Advertisement

Breast Cancer Research and Treatment

, Volume 172, Issue 3, pp 689–702 | Cite as

Young age at diagnosis is associated with worse prognosis in the Luminal A breast cancer subtype: a retrospective institutional cohort study

  • Zhiyang Liu
  • Zeyad Sahli
  • Yongchun Wang
  • Antonio C. Wolff
  • Leslie M. Cope
  • Christopher B. UmbrichtEmail author
Epidemiology

Abstract

Purpose

Although age is a recognized independent prognostic risk factor, its relative importance among molecular subtypes of Breast cancer (BCA) is not well documented. The aim of this study was to evaluate the prognostic role of age at diagnosis among different immunohistochemical subtypes of BCA.

Methods

We conducted a retrospective study of women with invasive BCA undergoing surgery at the Johns Hopkins Hospital, excluding patients presenting with stage IV breast cancer. Patients were stratified into three age groups: ≤ 40, 41–60, and > 60 years, and multivariable analysis was performed using Cox regression. We also identified differentially expressed genes (DEG) between age groups among BCA subtypes in the public TCGA dataset. Finally, we identified key driver genes within the DEGs using a weighted gene co-expression network analysis.

Results

Luminal A breast cancer patients had significantly lower 5 year disease-free survival (DFS) and distant metastasis-free survival (DMFS) in the ≤ 40 year age group compared to the 41–60 year age group, while the other molecular subtypes showed no significant association of DFS or DMFS with age. Age was a stronger outcome predictor than tumor grade or proliferative index in Luminal A BCA patients, but not other subtypes. BCA TCGA gene expression data were divided into two groups (≤ 40 years, > 40 years). We identified 374 DEGs in the Luminal A BCA subset, which were enriched in seven pathways and two modules of co-expressed genes. No age group-specific DEGs were identified in non-Luminal A subtypes.

Conclusions

Age at diagnosis may be an important prognostic factor in Luminal A BCA.

Keywords

Breast cancer Intrinsic subtype Age Prognosis Gene expression 

Abbreviations

BCA

Breast cancer

DEG

Differentially expressed genes

DFS

Disease free survival

DMFS

Distant metastasis-free survival

Her2

Human epidermal growth factor receptor 2

ER

Estrogen receptor

PR

Progesterone receptor

GSEA

Gene set enrichment analysis

IHC

Immunohistochemistry

VIMP

Variable importance score

WGCNA

Weighted gene co-expression network analysis

HR

Hazard ratio

ODXRS

Oncotype DX recurrence score

GEC

Gene expression classification

EMT

Epithelial to mesenchymal transition

TGF

Transforming growth factor

GNA13

G protein subunit alpha 13

Notes

Acknowledgements

We would like to thank all who participated in the annotation of the JH Integrated Breast Cancer Database that made this study possible.

Author contributions

All authors participated in the design and planning of the project and in writing the manuscript.

Funding

This work was supported, in part, by a DOD Grant (W81XWH-14-1-0080) to CBU and by funding from the Qingdao Municipal Hospital, China to ZL. Funding had no role in the design of the study nor in collection, analysis, and interpretation of data or in writing the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For the institutional cohorts, de-identified data were extracted from the Johns Hopkins Institutional Review Board-approved Integrated Breast Cancer Database (NA00022703). This article does not contain any studies with human participants performed by any of the authors.

Informed consent

For this type of study formal consent is not required.

Supplementary material

10549_2018_4950_MOESM1_ESM.eps (95.6 mb)
Supplementary material SFigure 1. Kaplan-Meier curve for 5 year DMFS for Luminal A (a), Luminal B (her2-negative) (b), Luminal B (her2-positive) (c), Her2 (d), Triple negative (e) breast cancer in young patients (≤40y: blue curve), middle age patients (41-60y: red curve) and old patients (>60y: green curve). P value was calculated by Log-rank tests. Number of patients at risk and number of patients censored over time are listed under each plot. (EPS 97863 KB)
10549_2018_4950_MOESM2_ESM.eps (62 mb)
Supplementary material SFigure 2. Gene dendrogram obtained by clustering the dissimilarity based on consensus Topological Overlap. The corresponding modules are indicated in the color row. Horizontal axis: modules in different colors. Vertical axis: heights of gene clustering trees. (EPS 63484 KB)
10549_2018_4950_MOESM3_ESM.eps (18 mb)
Supplementary material SFigure 3. Co-expression networks of genes. There were 103 nodes and 533 edges in the turquoise co-expression network (Network A), where nodes referred to genes and edges between nodes indicated interaction of genes in the network. Yellow nodes are hub genes (MED13 BPTF HELZ GNA13 CLTC INTS2). There were 57 nodes and 366 edges in the blue co-expression network (Network B). Yellow nodes are hub genes (MST1 AHSA2 NSUN5P2 APBB3 MST1P2 AGAP4 LOC338799 NCRNA00105 NSUN5P1). (EPS 18447 KB)

References

  1. 1.
    Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68(1):7–30.  https://doi.org/10.3322/caac.21442 CrossRefGoogle Scholar
  2. 2.
    Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein Lonning P, Borresen-Dale AL (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98(19):10869–10874CrossRefGoogle Scholar
  3. 3.
    Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, Senn HJ, Panel m (2013) 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 24(9):2206–2223.  https://doi.org/10.1093/annonc/mdt303 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K, Karaca G, Troester MA, Tse CK, Edmiston S, Deming SL, Geradts J, Cheang MC, Nielsen TO, Moorman PG, Earp HS, Millikan RC (2006) Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295(21):2492–2502.  https://doi.org/10.1001/jama.295.21.2492 CrossRefPubMedGoogle Scholar
  5. 5.
    Engstrom MJ, Opdahl S, Hagen AI, Romundstad PR, Akslen LA, Haugen OA, Vatten LJ, Bofin AM (2013) Molecular subtypes, histopathological grade and survival in a historic cohort of breast cancer patients. Breast Cancer Res Treat 140(3):463–473.  https://doi.org/10.1007/s10549-013-2647-2 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Kennecke H, Yerushalmi R, Woods R, Cheang MC, Voduc D, Speers CH, Nielsen TO, Gelmon K (2010) Metastatic behavior of breast cancer subtypes. J Clin Oncol 28(20):3271–3277.  https://doi.org/10.1200/JCO.2009.25.9820 CrossRefPubMedGoogle Scholar
  7. 7.
    Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lonning PE, Brown PO, Borresen-Dale AL, Botstein D (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100(14):8418–8423CrossRefGoogle Scholar
  8. 8.
    Voduc KD, Cheang MC, Tyldesley S, Gelmon K, Nielsen TO, Kennecke H (2010) Breast cancer subtypes and the risk of local and regional relapse. J Clin Oncol 28(10):1684–1691CrossRefGoogle Scholar
  9. 9.
    Alieldin NH, Abo-Elazm OM, Bilal D, Salem SE, Gouda E, Elmongy M, Ibrahim AS (2014) Age at diagnosis in women with non-metastatic breast cancer: is it related to prognosis? J Egypt Natl Cancer Inst 26(1):23–30.  https://doi.org/10.1016/j.jnci.2013.08.005 CrossRefGoogle Scholar
  10. 10.
    Azim HA Jr, Michiels S, Bedard PL, Singhal SK, Criscitiello C, Ignatiadis M, Haibe-Kains B, Piccart MJ, Sotiriou C, Loi S (2012) Elucidating prognosis and biology of breast cancer arising in young women using gene expression profiling. Clin Cancer Res 18(5):1341–1351.  https://doi.org/10.1158/1078-0432.CCR-11-2599 CrossRefPubMedGoogle Scholar
  11. 11.
    Ferguson NL, Bell J, Heidel R, Lee S, Vanmeter S, Duncan L, Munsey B, Panella T, Orucevic A (2013) Prognostic value of breast cancer subtypes, Ki-67 proliferation index, age, and pathologic tumor characteristics on breast cancer survival in Caucasian women. Breast J 19(1):22–30.  https://doi.org/10.1111/tbj.12059 CrossRefPubMedGoogle Scholar
  12. 12.
    Liedtke C, Rody A, Gluz O, Baumann K, Beyer D, Kohls EB, Lausen K, Hanker L, Holtrich U, Becker S, Karn T (2015) The prognostic impact of age in different molecular subtypes of breast cancer. Breast Cancer Res Treat 152(3):667–673.  https://doi.org/10.1007/s10549-015-3491-3 CrossRefPubMedGoogle Scholar
  13. 13.
    van de Water W, Markopoulos C, van de Velde CJ, Seynaeve C, Hasenburg A, Rea D, Putter H, Nortier JW, de Craen AJ, Hille ET, Bastiaannet E, Hadji P, Westendorp RG, Liefers GJ, Jones SE (2012) Association between age at diagnosis and disease-specific mortality among postmenopausal women with hormone receptor-positive breast cancer. JAMA 307(6):590–597.  https://doi.org/10.1001/jama.2012.84 CrossRefPubMedGoogle Scholar
  14. 14.
    Wray CJ, Phatak UR, Robinson EK, Wiatek RL, Rieber AG, Gonzalez A, Ko TC, Kao LS (2013) The effect of age on race-related breast cancer survival disparities. Ann Surg Oncol 20(8):2541–2547.  https://doi.org/10.1245/s10434-013-2913-x CrossRefPubMedGoogle Scholar
  15. 15.
    Network TCGAR (2012) Comprehensive molecular portraits of human breast tumours. Nature 490(7418):61–70.  https://doi.org/10.1038/nature11412 CrossRefGoogle Scholar
  16. 16.
    Hammond ME, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, Fitzgibbons PL, Francis G, Goldstein NS, Hayes M, Hicks DG, Lester S, Love R, Mangu PB, McShane L, Miller K, Osborne CK, Paik S, Perlmutter J, Rhodes A, Sasano H, Schwartz JN, Sweep FC, Taube S, Torlakovic EE, Valenstein P, Viale G, Visscher D, Wheeler T, Williams RB, Wittliff JL, Wolff AC (2010) American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 28(16):2784–2795CrossRefGoogle Scholar
  17. 17.
    Wolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS, Bilous M, Ellis IO, Fitzgibbons P, Hanna W, Jenkins RB, Press MF, Spears PA, Vance GH, Viale G, McShane LM, Dowsett M (2018) Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update. Arch Pathol Lab Med.  https://doi.org/10.5858/arpa.2018-0902-SA CrossRefPubMedGoogle Scholar
  18. 18.
    Ignatiadis M, Buyse M, Sotiriou C (2015) St Gallen International Expert Consensus on the primary therapy of early breast cancer: an invaluable tool for physicians and scientists. Ann Oncol 26(8):1519–1520.  https://doi.org/10.1093/annonc/mdv259 CrossRefPubMedGoogle Scholar
  19. 19.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32.  https://doi.org/10.1023/A:101093340 CrossRefGoogle Scholar
  20. 20.
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80CrossRefGoogle Scholar
  21. 21.
    He XM, Zou DH (2017) The association of young age with local recurrence in women with early-stage breast cancer after breast-conserving therapy: a meta-analysis. Sci Rep 7(1):11058.  https://doi.org/10.1038/s41598-017-10729-9 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Lian W, Fu F, Lin Y, Lu M, Chen B, Yang P, Zeng B, Huang M, Wang C (2017) The impact of young age for prognosis by subtype in women with early breast cancer. Sci Rep 7(1):11625.  https://doi.org/10.1038/s41598-017-10414-x CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Partridge AH, Hughes ME, Warner ET, Ottesen RA, Wong YN, Edge SB, Theriault RL, Blayney DW, Niland JC, Winer EP, Weeks JC, Tamimi RM (2016) Subtype-dependent relationship between young age at diagnosis and breast cancer survival. J Clin Oncol 34(27):3308–3314.  https://doi.org/10.1200/JCO.2015.65.8013 CrossRefPubMedGoogle Scholar
  24. 24.
    Tammemagi CM, Nerenz D, Neslund-Dudas C, Feldkamp C, Nathanson D (2005) Comorbidity and survival disparities among black and white patients with breast cancer. JAMA 294(14):1765–1772.  https://doi.org/10.1001/jama.294.14.1765 CrossRefPubMedGoogle Scholar
  25. 25.
    Carter CL, Allen C, Henson DE (1989) Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 63(1):181–187CrossRefGoogle Scholar
  26. 26.
    Ehinger A, Malmstrom P, Bendahl PO, Elston CW, Falck AK, Forsare C, Grabau D, Ryden L, Stal O, Ferno M, South, South-East Swedish Breast Cancer G (2017) Histological grade provides significant prognostic information in addition to breast cancer subtypes defined according to St Gallen 2013. Acta Oncol 56(1):68–74.  https://doi.org/10.1080/0284186X.2016.1237778 CrossRefPubMedGoogle Scholar
  27. 27.
    Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P (2015) The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1(6):417–425.  https://doi.org/10.1016/j.cels.2015.12.004 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559.  https://doi.org/10.1186/1471-2105-9-559 CrossRefGoogle Scholar
  29. 29.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504.  https://doi.org/10.1101/gr.1239303 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Fowble BL, Schultz DJ, Overmoyer B, Solin LJ, Fox K, Jardines L, Orel S, Glick JH (1994) The influence of young age on outcome in early stage breast cancer. Int J Radiat Oncol Biol Phys 30(1):23–33CrossRefGoogle Scholar
  31. 31.
    Orucevic A, Bell JL, McNabb AP, Heidel RE (2017) Oncotype DX breast cancer recurrence score can be predicted with a novel nomogram using clinicopathologic data. Breast Cancer Res Treat 163(1):51–61.  https://doi.org/10.1007/s10549-017-4170-3 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    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 (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826CrossRefGoogle Scholar
  33. 33.
    van de Vijver 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, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347(25):1999–2009CrossRefGoogle Scholar
  34. 34.
    Morrison DH, Rahardja D, King E, Peng Y, Sarode VR (2012) Tumour biomarker expression relative to age and molecular subtypes of invasive breast cancer. Br J Cancer 107(2):382–387.  https://doi.org/10.1038/bjc.2012.219 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Perez-Fidalgo JA, Rosello S, Garcia-Garre E, Jorda E, Martin-Martorell P, Bermejo B, Chirivella I, Guzman C, Lluch A (2010) Incidence of chemotherapy-induced amenorrhea in hormone-sensitive breast cancer patients: the impact of addition of taxanes to anthracycline-based regimens. Breast Cancer Res Treat 120(1):245–251.  https://doi.org/10.1007/s10549-009-0426-x CrossRefPubMedGoogle Scholar
  36. 36.
    Saha P, Regan MM, Pagani O, Francis PA, Walley BA, Ribi K, Bernhard J, Luo W, Gomez HL, Burstein HJ, Parmar V, Torres R, Stewart J, Bellet M, Perello A, Dane F, Moreira A, Vorobiof D, Nottage M, Price KN, Coates AS, Goldhirsch A, Gelber RD, Colleoni M, Fleming GF, Soft, Investigators T, International Breast Cancer Study G (2017) Treatment efficacy, adherence, and quality of life among women younger than 35 years in the International Breast Cancer Study Group TEXT and SOFT Adjuvant Endocrine Therapy Trials. J Clin Oncol 35(27):3113–3122.  https://doi.org/10.1200/JCO.2016.72.0946 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100(1):57–70CrossRefGoogle Scholar
  38. 38.
    Wang Y, Zhou BP (2013) Epithelial-mesenchymal transition—a hallmark of breast cancer metastasis. Cancer Hallmarks 1(1):38–49.  https://doi.org/10.1166/ch.2013.1004 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Radisky ES, Radisky DC (2010) Matrix metalloproteinase-induced epithelial-mesenchymal transition in breast cancer. J Mamm Gland Biol Neoplasia 15(2):201–212.  https://doi.org/10.1007/s10911-010-9177-x CrossRefGoogle Scholar
  40. 40.
    Cichon MA, Nelson CM, Radisky DC (2015) Regulation of epithelial–mesenchymal transition in breast cancer cells by cell contact and adhesion. Cancer Inform 14(Suppl 3):1–13.  https://doi.org/10.4137/CIN.S18965 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Barcellos-Hoff MH, Akhurst RJ (2009) Transforming growth factor-beta in breast cancer: too much, too late. Breast Cancer Res 11(1):202.  https://doi.org/10.1186/bcr2224 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Xu J, Lamouille S, Derynck R (2009) TGF-beta-induced epithelial to mesenchymal transition. Cell Res 19(2):156–172.  https://doi.org/10.1038/cr.2009.5 CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Broude EV, Gyorffy B, Chumanevich AA, Chen M, McDermott MS, Shtutman M, Catroppo JF, Roninson IB (2015) Expression of CDK8 and CDK8-interacting Genes as Potential Biomarkers in Breast Cancer. Curr Cancer Drug Targets 15(8):739–749CrossRefGoogle Scholar
  44. 44.
    Dai M, Lu JJ, Guo W, Yu W, Wang Q, Tang R, Tang Z, Xiao Y, Li Z, Sun W, Sun X, Qin Y, Huang W, Deng WG, Wu T (2015) BPTF promotes tumor growth and predicts poor prognosis in lung adenocarcinomas. Oncotarget 6(32):33878–33892.  https://doi.org/10.18632/oncotarget.5302 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Rasheed SA, Teo CR, Beillard EJ, Voorhoeve PM, Zhou W, Ghosh S, Casey PJ (2015) MicroRNA-31 controls G protein alpha-13 (GNA13) expression and cell invasion in breast cancer cells. Mol Cancer 14:67.  https://doi.org/10.1186/s12943-015-0337-x CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Rasheed SAK, Leong HS, Lakshmanan M, Raju A, Dadlani D, Chong FT, Shannon NB, Rajarethinam R, Skanthakumar T, Tan EY, Hwang JSG, Lim KH, Tan DS, Ceppi P, Wang M, Tergaonkar V, Casey PJ, Iyer NG (2018) GNA13 expression promotes drug resistance and tumor-initiating phenotypes in squamous cell cancers. Oncogene 37(10):1340–1353.  https://doi.org/10.1038/s41388-017-0038-6 CrossRefPubMedGoogle Scholar
  47. 47.
    Lin XY, Cai FF, Wang MH, Pan X, Wang F, Cai L, Cui RR, Chen S, Biskup E (2017) Mammalian sterile 20-like kinase 1 expression and its prognostic significance in patients with breast cancer. Oncol Lett 14(5):5457–5463.  https://doi.org/10.3892/ol.2017.6852 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of General SurgeryQingdao Municipal Hospital (East)QingdaoChina
  2. 2.Department of SurgeryThe Johns Hopkins University School of MedicineBaltimoreUSA
  3. 3.Department of OncologyThe Johns Hopkins University School of MedicineBaltimoreUSA
  4. 4.Department of Oncology Bioinformatics, The Sidney Kimmel Comprehensive Cancer CenterThe Johns Hopkins University School of MedicineBaltimoreUSA

Personalised recommendations