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Breast Cancer Research
Published in: Breast Cancer Research 1/2015

Open Access 01-12-2015 | Research article

Long non-coding RNA expression profiles predict metastasis in lymph node-negative breast cancer independently of traditional prognostic markers

Authors: Kristina P Sørensen, Mads Thomassen, Qihua Tan, Martin Bak, Søren Cold, Mark Burton, Martin J Larsen, Torben A Kruse

Published in: Breast Cancer Research | Issue 1/2015

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Abstract

Introduction

Patients with clinically and pathologically similar breast tumors often have very different outcomes and treatment responses. Current prognostic markers allocate the majority of breast cancer patients to the high-risk group, yielding high sensitivities in expense of specificities below 20%, leading to considerable overtreatment, especially in lymph node-negative patients. Seventy percent would be cured by surgery and radiotherapy alone in this group. Thus, precise and early indicators of metastasis are highly desirable to reduce overtreatment. Previous prognostic RNA-profiling studies have only focused on the protein-coding part of the genome, however the human genome contains thousands of long non-coding RNAs (lncRNAs) and this unexplored field possesses large potential for identification of novel prognostic markers.

Methods

We evaluated lncRNA microarray data from 164 primary breast tumors from adjuvant naïve patients with a mean follow-up of 18 years. Eighty two patients who developed detectable distant metastasis were compared to 82 patients where no metastases were diagnosed. For validation, we determined the prognostic value of the lncRNA profiles by comparing the ability of the profiles to predict metastasis in two additional, previously-published, cohorts.

Results

We showed that lncRNA profiles could distinguish metastatic patients from non-metastatic patients with sensitivities above 90% and specificities of 64-65%. Furthermore; classifications were independent of traditional prognostic markers and time to metastasis.

Conclusions

To our knowledge, this is the first study investigating the prognostic potential of lncRNA profiles. Our study suggest that lncRNA profiles provide additional prognostic information and may contribute to the identification of early breast cancer patients eligible for adjuvant therapy, as well as early breast cancer patients that could avoid unnecessary systemic adjuvant therapy. This study emphasizes the potential role of lncRNAs in breast cancer prognosis.
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Literature
1.
Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med. 2009;360:790–800.CrossRefPubMed
2.
Veer LJ V ’t, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–6.
3.
Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365:671–9.CrossRefPubMed
4.
Thomassen M, Tan Q, Eiriksdottir F, Bak M, Cold S, Kruse TA. Prediction of metastasis from low-malignant breast cancer by gene expression profiling. Int J Cancer. 2007;120:1070–5.CrossRefPubMed
5.
Perou CM, Sorlie T, Eisen MB, van de RM, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–52.
6.
Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98:10869–74.CrossRefPubMedPubMedCentral
7.
Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet. 2011;378:1812–23.CrossRefPubMed
8.
Consortium TEP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.CrossRef
9.
Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, Zhu X, et al. Global identification of human transcribed sequences with genome tiling arrays. Science. 2004;306:2242–6.CrossRefPubMed
10.
Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 2012;22:1775–89.CrossRefPubMedPubMedCentral
11.
Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 2011;25:1915–27.CrossRefPubMedPubMedCentral
12.
13.
Guttman M, Amit I, Garber M, French C, Lin MF, Feldser D, et al. Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals. Nature. 2009;458:223–7.CrossRefPubMedPubMedCentral
14.
Mercer TR, Dinger ME, Mattick JS. Long non-coding RNAs: insights into functions. Nat Rev Genet. 2009;10:155–9.CrossRefPubMed
15.
16.
Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 2010;464:1071–6.CrossRefPubMedPubMedCentral
17.
Kogo R, Shimamura T, Mimori K, Kawahara K, Imoto S, Sudo T, et al. Long noncoding RNA HOTAIR regulates polycomb-dependent chromatin modification and is associated with poor prognosis in colorectal cancers. Cancer Res. 2011;71:6320–6.CrossRefPubMed
18.
Sørensen KP, Thomassen M, Tan Q, Bak M, Cold S, Burton M, et al. Long non-coding RNA HOTAIR is an independent prognostic marker of metastasis in estrogen receptor-positive primary breast cancer. Breast Cancer Res Treat. 2013;142:529–36.CrossRefPubMed
19.
Prensner JR, Iyer MK, Balbin OA, Dhanasekaran SM, Cao Q, Brenner JC, et al. Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression. Nat Biotechnol. 2011;29:742–9.CrossRefPubMedPubMedCentral
20.
Ji P, Diederichs S, Wang W, Boing S, Metzger R, Schneider PM, et al. MALAT-1, a novel noncoding RNA, and thymosin beta4 predict metastasis and survival in early-stage non-small cell lung cancer. Oncogene. 2003;22:8031–41.CrossRefPubMed
21.
22.
Tørring PM, Larsen MJ, Kjeldsen AD, Ousager LB, Tan Q, Brusgaard K. Long non-coding RNA expression profiles in hereditary haemorrhagic telangiectasia. PLoS One. 2014;9, e90272.CrossRefPubMedPubMedCentral
23.
Cruz JA, Wishart DS. Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Inform. 2007;2:59–77.PubMedPubMedCentral
24.
Somorjai RL, Dolenko B, Baumgartner R. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics. 2003;19:1484–91.CrossRefPubMed
25.
26.
Burton M, Thomassen M, Tan Q, Kruse TA. Gene expression profiles for predicting metastasis in breast cancer: a cross-study comparison of classification methods. ScientificWorldJournal. 2012;2012:380495.CrossRefPubMedPubMedCentral
27.
Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27:1160–7.CrossRefPubMedPubMedCentral
28.
Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, 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;102:13550–5.CrossRefPubMedPubMedCentral
29.
Pawitan Y, Bjohle J, Amler L, Borg AL, Egyhazi S, Hall P, 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:R953–64.CrossRefPubMedPubMedCentral
30.
31.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.CrossRefPubMedPubMedCentral
32.
33.
McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. REporting recommendations for tumor MARKer prognostic studies (REMARK). Breast Cancer Res Treat. 2006;100:229–35.CrossRefPubMed
34.
Weigelt B, Glas AM, Wessels LF, Witteveen AT, Peterse JL, van’t Veer LJ. Gene expression profiles of primary breast tumors maintained in distant metastases. Proc Natl Acad Sci USA. 2003;100:15901–5.CrossRefPubMedPubMedCentral
35.
Cardoso F, van't Veer L, Rutgers E, Loi S, Mook S, Piccart-Gebhart MJ. Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol. 2008;26:729–35.CrossRefPubMed
36.
Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, et al. 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. 2007;13:3207–14.CrossRefPubMed
37.
Desmedt C, Haibe-Kains B, Wirapati P, Buyse M, Larsimont D, Bontempi G, et al. Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res. 2008;14:5158–65.CrossRefPubMed
38.
Wirapati P, Sotiriou C, Kunkel S, Farmer P, Pradervand S, Haibe-Kains B, et al. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res. 2008;10:R65.CrossRefPubMedPubMedCentral
39.
Roepman P, Horlings HM, Krijgsman O, Kok M, Bueno-de-Mesquita JM, Bender R, et al. Microarray-Based Determination of Estrogen Receptor, Progesterone Receptor, and HER2 Receptor Status in Breast Cancer. Clin Cancer Res. 2009;15:7003–11.CrossRefPubMed
40.
Esserman LJ, Moore DH, Tsing PJ, Chu PW, Yau C, Ozanne E, et al. Biologic markers determine both the risk and the timing of recurrence in breast cancer. Breast Cancer ResTreat. 2011;129:607–16.CrossRef
41.
Bentzon N, Düring M, Rasmussen BB, Mouridsen H, Kroman N. Prognostic effect of estrogen receptor status across age in primary breast cancer. Int J Cancer. 2008;122:1089–94.CrossRefPubMed
42.
Jumppanen M, Gruvberger-Saal S, Kauraniemi P, Tanner M, Bendahl P-O, Lundin M, et al. Basal-like phenotype is not associated with patient survival in estrogen-receptor-negative breast cancers. Breast Cancer Res. 2007;9:R16.CrossRefPubMedPubMedCentral
43.
Teschendorff AE, Caldas C. A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer. Breast Cancer Res. 2008;10:R73.CrossRefPubMedPubMedCentral
44.
Oskarsson T. Extracellular matrix components in breast cancer progression and metastasis. Breast Edinb Scotl. 2013;22:S66–72.CrossRef
45.
Chakravarty D, Sboner A, Nair SS, Giannopoulou E, Li R, Hennig S, et al. The oestrogen receptor alpha-regulated lncRNA NEAT1 is a critical modulator of prostate cancer. Nat Commun. 2014;5:5383.CrossRefPubMedPubMedCentral
46.
Choudhry H, Albukhari A, Morotti M, Hider S, Moralli D, Smythies J, et al. Tumor hypoxia induces nuclear paraspeckle formation through HIF-2α dependent transcriptional activation of NEAT1 leading to cancer cell survival. Oncogene. 2014. doi:10.1038/onc.2014.378.
47.
Su X, Malouf GG, Chen Y, Zhang J, Yao H, Valero V, et al. Comprehensive analysis of long non-coding RNAs in human breast cancer clinical subtypes. Oncotarget. 2014;5:9864–76.CrossRefPubMedPubMedCentral
48.
Chau YM, Pando S, Taylor HS. HOXA11 silencing and endogenous HOXA11 antisense ribonucleic acid in the uterine endometrium. J Clin Endocrinol Metab. 2002;87:2674–80.CrossRefPubMed
49.
Cao W-J, Wu H-L, He B-S, Zhang Y-S, Zhang Z-Y. Analysis of long non-coding RNA expression profiles in gastric cancer. World J Gastroenterol. 2013;19:3658–64.CrossRefPubMedPubMedCentral
50.
Du Z, Fei T, Verhaak RGW, Su Z, Zhang Y, Brown M, et al. Integrative genomic analyses reveal clinically relevant long noncoding RNAs in human cancer. Nat Struct Mol Biol. 2013;20:908–13.CrossRefPubMedPubMedCentral
51.
Michelhaugh SK, Lipovich L, Blythe J, Jia H, Kapatos G, Bannon MJ. Mining Affymetrix microarray data for long non-coding RNAs: altered expression in the nucleus accumbens of heroin abusers. J Neurochem. 2011;116:459–66.CrossRefPubMed
52.
Kuo WP, Jenssen T-K, Butte AJ, Ohno-Machado L, Kohane IS. Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics. 2002;18:405–12.CrossRefPubMed
53.
Burton M, Thomassen M, Tan Q, Kruse TA. Prediction of breast cancer metastasis by gene expression profiles: a comparison of metagenes and single genes. Cancer Inf. 2012;11:193–217.CrossRef
54.
Stec J, Wang J, Coombes K, Ayers M, Hoersch S, Gold DL, et al. Comparison of the Predictive Accuracy of DNA Array-Based Multigene Classifiers across cDNA Arrays and Affymetrix GeneChips. J Mol Diagn. 2005;7:357–67.CrossRefPubMedPubMedCentral
55.
Tahira AC, Kubrusly MS, Faria MF, Dazzani B, Fonseca RS, Maracaja-Coutinho V, et al. Long noncoding intronic RNAs are differentially expressed in primary and metastatic pancreatic cancer. Mol Cancer. 2011;10:141.CrossRefPubMedPubMedCentral
56.
Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24:2206–23.CrossRefPubMedPubMedCentral
57.
Schmidt M, Victor A, Bratzel D, Boehm D, Cotarelo C, Lebrecht A, et al. Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer—comparison between Adjuvant! St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3) trial. Ann Oncol. 2009;20:258–64.CrossRefPubMed
58.
Buyse M, Loi S, van't Veer VL, Viale G, Delorenzi M, Glas AM, et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst. 2006;98:1183–92.CrossRefPubMed
Metadata
Title
Long non-coding RNA expression profiles predict metastasis in lymph node-negative breast cancer independently of traditional prognostic markers
Authors
Kristina P Sørensen
Mads Thomassen
Qihua Tan
Martin Bak
Søren Cold
Mark Burton
Martin J Larsen
Torben A Kruse
Publication date
01-12-2015
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2015
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-015-0557-4

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