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Breast Cancer Research
Published in: Breast Cancer Research 4/2008

Open Access 01-08-2008 | Research article

A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer

Authors: Andrew E Teschendorff, Carlos Caldas

Published in: Breast Cancer Research | Issue 4/2008

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Abstract

Introduction

Patients with primary operable oestrogen receptor (ER) negative (-) breast cancer account for about 30% of all cases and generally have a worse prognosis than ER-positive (+) patients. Nevertheless, a significant proportion of ER- cases have favourable outcomes and could potentially benefit from a less aggressive course of therapy. However, identification of such patients with a good prognosis remains difficult and at present is only possible through examining histopathological factors.

Methods

Building on a previously identified seven-gene prognostic immune response module for ER- breast cancer, we developed a novel statistical tool based on Mixture Discriminant Analysis in order to build a classifier that could accurately identify ER- patients with a good prognosis.

Results

We report the construction of a seven-gene expression classifier that accurately predicts, across a training cohort of 183 ER- tumours and six independent test cohorts (a total of 469 ER- tumours), ER- patients of good prognosis (in test sets, average predictive value = 94% [range 85 to 100%], average hazard ratio = 0.15 [range 0.07 to 0.36] p < 0.000001) independently of lymph node status and treatment.

Conclusions

This seven-gene classifier could be used in a polymerase chain reaction-based clinical assay to identify ER- patients with a good prognosis, who may therefore benefit from less aggressive treatment regimens.
Appendix
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Literature
1.
Rakha EA, El-Rehim DA, Paish C, Green AR, Lee AH, Robertson JF, Blamey RW, Macmillan D, Ellis IO: Basal phenotype identifies a poor prognostic subgroup of breast cancer of clinical importance. Eur J Cancer. 2006, 42: 3149-3156. 10.1016/j.ejca.2006.08.015.CrossRefPubMed
2.
Rakha EA, El-Sayed ME, Green AR, Lee AH, Robertson JF, Ellis IO: Prognostic markers in triple-negative breast cancer. Cancer. 2007, 109: 25-32. 10.1002/cncr.22381.CrossRefPubMed
3.
Vijver van de MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, Velde van der T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002, 347: 1999-2009. 10.1056/NEJMoa021967.CrossRefPubMed
4.
Eden P, Ritz C, Rose C, Ferno M, Peterson C: 'Good Old' clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers. Eur J Cancer. 2004, 40: 1837-1841. 10.1016/j.ejca.2004.02.025.CrossRefPubMed
5.
Teschendorff AE, Miremadi A, Pinder SE, Ellis IO, Caldas C: An immune response gene expression module iden-tifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol. 2007, 8: R157-10.1186/gb-2007-8-8-r157.CrossRefPubMedPubMedCentral
6.
Kreike B, van Kouwenhove M, Horlings H, Weigelt B, Peterse H, Bartelink H, Vijver van de MJ: Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas. Breast Cancer Res. 2007, 9: R65-10.1186/bcr1771.CrossRefPubMedPubMedCentral
7.
Jumppanen M, Gruvberger-Saal S, Kauraniemi P, Tanner M, Bendahl PO, Lundin M, Krogh M, Kataja P, Borg A, Ferno M, Isola J: Basal-like phenotype is not associated with patient survival in estrogen-receptor-negative breast cancers. Breast Cancer Res. 2007, 9: R16-10.1186/bcr1649.CrossRefPubMedPubMedCentral
8.
Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005, 365: 671-679.CrossRefPubMed
9.
Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B, Desmedt C, Larsimont D, Cardoso F, Peterse H, Nuyten D, Buyse M, Vijver Van de MJ, Bergh J, Piccart M, Delorenzi M: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006, 98: 262-272.CrossRefPubMed
10.
Naderi A, Teschendorff AE, Barbosa-Morais NL, Pinder SE, Green AR, Powe DG, Robertson JF, Aparicio S, Ellis IO, Brenton JD, Caldas C: A gene-expression signature to predict survival in breast cancer across independent data sets. Oncogene. 2007, 26: 1507-1516. 10.1038/sj.onc.1209920.CrossRefPubMed
11.
Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C: PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer. Bioinformatics. 2006, 22: 2269-2275. 10.1093/bioinformatics/btl174.CrossRefPubMed
12.
Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, Pawitan Y, Hall P, Klaar S, Liu ET, Bergh J: 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: 13550-13555. 10.1073/pnas.0506230102.CrossRefPubMedPubMedCentral
13.
Alexe G, Dalgin GS, Scanfeld D, Tamayo P, Mesirov JP, DeLisi C, Harris L, Barnard N, Martel M, Levine AJ, Ganesan S, Bhanot G: High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. Cancer Res. 2007, 67: 10669-10676. 10.1158/0008-5472.CAN-07-0539.CrossRefPubMed
14.
Schmidt M, Böhm D, von Törne C, Steiner E, Puhl A, Pilch H, Lehr HA, Hengstler JG, Kölbl H, Gehrmann M: The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res. 2008, 68: 5405-5413. 10.1158/0008-5472.CAN-07-5206.CrossRefPubMed
15.
Calabrò A, Beissbarth T, Kuner R, Stojanov M, Benner A, Asslaber M, Ploner F, Zatloukal K, Samonigg H, Poustka A, Sültmann H: Effects of infiltrating lymphocytes and estrogen receptor on gene expression and prognosis in breast cancer. Breast Cancer Res Treat. 2008
16.
Desmedt C, Haibe-Kains B, Wirapati P, Buyse M, Larsimont D, Bontempi G, Delorenzi M, Piccart M, Sotiriou C: Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res. 2008, 14: 5158-5165. 10.1158/1078-0432.CCR-07-4756.CrossRefPubMed
17.
Olivotto IA, Bajdik CD, Ravdin PM, Speers CH, Coldman AJ, Norris BD, Davis GJ, Chia SK, Gelmon KA: Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol. 2005, 23: 2716-2725. 10.1200/JCO.2005.06.178.CrossRefPubMed
18.
Hoadley KA, Weigman VJ, Fan C, Sawyer LR, He X, Troester MA, Sartor CI, Rieger-House T, Bernard PS, Carey LA, Perou CM: EGFR associated expression profiles vary with breast tumor subtype. BMC Genomics. 2007, 8: 258-10.1186/1471-2164-8-258.CrossRefPubMedPubMedCentral
19.
Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, Kuo WL, Lapuk A, Neve RM, Qian Z, Ryder T, Chen F, Feiler H, Tokuyasu T, Kingsley C, Dairkee S, Meng Z, Chew K, Pinkel D, Jain A, Ljung BM, Esserman L, Albertson DG, Waldman FM, Gray JW: Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell. 2006, 10: 529-541. 10.1016/j.ccr.2006.10.009.CrossRefPubMed
20.
Loi S, Haibe-Kains B, Desmedt C, Lallemand F, Tutt AM, Gillet C, Ellis P, Harris A, Bergh J, Foekens JA, Klijn JG, Larsimont D, Buyse M, Bontempi G, Delorenzi M, Piccart MJ, Sotiriou C: Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol. 2007, 25: 1239-1246. 10.1200/JCO.2006.07.1522.CrossRefPubMed
21.
Fraley C, Raftery AE: Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association. 2002, 97: 611-631. 10.1198/016214502760047131.CrossRef
22.
Hastie T, Tibshirani R, Friedman J: The elements of statistical learning: data mining, inference and prediction. 2001, Springer. New York, Chapter 4:CrossRef
23.
Teschendorff AE, Wang Y, Barbosa-Morais NL, Brenton JD, Caldas C: A variational Bayesian mixture modeling framework for cluster analysis of gene-expression data. Bioinformatics. 2005, 21: 3025-3033. 10.1093/bioinformatics/bti466.CrossRefPubMed
24.
Heagerty PJ, Lumley T, Pepe MS: Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000, 56: 337-344. 10.1111/j.0006-341X.2000.00337.x.CrossRefPubMed
25.
Simon R: Development and validation of therapeutically relevant multi-gene biomarker classifiers. J Natl Cancer Inst. 2005, 97: 866-867.CrossRefPubMed
26.
Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, Livasy C, Carey LA, Reynolds E, Dressler L, Nobel A, Parker J, Ewend MG, Sawyer LR, Wu J, Liu Y, Nanda R, Tretiakova M, Ruiz Orrico A, Dreher D, Palazzo JP, Perreard L, Nelson E, Mone M, Hansen H, Mullins M, Quackenbush JF, Ellis MJ, Olopade OI, Bernard PS, et al: The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 2006, 7: 96-10.1186/1471-2164-7-96.CrossRefPubMedPubMedCentral
27.
Bertucci F, Finetti P, Cervera N, Charafe-Jauffret E, Mamessier E, Adelaide J, Debono S, Houvenaeghel G, Maraninchi D, Viens P, Charpin C, Jacquemier J, Birnbaum D: Gene expression profiling shows medullary breast cancer is a subgroup of basal breast cancers. Cancer Res. 2006, 66: 4636-4644. 10.1158/0008-5472.CAN-06-0031.CrossRefPubMed
28.
Kaufman L, Rousseeuw P: Finding Groups in Data: An introduction to Cluster Analysis. Wiley Series in Probability and Statistics. 2005, New Jersey: John Wiley & Sons, Chapter 2:
Metadata
Title
A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer
Authors
Andrew E Teschendorff
Carlos Caldas
Publication date
01-08-2008
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 4/2008
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/bcr2138

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