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01-08-2012 | Brief Report

Feature extraction via composite scoring and voting in breast cancer

Authors: Martin Koch, Markus Hanl, Michael Wiese

Published in: Breast Cancer Research and Treatment | Issue 1/2012

Abstract

Identification and characterization of tumor subtypes using gene expression profiles of triple negative breast cancer patients. Microarray data of four breast cancer studies were pooled and evaluated. Molecular subtype classification was performed using random forest and a novel algorithm for feature extraction via composite scoring and voting. Biological and clinical properties were evaluated via GSEA, functional annotation clustering and clinical endpoint analysis. The subtype signatures are highly predictive for distant metastasis free survival of tamoxifen-treated patients. Consensus clustering and the novel algorithm proposed three triple negative subtypes. One subtype shows low E2F4 gene expression and is predictive for survival of ER negative breast cancer patients. The other two subtypes share commonalities with luminal B tumors. Classification of breast cancer expression profiles may reveal novel tumor subtypes, possessing clinical impact. Furthermore, subtype characterizing gene signatures might hold potential for novel strategies in cancer therapy.

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Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537PubMedCrossRef

Rhodes DR, Kalyana-sundaram S, Mahavisno V, Barrette TR, Ghosh D (2005) Mining for regulatory programs in the cancer transcriptome. Nat Genet 37:579–583. doi:10.1038/ng1578 PubMedCrossRef

Schachtner R, Lutter D, Knollmüller P, Tomé a M, Theis FJ, Schmitz G, Stetter M, Vilda PG, Lang EW, Biophysics C (2008) Knowledge-based gene expression classification via matrix factorization. Bioinformatics 24:1688–1697. doi:10.1093/bioinformatics/btn245 PubMedCrossRef

Zhang Y, Sieuwerts AM, McGreevy M, Casey G, Cufer T, Paradiso A, Harbeck N, Span PN, Hicks DG, Crowe J, Tubbs RR, Budd GT, Lyons J, Sweep FC, Schmitt M, Schittulli F, Golouh R, Talantov D, Wang Y, Foekens JA (2009) The 76-gene signature defines high-risk patients that benefit from adjuvant tamoxifen therapy. Breast Cancer Res Treat 116:303–309. doi:10.1007/s10549-008-0183-2 PubMedCrossRef

Minn AJ, Gupta GP, Siegel PM, Bos PD, Shu W, Giri DD, Viale A, Olshen AB, Gerald WL, Massague J (2005) Genes that mediate breast cancer metastasis to lung. Nature 436:518–524. doi:10.1038/nature03799 PubMedCrossRef

Finak G, Sadekova S, Pepin F, Hallett M, Meterissian S, Halwani F, Khetani K, Souleimanova M, Zabolotny B, Omeroglu A, Park M (2006) Gene expression signatures of morphologically normal breast tissue identify basal-like tumors. Breast Cancer Res 8:R58. doi:10.1186/bcr1608 PubMedCrossRef

Miller LD, Coffman LG, Chou JW, Black MA, Bergh J, D’Agostino R Jr, Torti SV, Torti FM (2011) An iron regulatory gene signature predicts outcome in breast cancer. Cancer Res 71:6728–6737. doi:10.1158/0008-5472.CAN-11-1870 PubMedCrossRef

Buffa FM, Harris a L, West CM, Miller CJ (2010) Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene. Br J Cancer 102:428–435. doi:10.1038/sj.bjc.6605450 PubMedCrossRef

Pawitan Y, Bjöhle J, Amler L et al (2005) Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res 7:R953–R964. doi:10.1186/bcr1325 PubMedCrossRef

10.

Higgins MJ, Baselga J (2011) Targeted therapies for breast cancer. J Clin Invest 121:3797–3803. doi:10.1172/JCI57152 PubMedCrossRef

11.

Staudacher L, Cottu PH, Dieras V, Vincent-Salomon A, Guilhaume MN, Escalup L, Dorval T, Beuzeboc P, Mignot L, Pierga JY (2011) Platinum-based chemotherapy in metastatic triple-negative breast cancer: the Institute Curie experience. Ann Oncol 22:848–856. doi:10.1093/annonc/mdq461 PubMedCrossRef

12.

Kreike B, van Kouwenhove M, Horlings H, Weigelt B, Peterse H, Bartelink H, van de Vijver MJ (2007) Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas. Breast Cancer Res 9:R65. doi:10.1186/bcr1771 PubMedCrossRef

13.

Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA (2011) Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest 121:2750–2767. doi:10.1172/JCI45014DS1 PubMedCrossRef

14.

Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP, Poggio T, Gerald W, Loda M, Lander ES, Golub TR (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 98:15149–15154. doi:10.1073/pnas.211566398 PubMedCrossRef

15.

Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99:6567–6572. doi:10.1073/pnas.082099299 PubMedCrossRef

16.

Li T, Zhang C, Ogihara M (2004) A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20:2429–2437. doi:10.1093/bioinformatics/bth267 PubMedCrossRef

17.

Loi S, Haibe-Kains B, Desmedt C, Wirapati P, Lallemand F, Tutt AM, Gillet C, Ellis P, Ryder K, Reid JF, Daidone MG, Pierotti MA, Berns EM, Jansen MP, Foekens JA, Delorenzi M, Bontempi G, Piccart MJ, Sotiriou C (2008) Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen. BMC Genomics 9:239. doi:10.1186/1471-2164-9-239 PubMedCrossRef

18.

Haibe-Kains B, Desmedt C, Loi S, Culhane AC, Bontempi G, Quackenbush J, Sotiriou C (2012) A three-gene model to robustly identify breast cancer molecular subtypes. J Natl Cancer Inst 104:311–325. doi:10.1093/jnci/djr545 PubMedCrossRef

19.

Burstein HJ, Griggs JJ (2012) Deep time: the long and the short of adjuvant endocrine therapy for breast cancer. J Clin Oncol 30:684–686. doi:10.1200/JCO.2011.40.1455 PubMedCrossRef

20.

Popovici V, Chen W, Gallas BG, Hatzis C, Shi W, Samuelson FW, Nikolsky Y, Tsyganova M, Ishkin A, Nikolskaya T, Hess KR, Valero V, Booser D, Delorenzi M, Hortobagyi GN, Shi L, Symmans WF, Pusztai L (2010) Effect of training-sample size and classification difficulty on the accuracy of genomic predictors. Breast Cancer Res 12:R5. doi:10.1186/bcr2468 PubMedCrossRef

21.

Slawski M, Daumer M, Boulesteix AL (2008) CMA: a comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9:439. doi:10.1186/1471-2105-9-439 PubMedCrossRef

22.

Monti S, Tamayo P, Mesirov J, Golub T (2003) Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn 52:91–118CrossRef

23.

Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102:15545–15550. doi:10.1073/pnas.0506580102 PubMedCrossRef

24.

Ringner M, Fredlund E, Häkkinen J, Borg A, Staaf J (2011) GOBO: gene expression-based outcome for breast cancer online. PLoS ONE 6:e17911. doi:10.1371/journal.pone.0017911.g001 PubMedCrossRef

25.

Barrett T, Suzek TO, Troup DB et al (2005) NCBI GEO: mining millions of expression profiles–database and tools. Nucleic Acids Res 33:D562–D566. doi:10.1093/nar/gki022 PubMedCrossRef

26.

Brazma A (2003) ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 31:68–71. doi:10.1093/nar/gkg091 PubMedCrossRef

27.

Li Y, Zou L, Li Q, Haibe-Kains B, Tian R, Desmedt C, Sotiriou C, Szallasi Z, Iglehart JD, Richardson AL, Wang ZC (2010) Amplification of LAPTM4B and YWHAZ contributes to chemotherapy resistance and recurrence of breast cancer. Nat Med 16:214–218. doi:10.1038/nm.2090 PubMedCrossRef

28.

Shi L, Campbell G, Jones WD et al (2010) The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 28:827–838. doi:10.1038/nbt.1665 PubMedCrossRef

29.

Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, Clark L, Bayani N, Coppe J-P, Tong F, Speed T, Spellman PT, DeVries S, Lapuk A, Wang NJ, Kuo W-L, Stilwell JL, Pinkel D, Albertson DG, Waldman FM, McCormick F, Dickson RB, Johnson MD, Lippman M, Ethier S, Gazdar A, Gray JW (2006) A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10:515–527. doi:10.1016/j.ccr.2006.10.008 PubMedCrossRef

30.

Watanabe T, Komuro Y, Kiyomatsu T, Kanazawa T, Kazama Y, Tanaka J, Tanaka T, Yamamoto Y, Shirane M, Muto T, Nagawa H (2006) Prediction of sensitivity of rectal cancer cells in response to preoperative radiotherapy by DNA microarray analysis of gene expression profiles. Cancer Res 66:3370–3374. doi:10.1158/0008-5472.CAN-05-3834 PubMedCrossRef

31.

Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, Viale G, Delorenzi M, Zhang Y, d’Assignies MS, Bergh J, Lidereau R, Ellis P, Harris AL, Klijn JG, Foekens JA, Cardoso F, Piccart MJ, Buyse M, Sotiriou C, Consortium T C (2007) Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res 13:3207–3214. doi:10.1158/1078-0432.CCR-06-2765 PubMedCrossRef

32.

Wilson CL, Miller CJ (2005) Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis. Bioinformatics 21:3683–3685. doi:10.1093/bioinformatics/bti605 PubMedCrossRef

33.

Irizarry R, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264. doi:10.1093/biostatistics/4.2.249 PubMedCrossRef

34.

Rudy J, Valafar F (2011) Empirical comparison of cross-platform normalization methods for gene expression data. BMC Bioinformatics 12:467. doi:10.1186/1471-2105-12-467 PubMedCrossRef

35.

Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999PubMedCrossRef

36.

Guyon I, Weston J, Barnhil S, Vapnik VN (2002) Gene selection for cancer classification using support vector machine. Mach Learn 46:389–422CrossRef

37.

Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRef

38.

Friedman JH (2001) Greedy function approximation: a gradient boosting machine. IMS 1999 Reitz Lecture

39.

Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3. doi:10.2202/1544-6115.1027

40.

Brier G (1950) Verification of forcasts expressed in terms of probability. Mon Weather Rev 78:1–3CrossRef

41.

Wilkerson MD, Hayes DN (2010) ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26:1572–1573. doi:10.1093/bioinformatics/btq170 PubMedCrossRef

42.

Mayer EL, Baurain JF, Sparano J, Strauss L, Campone M, Fumoleau P, Rugo H, Awada A, Sy O, Llombart-Cussac A (2011) A phase 2 trial of dasatinib in patients with advanced HER2-positive and/or hormone receptor-positive breast cancer. Clin Cancer Res 17:6897–6904. doi:10.1158/1078-0432.CCR-11-0070 PubMedCrossRef

43.

Silver DP, Richardson AL, Eklund AC, Wang ZC, Szallasi Z, Li Q, Juul N, Leong CO, Calogrias D, Buraimoh A, Fatima A, Gelman RS, Ryan PD, Tung NM, De Nicolo A, Ganesan S, Miron A, Colin C, Sgroi DC, Ellisen LW, Winer EP, Garber JE (2010) Efficacy of neoadjuvant cisplatin in triple-negative breast cancer. J Clin Oncol 28:1145–1153. doi:10.1200/JCO.2009.22.4725 PubMedCrossRef

44.

Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8:R183. doi:10.1186/gb-2007-8-9-r183 CrossRef

45.

Li J, Wang CY (2008) TBL1–TBLR1 and beta-catenin recruit each other to Wnt target-gene promoter for transcription activation and oncogenesis. Nat Cell Biol 10:160–169. doi:10.1038/ncb1684 PubMedCrossRef

46.

Wang H, Shao N, Ding QM, Cui J-q, Reddy ESP, Rao VN (1997) BRCA1 proteins are transported to the nucleus in the absence of serum and splice variants BRCA1a, BRCA1b are tyrosine phosphoproteins that associate with E2F, cyclins and cyclin dependent kinases. Oncogene 15:143–157PubMedCrossRef

47.

Bindra RS, Glazer PM (2007) Repression of RAD51 gene expression by E2F4/p130 complexes in hypoxia. Oncogene 26:2048–2057. doi:10.1038/sj.onc.1210001 PubMedCrossRef

48.

Byrski T, Huzarski T, Dent R, Gronwald J, Zuziak D, Cybulski C, Kladny J, Gorski B, Lubinski J, Narod SA (2009) Response to neoadjuvant therapy with cisplatin in BRCA1-positive breast cancer patients. Breast Cancer Res Treat 115:359–363. doi:10.1007/s10549-008-0128-9 PubMedCrossRef

Title: Feature extraction via composite scoring and voting in breast cancer
Authors: Martin Koch
Markus Hanl
Michael Wiese
Publication date: 01-08-2012
Publisher: Springer US
Published in: Breast Cancer Research and Treatment / Issue 1/2012
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI: https://doi.org/10.1007/s10549-012-2177-3

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