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BMC Cancer
Published in: BMC Cancer 1/2016

Open Access 01-12-2016 | Research article

The effects of lymph node status on predicting outcome in ER+ /HER2- tamoxifen treated breast cancer patients using gene signatures

Authors: Jessica G. Cockburn, Robin M. Hallett, Amy E. Gillgrass, Kay N. Dias, T. Whelan, M. N. Levine, John A. Hassell, Anita Bane

Published in: BMC Cancer | Issue 1/2016

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Abstract

Background

Lymph node (LN) status is the most important prognostic variable used to guide ER positive (+) breast cancer treatment. While a positive nodal status is traditionally associated with a poor prognosis, a subset of these patients respond well to treatment and achieve long-term survival. Several gene signatures have been established as a means of predicting outcome of breast cancer patients, but the development and indication for use of these assays varies. Here we compare the capacity of two approved gene signatures and a third novel signature to predict outcome in distinct LN negative (-) and LN+ populations. We also examine biological differences between tumours associated with LN- and LN+ disease.

Methods

Gene expression data from publically available data sets was used to compare the ability of Oncotype DX and Prosigna to predict Distant Metastasis Free Survival (DMFS) using an in silico platform. A novel gene signature (Ellen) was developed by including patients with both LN- and LN+ disease and using Prediction Analysis of Microarrays (PAM) software. Gene Set Enrichment Analysis (GSEA) was used to determine biological pathways associated with patient outcome in both LN- and LN+ tumors.

Results

The Oncotype DX gene signature, which only used LN- patients during development, significantly predicted outcome in LN- patients, but not LN+ patients. The Prosigna gene signature, which included both LN- and LN+ patients during development, predicted outcome in both LN- and LN+ patient groups. Ellen was also able to predict outcome in both LN- and LN+ patient groups. GSEA suggested that epigenetic modification may be related to poor outcome in LN- disease, whereas immune response may be related to good outcome in LN+ disease.

Conclusions

We demonstrate the importance of incorporating lymph node status during the development of prognostic gene signatures. Ellen may be a useful tool to predict outcome of patients regardless of lymph node status, or for those with unknown lymph node status. Finally we present candidate biological processes, unique to LN- and LN+ disease, that may indicate risk of relapse.
Appendix
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Literature
1.
CCO. Surgical Management of Early-Stage Invasive Breast Cancer Overview Guideline Report History. 2011.
2.
NCCN. Practice Guidelines in Oncology. 2012.
3.
Fisher B, Dignam J, Wolmark N, DeCillis A, Emir B, Wickerham DL, Bryant J, Dimitrov NV, Abramson N, Atkins JN, Shibata H, Deschenes L, Margolese RG. Tamoxifen and chemotherapy for lymph node-negative, estrogen receptor-positive breast cancer. J Natl Cancer Inst. 1997;89:1673–82.CrossRefPubMed
4.
Muss HB, Woolf S, Berry D, Cirrincione C, Weiss RB, Budman D, Wood WC, Henderson IC, Hudis C, Winer E, Cohen H, Wheeler J, Norton L. Adjuvant chemotherapy in older and younger women with lymph node-positive breast cancer. JAMA. 2005;293:1073–81.CrossRefPubMed
5.
Capulli M, Angelucci A, Driouch K, Garcia T, Clement-Lacroix P, Martella F, Ventura L, Bologna M, Flamini S, Moreschini O, Lidereau R, Ricevuto E, Muraca M, Teti A, Rucci N. Increased expression of a set of genes enriched in oxygen binding function discloses a predisposition of breast cancer bone metastases to generate metastasis spread in multiple organs. J Bone Miner Res. 2012;27:2387–98.CrossRefPubMed
6.
Van den Eynden GG, Van Laere SJ, Van der Auwera I, Gilles L, Burn JL, Colpaert C, van Dam P, Van Marck EA, Dirix LY, Vermeulen PB. Differential expression of hypoxia and (lymph)angiogenesis-related genes at different metastatic sites in breast cancer. Clin Exp Metastasis. 2007;24:13–23.CrossRefPubMed
7.
Clarke R, Skaar TC, Bouker KB, Davis N, Lee YR, Welch JN, Leonessa F. Molecular and pharmacological aspects of antiestrogen resistance. J Steroid Biochem Mol Biol. 2001;76:71–84.CrossRefPubMed
8.
Jatoi I, Hilsenbeck SG, Clark GM, Osborne CK. Significance of Axillary Lymph Node Metastasis in Primary Breast Cancer. J Clin Oncol. 1999;17:2334.CrossRefPubMed
9.
Tseng SPLLWW. Micrometastatic Cancer Cells in Lymph Nodes, Bone Marrow, and Blood. CA Cancer J Clin. 2014;64:195–206.CrossRefPubMed
10.
Ursaru M, Jari I, Naum A, Scripcariu V, Negru D. Causes of death in patients with stage 0-II breast cancer. Rev Med Chir Soc Med Nat lasi. 2015;119:374–8.
11.
Singh SK, Clarke ID, Terasaki M, Bonn VE, Hawkins C, Squire J, Dirks PB. Identification of a cancer stem cell in human brain tumors. Cancer Res. 2003;63:5821–8.PubMed
12.
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. Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen. BMC Genomics. 2008;9:239.CrossRefPubMedPubMedCentral
13.
Prat A, Parker JS, Fan C, Cheang MCU, Miller LD, Bergh J, Chia SKL, Bernard PS, Nielsen TO, Ellis MJ, Carey LA, Perou CM. Concordance among gene expression-based predictors for ER-positive breast cancer treated with adjuvant tamoxifen. Ann Oncol. 2012;23:2866–73.CrossRefPubMedPubMedCentral
14.
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, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–6.CrossRef
15.
Blohmer JU, Rezai M, Kümmel S, Kühn T, Warm M, Friedrichs K, Benkow A, Valentine WJ, Eiermann W. Using the 21-gene assay to guide adjuvant chemotherapy decision-making in early-stage breast cancer: a cost-effectiveness evaluation in the German setting. J Med Econ. 2013;16:30–40.CrossRefPubMed
16.
Habel LA, Shak S, Jacobs MK, Capra A, Alexander C, Pho M, Baker J, Walker M, Watson D, Hackett J, Blick NT, Greenberg D, Fehrenbacher L, Langholz B, Quesenberry CP. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res. 2006;8:R25.CrossRefPubMedPubMedCentral
17.
Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351:2817–26.CrossRefPubMed
18.
Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F, Singer CF, Dietze O, Greil R, Jelen A, Sevelda P, Freibauer C, Müller V, Jänicke F, Schmidt M, Kölbl H, Rody A, Kaufmann M, Schroth W, Brauch H, Schwab M, Fritz P, Weber KE, Feder IS, Hennig G, Kronenwett R, Gehrmann M, Gnant M. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin Cancer Res. 2011;17:6012–20.CrossRefPubMed
19.
Barrett T, Wilhite S, Ledoux P, Evangelista C, Kim I, Tomashevsky M, Marshall K, Phillip P, Holko M, Yefanov A, Lee H, Zhang N, Roberston C, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013;41:D991–5.CrossRefPubMed
20.
Symmans WF, Hatzis C, Sotiriou C, Andre F, Peintinger F, Regitnig P, Daxenbichler G, Desmedt C, Domont J, Marth C, Delaloge S, Bauernhofer T, Valero V, Booser DJ, Hortobagyi GN, Pusztai L. Genomic index of sensitivity to endocrine therapy for breast cancer. J Clin Oncol. 2010;28:4111–4119.CrossRefPubMedPubMedCentral
21.
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;25:1239–46.CrossRefPubMed
22.
23.
McCall MN, Bolstad BM, Irizarry RA. Frozen robust multiarray analysis (fRMA). Biostatistics. 2012;11:242–253.CrossRef
24.
Gyorffy B, Molnar B, Lage H, Szallasi Z, Eklund AC. Evaluation of microarray preprocessing algorithms based on concordance with RT-PCR in clinical samples. PLoS One. 2009;4:e5645.CrossRefPubMedPubMedCentral
25.
26.
Aravind Subramanian PT. Gene Set Enrichment Analysis. 2014.
27.
Hallett RM, Dvorkin-Gheva A, Bane A, Hassell JA. A Gene Signature for Predicting Outcome in Patients with Basal-like Breast Cancer. Sci Rep. 2012;2:227.CrossRefPubMedPubMedCentral
28.
29.
Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A. 2002;99:6567–72.CrossRefPubMedPubMedCentral
30.
Győrffy B, Benke Z, Lánczky A, Balázs B, Szállási Z, Timár J, Schäfer R. RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat. 2012;132:1025–34.CrossRefPubMed
31.
Elloumi F, Hu Z, Li Y, Parker JS, Gulley ML, Amos KD, Troester MA. Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples. BMC Med Genomics. 2011;4:54.CrossRefPubMedPubMedCentral
32.
Naoi Y, Kishi K, Tsunashima R, Shimazu K, Shimomura A, Maruyama N, Shimoda M, Kagara N, Baba Y, Kim SJ, Noguchi S. Comparison of efficacy of 95-gene and 21-gene classifier (Oncotype DX) for prediction of recurrence in ER-positive and node-negative breast cancer patients. Breast Cancer Res Treat. 2013.
33.
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2010;144:646–74.CrossRef
34.
Holt S, Bertelli G, Humphreys I, Valentine W, Durrani S, Pudney D, Rolles M, Moe M, Khawaja S, Sharaiha Y, Brinkworth E, Whelan S, Jones S, Bennett H, Phillips CJ. A decision impact, decision conflict and economic assessment of routine Oncotype DX testing of 146 women with node-negative or pNImi, ER-positive breast cancer in the UK. Br J Cancer. 2013;108:2250–8.CrossRefPubMedPubMedCentral
35.
Joh JE, Esposito NN, Kiluk JV, Laronga C, Lee MC, Loftus L, Soliman H, Boughey JC, Reynolds C, Lawton TJ, Acs PI, Gordan L, Acs G. The effect of Oncotype DX recurrence score on treatment recommendations for patients with estrogen receptor-positive early stage breast cancer and correlation with estimation of recurrence risk by breast cancer specialists. Oncologist. 2011;16:1520–6.CrossRefPubMedPubMedCentral
36.
Albain KS, Barlow WE, Shak S, Hortobagyi GN, Livingston RB, Yeh I-T, Ravdin P, Bugarini R, Baehner FL, Davidson NE, Sledge GW, Winer EP, Hudis C, Ingle JN, Perez EA, Pritchard KI, Shepherd L, Gralow JR, Yoshizawa C, Allred DC, Osborne CK, Hayes DF. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol. 2010;11:55–65.CrossRefPubMed
37.
Saghatchian M, Mook S, Pruneri G, Viale G, Glas AM, Guerin S, et al. Additional prognostic value of the 70-gene signature (MammaPrint(®)) among breast cancer patients with 4-9 positive lymph nodes. Breast. 2013.
38.
Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature. 2000;406:747–52.CrossRefPubMed
39.
Parker JS, Mullins M, Cheang MCU, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27:1160–7.CrossRefPubMedPubMedCentral
40.
41.
Huynh KT, Hoon DSB. Epigenetics of regional lymph node metastasis in solid tumors. Clin Exp Metastasis. 2012;29:747–56.CrossRefPubMed
42.
Lo P-K, Sukumar S. Epigenoics and breast cancer. Pharmacogenomics. 2009;9:1879–902.CrossRef
43.
Kamalakaran S, Varadan V, Giercksky Russnes HE, Levy D, Kendall J, Janevski A, Riggs M, Banerjee N, Synnestvedt M, Schlichting E, Kåresen R, Shama Prasada K, Rotti H, Rao R, Rao L, Eric Tang M-H, Satyamoorthy K, Lucito R, Wigler M, Dimitrova N, Naume B, Borresen-Dale A-L, Hicks JB. DNA methylation patterns in luminal breast cancers differ from non-luminal subtypes and can identify relapse risk independent of other clinical variables. Mol Oncol. 2011;5:77–92.CrossRefPubMed
44.
Hsu DS, Kim MK, Balakumaran BS, Acharya CR, Anders CK, Clay T, Lyerly HK, Drake CG, Morse MA, Febbo PG. Immune signatures predict prognosis in localized cancer. Cancer Invest. 2010;28:765–73.CrossRefPubMedPubMedCentral
45.
Oh E, Choi Y-L, Park T, Lee S, Nam SJ, Shin YK. A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity. Breast Cancer Res Treat. 2012;132:499–509.CrossRefPubMed
46.
Mirisola V, Zuccarino A, Bachmeier BE, Sormani MP, Falter J, Nerlich A, Pfeffer U. CXCL12/SDF1 expression by breast cancers is an independent prognostic marker of disease-free and overall survival. Eur J Cancer. 2009;45:2579–87.CrossRefPubMed
47.
Wendt MK, Cooper AN, Dwinell MB. Epigenetic silencing of CXCL12 increases the metastatic potential of mammary carcinoma cells. Oncogene. 2008;27:1461–71.CrossRefPubMed
48.
Miller CP, Thorpe JD, Kortum AN, Coy CM, Cheng W-Y, Ou Yang T-H, Anastassiou D, Beatty JD, Urban ND, Blau CA. JAK2 Expression Is Associated with Tumor-Infiltrating Lymphocytes and Improved Breast Cancer Outcomes: Implications for Evaluating JAK2 Inhibitors. Cancer Immunol Res. 2014;2:301–6.CrossRefPubMedPubMedCentral
49.
Englert NA, Spink BC, Spink DC. Persistent and non-persistent changes in gene expression result from long-term estrogen exposure of MCF-7 breast cancer cells. J Steroid Biochem Mol Biol. 2011;123:140–50.CrossRefPubMed
50.
Koboldt DC, Fulton RS, McLellan MD, Schmidt H, Kalicki-Veizer J, McMichael JF, et al. Comprehensive molecular portraits of human breast tumours. Nature. 2012;1–10.
51.
Feng Q, Zhang Z, Shea MJ, Creighton CJ, Coarfa C, Hilsenbeck SG, et al. An epigenomic approach to therapy for tamoxifen-resistant breast cancer. Cell Res. 2014.
52.
Klarmann GJ, Decker A, Farrar WL. Epigenetic gene silencing in the Wnt pathway in breast cancer. Epigenetics. 2008;3:59–63.CrossRefPubMed
53.
Fan J, Yin W-J, Lu J-S, Wang L, Wu J, Wu F-Y, Di G-H, Shen Z-Z, Shao Z-M. ER alpha negative breast cancer cells restore response to endocrine therapy by combination treatment with both HDAC inhibitor and DNMT inhibitor. J Cancer Res Clin Oncol. 2008;134:883–90.CrossRefPubMed
54.
Wagner JM, Hackanson B, Lubbert M, Jung M. Histone deacetylase (HDAC) inhibitors in recent clinical trials for cancer therapy. Clin Epigenetics. 2010;1:117–136.CrossRefPubMedPubMedCentral
55.
Hennessy BT, Stemke-hale K, Gilcrease MZ, Krishnamurthy S, Lee J, Fridlyand J, Agarwal R, Joy C, Liu W, Stivers D, Baggerly K, Lluch A, Monteagudo C, He X, Weigman V, Palazzo J, Hortobagyi GN, Nolden LK, Wang NJ, Valero V, Gray JW, Perou CM, Mills GB. Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics. Cancer Res. 2009;69:4116–24.CrossRefPubMedPubMedCentral
Metadata
Title
The effects of lymph node status on predicting outcome in ER+ /HER2- tamoxifen treated breast cancer patients using gene signatures
Authors
Jessica G. Cockburn
Robin M. Hallett
Amy E. Gillgrass
Kay N. Dias
T. Whelan
M. N. Levine
John A. Hassell
Anita Bane
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2016
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-016-2501-0

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