Annals of Surgical Oncology

, Volume 26, Issue 10, pp 3344–3353 | Cite as

Clinicopathological Features of Triple-Negative Breast Cancer Epigenetic Subtypes

  • Maggie L. DiNome
  • Javier I. J. Orozco
  • Chikako Matsuba
  • Ayla O. Manughian-Peter
  • Miquel Ensenyat-Mendez
  • Shu-Ching Chang
  • John R. Jalas
  • Matthew P. Salomon
  • Diego M. MarzeseEmail author
Breast Oncology



Triple-negative breast cancer (TNBC) is a heterogeneous collection of breast tumors with numerous differences including morphological characteristics, genetic makeup, immune-cell infiltration, and response to systemic therapy. DNA methylation profiling is a robust tool to accurately identify disease-specific subtypes. We aimed to generate an epigenetic subclassification of TNBC tumors (epitypes) with utility for clinical decision-making.


Genome-wide DNA methylation profiles from TNBC patients generated in the Cancer Genome Atlas project were used to build machine learning-based epigenetic classifiers. Clinical and demographic variables, as well as gene expression and gene mutation data from the same cohort, were integrated to further refine the TNBC epitypes.


This analysis indicated the existence of four TNBC epitypes, named as Epi-CL-A, Epi-CL-B, Epi-CL-C, and Epi-CL-D. Patients with Epi-CL-B tumors showed significantly shorter disease-free survival and overall survival [log rank; P = 0.01; hazard ratio (HR) 3.89, 95% confidence interval (CI) 1.3–11.63 and P = 0.003; HR 5.29, 95% CI 1.55–18.18, respectively]. Significant gene expression and mutation differences among the TNBC epitypes suggested alternative pathway activation that could be used as ancillary therapeutic targets. These epigenetic subtypes showed complementarity with the recently described TNBC transcriptomic subtypes.


TNBC epigenetic subtypes exhibit significant clinical and molecular differences. The links between genetic make-up, gene expression programs, and epigenetic subtypes open new avenues in the development of laboratory tests to more efficiently stratify TNBC patients, helping optimize tailored treatment approaches.



This work was supported by the Associates for Breast and Prostate Cancer Studies (ABCs) Foundation, the Fashion Footwear Association of New York (FFANY) Foundation, and the John Wayne Cancer Institute Translational Research Fund.


None of the authors have any financial disclosures. The authors declare no competing interests.

Supplementary material

10434_2019_7565_MOESM1_ESM.pdf (276 kb)
Supplementary material 1 (PDF 276 kb)
10434_2019_7565_MOESM2_ESM.pdf (299 kb)
Supplementary material 2 (PDF 299 kb)
10434_2019_7565_MOESM3_ESM.pdf (276 kb)
Supplementary material 3 (PDF 275 kb)


  1. 1.
    Foulkes WD, Smith IE, Reis-Filho JS. Triple-negative breast cancer. N Engl J Med. 2010;363(20):1938–48.CrossRefGoogle Scholar
  2. 2.
    Lehmann BD, Bauer JA, Chen X, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750–67.CrossRefGoogle Scholar
  3. 3.
    Burstein MD, Tsimelzon A, Poage GM, et al. Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin Cancer Res. 2015;21(7):1688–98.CrossRefGoogle Scholar
  4. 4.
    Jezequel P, Loussouarn D, Guerin-Charbonnel C, et al. Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response. Breast Cancer Res. 2015;17:43.CrossRefGoogle Scholar
  5. 5.
    Liu YR, Jiang YZ, Xu XE, et al. Comprehensive transcriptome analysis identifies novel molecular subtypes and subtype-specific RNAs of triple-negative breast cancer. Breast Cancer Res. 2016;18(1):33.CrossRefGoogle Scholar
  6. 6.
    Lehmann BD, Jovanovic B, Chen X, et al. Refinement of triple-negative breast cancer molecular subtypes: implications for neoadjuvant chemotherapy selection. PLoS ONE. 2016;11(6):e0157368.CrossRefGoogle Scholar
  7. 7.
    Jeschke J, Bizet M, Desmedt C, et al. DNA methylation-based immune response signature improves patient diagnosis in multiple cancers. J Clin Invest. 2017;127(8):3090–102.CrossRefGoogle Scholar
  8. 8.
    Mundbjerg K, Chopra S, Alemozaffar M, et al. Identifying aggressive prostate cancer foci using a DNA methylation classifier. Genome Biol. 2017;18(1):3.CrossRefGoogle Scholar
  9. 9.
    Wu SP, Cooper BT, Bu F, et al. DNA methylation-based classifier for accurate molecular diagnosis of bone sarcomas. JCO Precis Oncol. 2017;1:1–11.Google Scholar
  10. 10.
    Klughammer J, Kiesel B, Roetzer T, et al. The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space. Nat Med. 2018;24(10):1611–24.CrossRefGoogle Scholar
  11. 11.
    Sahm F, Schrimpf D, Stichel D, et al. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol. 2017;18(5):682–94.CrossRefGoogle Scholar
  12. 12.
    Capper D, Jones DTW, Sill M, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555(7697):469–74.CrossRefGoogle Scholar
  13. 13.
    Orozco JIJ, Knijnenburg TA, Manughian-Peter AO, et al. Epigenetic profiling for the molecular classification of metastatic brain tumors. Nat Commun. 2018;9(1):4627.CrossRefGoogle Scholar
  14. 14.
    Marzese DM, Scolyer RA, Huynh JL, et al. Epigenome-wide DNA methylation landscape of melanoma progression to brain metastasis reveals aberrations on homeobox D cluster associated with prognosis. Hum Mol Genet. 2014;23(1):226–38.CrossRefGoogle Scholar
  15. 15.
    Moran S, Martinez-Cardus A, Sayols S, et al. Epigenetic profiling to classify cancer of unknown primary: a multicentre, retrospective analysis. Lancet Oncol. 2016;17(10):1386–95.CrossRefGoogle Scholar
  16. 16.
    Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–04.CrossRefGoogle Scholar
  17. 17.
    Colaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71.CrossRefGoogle Scholar
  18. 18.
    Aran D, Sirota M, Butte AJ. Systematic pan-cancer analysis of tumour purity. Nat Commun. 2015;6:8971.CrossRefGoogle Scholar
  19. 19.
    Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–69.CrossRefGoogle Scholar
  20. 20.
    Salomon MP, Orozco JIJ, Wilmott JS, et al. Brain metastasis DNA methylomes, a novel resource for the identification of biological and clinical features. Sci Data. 2018;5:180245.CrossRefGoogle Scholar
  21. 21.
    Chen X, Li J, Gray WH, et al. TNBCtype: a subtyping tool for triple-negative breast cancer. Cancer Inform. 2012;11:147–56.CrossRefGoogle Scholar
  22. 22.
    Franz M, Rodriguez H, Lopes C, et al. GeneMANIA update 2018. Nucleic Acids Res. 2018;46(W1):W60–W64.CrossRefGoogle Scholar
  23. 23.
    Napolitano F, Carrella D, Mandriani B, et al. gene2drug: a computational tool for pathway-based rational drug repositioning. Bioinformatics. 2018;34(9):1498–505.CrossRefGoogle Scholar
  24. 24.
    Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496–509.CrossRefGoogle Scholar
  25. 25.
    de Glas NA, Kiderlen M, Vandenbroucke JP, et al. Performing survival analyses in the presence of competing risks: a clinical example in older breast cancer patients. J Natl Cancer Inst. 2016;108(5):djv366.CrossRefGoogle Scholar
  26. 26.
    Devarajan K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol. 2008;4(7):e1000029.CrossRefGoogle Scholar
  27. 27.
    He X, Xiang H, Zong X, et al. CDK2-AP1 inhibits growth of breast cancer cells by regulating cell cycle and increasing docetaxel sensitivity in vivo and in vitro. Cancer Cell Int. 2014;14(1):130.CrossRefGoogle Scholar
  28. 28.
    Li S, Wu Z, Ma P, et al. Ligand-dependent EphA7 signaling inhibits prostate tumor growth and progression. Cell Death Dis. 2017;8(10):e3122.CrossRefGoogle Scholar
  29. 29.
    Singh MK, Nicolas E, Gherraby W, Dadke D, Lessin S, Golemis EA. HEI10 negatively regulates cell invasion by inhibiting cyclin B/Cdk1 and other promotility proteins. Oncogene. 2007;26(33):4825–32.CrossRefGoogle Scholar
  30. 30.
    Avanzato D, Pupo E, Ducano N, et al. High USP6NL levels in breast cancer sustain chronic AKT phosphorylation and GLUT1 stability fueling aerobic glycolysis. Cancer Res. 2018;78(13):3432–44.Google Scholar
  31. 31.
    Zhu X, Gu J, Qian H. Esculetin attenuates the growth of lung cancer by downregulating wnt targeted genes and suppressing NF-kappaB. Arch Bronconeumol. 2018;54(3):128–33.CrossRefGoogle Scholar
  32. 32.
    Yan L, Yu HH, Liu YS, Wang YS, Zhao WH. Esculetin enhances the inhibitory effect of 5-fluorouracil on the proliferation, migration and epithelial-mesenchymal transition of colorectal cancer. Cancer Biomark. 2019;24(2):231–40.CrossRefGoogle Scholar
  33. 33.
    Andre F, Ciruelos E, Rubovszky G, et al. Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced breast cancer. N Engl J Med. 2019;380(20):1929–40.CrossRefGoogle Scholar

Copyright information

© Society of Surgical Oncology 2019

Authors and Affiliations

  • Maggie L. DiNome
    • 1
  • Javier I. J. Orozco
    • 2
  • Chikako Matsuba
    • 3
  • Ayla O. Manughian-Peter
    • 2
  • Miquel Ensenyat-Mendez
    • 4
  • Shu-Ching Chang
    • 5
  • John R. Jalas
    • 6
  • Matthew P. Salomon
    • 3
  • Diego M. Marzese
    • 2
    Email author
  1. 1.Department of Surgery, David Geffen School of MedicineUniversity California Los Angeles (UCLA)Los AngelesUSA
  2. 2.Cancer Epigenetics LaboratoryJohn Wayne Cancer Institute at Providence Saint John’s Health CenterSanta MonicaUSA
  3. 3.Computational Biology LaboratoryJohn Wayne Cancer Institute at Providence St. John’s Health CenterSanta MonicaUSA
  4. 4.Cancer Cell Biology GroupBalearic Islands Health Research Institute (IdISBa)PalmaSpain
  5. 5.Medical Data Research CenterProvidence Saint Joseph HealthPortlandUSA
  6. 6.Department of PathologyProvidence Saint John’s Health CenterSanta MonicaUSA

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