for cancer patients. Target Oncol 2009, 4(3):235-252.
• [10]Zhang B, Horvath S: A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005., 4(1) Article 17
• [11]Langfelder P, Horvath S: WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559. BioMed Central Full Text
• [12]Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, Carlson S, Helgason A, Walters GB, Gunnarsdottir S, et al.: Genetics of gene expression and its effect on disease. Nature 2008, 452(7186):423-428.
• [13]Fuller TF, Ghazalpour A, Aten JE, Drake TA, Lusis AJ, Horvath S: Weighted gene coexpression network analysis strategies applied to mouse weight. Mamm Genome 2007, 18(6-7):463-472.
• [14]Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z, et al.: Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA 2006, 103(46):17402-17407.
• [15]Mason MJ, Fan G, Plath K, Zhou Q, Horvath S: Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells. BMC Genomics 2009, 10:327. BioMed Central Full Text
• [16]Presson AP, Sobel E, Papp JC, Horvath S: Integration of genetic and genomic approaches for the analysis of chronic fatigue syndrome identifies 184 candidate genes. Critical Assessment of Microarray Data Analysis conference proceedings 2006.
• [17]Kononen J, Bubendorf L, Kallioniemi A, Barlund M, Schraml P, Leighton S, Torhorst J, Mihatsch MJ, Sauter G, Kallioniemi OP: Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat Med 1998, 4(7):844-847.
• [18]Shen D, Nooraie F, Elshimali Y, Lonsberry V, He J, Bose S, Chia D, Seligson D, Chang HR, Goodglick L: Decreased expression of annexin A1 is correlated with breast cancer development and progression as determined by a tissue microarray analysis. Hum Pathol 2006, 37(12):1583-1591.
• [19]Seligson DB, Hongo F, Huerta-Yepez S, Mizutani Y, Miki T, Yu H, Horvath S, Chia D, Goodglick L, Bonavida B: Expression of X-Linked Inhibitor of Apoptosis Protein Is a Strong Predictor of Human Prostate Cancer Recurrence. Clin Cancer Res 2007, 13(20):6056-6063.
• [20]Yoon NK, Maresh EL, Elshimali Y, Li A, Horvath S, Seligson DB, Chia D, Goodglick L: Elevated MED28 expression predicts poor outcome in women with breast cancer. BMC Cancer 2010, 10:335. BioMed Central Full Text
• [21]Yoon NK, Seligson DB, Chia D, Elshimali Y, Sulur G, Li A, Horvath S, Maresh E, Mah V, Bose S, et al.: Higher expression levels of 14-3-3 sigma in ductal carcinoma in situ of the breast predict poorer outcome. Cancer Biomark 2009, 5(4):215-224.
• [22]Mah V, Seligson DB, Li A, Marquez DC, Wistuba II, Elshimali Y, Fishbein MC, Chia D, Pietras RJ, Goodglick L: Aromatase expression predicts survival in women with early-stage non small cell lung cancer. Cancer Res 2007, 67(21):10484-10490.
• [23]Liu X, Minin V, Huang Y, Seligson DB, Horvath S: Statistical methods for analyzing tissue microarray data. J Biopharm Stat 2004, 14(3):671-685.
• [24]Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, Pawitan Y, Hall P, Klaar S, Liu ET, et al.: An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA 2005, 102(38):13550-13555.
• [25]Pawitan Y, Bjohle J, Amler L, Borg AL, 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(6):R953-964. BioMed Central Full Text
• [26]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(4):262-272.
• [27]Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH: Functional organization of the transcriptome in human brain. Nat Neurosci 2008, 11(11):1271-1282.
• [28]Johnson WE, Li C, Rabinovic A: Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007, 8(1):118-127.
• [29]Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z, et al.: Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA 2006, 103(46):17402-17407.
• [30]Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R, Brozell A, Schadt EE, Drake TA, Lusis AJ, et al.: Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet 2006, 2(8):e130.
• [31]R Development Core Team: R: A Language and Environment for Statistical Computing [http://www.R-project.org] webcite In. R Foundation for Statistical Computing, Vienna, Austria; 2010: ISBN 3-900051-900007-900050, URL
• [32]Yip AM, Horvath S: Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics 2007, 8:22. BioMed Central Full Text
• [33]Li A, Horvath S: Network neighborhood analysis with the multi-node topological overlap measure. Bioinformatics 2007, 23(2):222-231.
• [34]Langfelder P, Zhang B, Horvath S: Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 2008, 24(5):719-720.
• [35]Horvath S: Weighted Network Analysis: Applications in Genomics and Systems Biology. 1st edition. New York: Springer; 2011.
• [36]Langfelder P, Luo R, Oldham MC, Horvath S: Is my network module preserved and reproducible? PLoS Comput Biol 2011, 7(1):e1001057.
• [37]Mahalingam M, Nguyen LP, Richards JE, Muzikansky A, Hoang MP: The diagnostic utility of immunohistochemistry in distinguishing primary skin adnexal carcinomas from metastatic adenocarcinoma to skin: an immunohistochemical reappraisal using cytokeratin 15, nestin, p63, D2-40, and calretinin. Mod Pathol 2010, 23(5):713-719.
• [38]Meleth S, Chatla C, Katkoori VR, Anderson B, Hardin JM, Jhala NC, Bartolucci A, Grizzle WE, Manne U: Comparison of predicted probabilities of proportional hazards regression and linear discriminant analysis methods using a colorectal cancer molecular biomarker database. Cancer Inform 2007, 3:115-122.
• [39]Mikkola A, Aro J, Rannikko S, Ruutu M: Prognostic grouping of metastatic prostate cancer using conventional pretreatment prognostic factors. Scand J Urol Nephrol 2009, 43(4):265-270.
• [40]Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, 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(27):2817-2826.
• [41]van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415(6871):530-536.
• [42]Fiore E, Campani D, Muller I, Belardi V, Giustarini E, Rossi G, Pinchera A, Giani C: IGF-II mRNA expression in breast cancer: predictive value and relationship to other prognostic factors. Int J Biol Markers 2010, 25(3):150-156.
• [43]Fang WB, Jokar I, Chytil A, Moses HL, Abel T, Cheng N: Loss of one Tgfbr2 allele in fibroblasts promotes metastasis in MMTV: polyoma middle T transgenic and transplant mouse models of mammary tumor progression. Clin Exp Metastasis 2011, 28(4):351-366.
• [44]Yancy HF, Mason JA, Peters S, Thompson CE, Littleton GK, Jett M, Day AA: Metastatic progression and gene expression between breast cancer cell lines from African American and Caucasian women. J Carcinog 2007, 6:8. BioMed Central Full Text