SpringerLink
Log in

Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer

  • Breast Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

In the era of molecular stratification and effective multimodality therapies, surgical staging of the axilla is becoming less relevant for patients with estrogen receptor (ER)-positive early-stage breast cancer (EBC). Therefore, a nonsurgical method for accurately predicting lymph node disease is the next step in the de-escalation of axillary surgery. This study sought to identify epigenetic signatures in the primary tumor that accurately predict lymph node status.

Patients and Methods

We selected a cohort of patients in The Cancer Genome Atlas (TCGA) with ER-positive, HER2-negative invasive ductal carcinomas, and clinically-negative axillae (n = 127). Clinicopathological nomograms from the Memorial Sloan Kettering Cancer Center (MSKCC) and the MD Anderson Cancer Center (MDACC) were calculated. DNA methylation (DNAm) patterns from primary tumor specimens were compared between patients with pN0 and those with > pN0. The cohort was divided into training (n = 85) and validation (n = 42) sets. Random forest was employed to obtain the combinations of DNAm features with the highest accuracy for stratifying patients with > pN0. The most efficient combinations were selected according to the area under the curve (AUC).

Results

Clinicopathological models displayed a modest predictive potential for identifying > pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with > pN0 were identified (n = 1656). DMS showed a similar performance to the MSKCC model (AUC = 0.76, p = 0.83). Machine learning approaches generated five epigenetic classifiers, which showed higher discriminative potential than the clinicopathological variables tested (AUC > 0.88, p < 0.05).

Conclusions

Epigenetic classifiers based on primary tumor characteristics can efficiently stratify patients with no lymph node involvement from those with axillary lymph node disease, thereby providing an accurate method of staging the axilla.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Society of Surgical Oncology. Choosing Wisely: Don’t routinely use sentinel node biopsy in clinically node negative women ≥ 70 years of age with early stage hormone receptor positive, HER2 negative invasive breast cancer. https://www.choosingwisely.org/clinician-lists/sso-sentinel-node-biopsy-in-node-negative-women-70-and-over/. Updated 07/27/2021. Accessed 8 March 2022.

  2. National Comprehensive Cancer Network. Breast Cancer (Version 3.2022). https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf. Accessed 18 May 2022.

  3. Giuliano AE, Ballman KV, McCall L, et al. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011 (Alliance) randomized clinical trial. JAMA. 2017;318(10):918–26.

    Article  Google Scholar 

  4. Kalinsky K, Barlow WE, Gralow JR, et al. 21-gene assay to inform chemotherapy benefit in node-positive breast cancer. N Engl J Med. 2021;385(25):2336–47.

    Article  CAS  Google Scholar 

  5. Veronesi U, Paganelli G, Viale G, et al. A randomized comparison of sentinel-node biopsy with routine axillary dissection in breast cancer. N Engl J Med. 2003;349(6):546–53.

    Article  Google Scholar 

  6. Krag DN, Anderson SJ, Julian TB, et al. Sentinel-lymph-node resection compared with conventional axillary-lymph-node dissection in clinically node-negative patients with breast cancer: overall survival findings from the NSABP B-32 randomised phase 3 trial. Lancet Oncol. 2010;11(10):927–33.

    Article  Google Scholar 

  7. Bevilacqua JL, Kattan MW, Fey JV, Cody HS 3rd, Borgen PI, Van Zee KJ. Doctor, what are my chances of having a positive sentinel node? A validated nomogram for risk estimation. J Clin Oncol. 2007;25(24):3670–9.

    Article  Google Scholar 

  8. Klar M, Foeldi M, Markert S, Gitsch G, Stickeler E, Watermann D. Good prediction of the likelihood for sentinel lymph node metastasis by using the MSKCC nomogram in a German breast cancer population. Ann Surg Oncol. 2009;16(5):1136–42.

    Article  CAS  Google Scholar 

  9. Chen JY, Chen JJ, Yang BL, et al. Predicting sentinel lymph node metastasis in a Chinese breast cancer population: assessment of an existing nomogram and a new predictive nomogram. Breast Cancer Res Treat. 2012;135(3):839–48.

    Article  Google Scholar 

  10. Reyal F, Rouzier R, Depont-Hazelzet B, et al. The molecular subtype classification is a determinant of sentinel node positivity in early breast carcinoma. PLoS One. 2011;6(5):e20297.

    Article  CAS  Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. DiNome ML, Orozco JIJ, Matsuba C, et al. Clinicopathological features of triple-negative breast cancer epigenetic subtypes. Ann Surg Oncol. 2019;26(10):3344–53.

    Article  Google Scholar 

  13. Urrutia G, Laurito S, Marzese DM, et al. Epigenetic variations in breast cancer progression to lymph node metastasis. Clin Exp Metastasis. 2015;32(2):99–110.

    Article  CAS  Google Scholar 

  14. 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.

    Article  CAS  Google Scholar 

  15. National Cancer Institute/GDC Data Portal. Harmonized Cancer Datasets. Genomic Data Commons Data Portal. https://portal.gdc.cancer.gov. Accessed 1 August 2020.

  16. Memorial Sloan Kettering Cancer Center. Breast cancer nomogram: sentinel lymph node metastasis. http://nomograms.mskcc.org/breast/BreastSLNodeMetastasisPage.aspx. Accessed 9 November 2020.

  17. MD Anderson Cancer Center. Breast cancer nomogram to predict positive sentinel lymph nodes, without neoadjuvant chemotherapy. http://www3.mdanderson.org/app/medcalc/bc_nomogram3/index.cfm?pagename=sln. Accessed 9 November 2020.

  18. 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.

    Article  Google Scholar 

  19. Aran D, Sirota M, Butte AJ. Systematic pan-cancer analysis of tumour purity. Nat Commun. 2015;6:8971.

    Article  CAS  Google Scholar 

  20. John CR, Watson D, Russ D, et al. M3C: monte carlo reference-based consensus clustering. Sci Rep. 2020;10(1):1816.

    Article  CAS  Google Scholar 

  21. O’Connor T, Grant CE, Bodén M, Bailey TL. T-gene: improved target gene prediction. Bioinformatics. 2020;36(12):3902–4.

    Article  CAS  Google Scholar 

  22. Grote S. GOfuncR: Gene ontology enrichment using FUNC. R package version 1.16.0. 2022.

  23. Diaz-Uriarte R. GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest. BMC Bioinform. 2007;8:328.

    Article  Google Scholar 

  24. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21(20):3940–1.

    Article  CAS  Google Scholar 

  25. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.

    Article  CAS  Google Scholar 

  26. DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics. CA Cancer J Clin. 2019;69(6):438–51.

    Article  Google Scholar 

  27. Donker M, van Tienhoven G, Straver ME, et al. Radiotherapy or surgery of the axilla after a positive sentinel node in breast cancer (EORTC 10981–22023 AMAROS): a randomised, multicentre, open-label, phase 3 non-inferiority trial. Lancet Oncol. 2014;15(12):1303–10.

    Article  Google Scholar 

  28. Qiu PF, Liu JJ, Wang YS, et al. Risk factors for sentinel lymph node metastasis and validation study of the MSKCC nomogram in breast cancer patients. Jpn J Clin Oncol. 2012;42(11):1002–7.

    Article  Google Scholar 

  29. Moran S, Martínez-Cardús A, Sayols S, et al. Epigenetic profiling to classify cancer of unknown primary: a multicentre, retrospective analysis. Lancet Oncol. 2016;17(10):1386–95.

    Article  Google Scholar 

  30. Capper D, Jones DTW, Sill M, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555(7697):469–74.

    Article  CAS  Google Scholar 

  31. 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.

    Article  CAS  Google Scholar 

  32. 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.

    Article  Google Scholar 

  33. Ensenyat-Mendez M, Íñiguez-Muñoz S, Sesé B, Marzese DM. iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes. BioData Min. 2021;14(1):42.

    Article  CAS  Google Scholar 

  34. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94.

    Article  CAS  Google Scholar 

  35. Møller M, Strand SH, Mundbjerg K, et al. Heterogeneous patterns of DNA methylation-based field effects in histologically normal prostate tissue from cancer patients. Sci Rep. 2017;7:40636.

    Article  Google Scholar 

  36. Uhl B, Gevensleben H, Tolkach Y, et al. PITX2 DNA methylation as biomarker for individualized risk assessment of prostate cancer in core biopsies. J Mol Diagn. 2017;19(1):107–14.

    Article  CAS  Google Scholar 

Download references

Acknowledgment

The research described was supported by NIH/National Center for Advancing Translational Science (NCATS) UCLA CTSI Grant No. UL1TR001881, the Associates for Breast and Prostate Cancer Studies (ABCs) Foundation, the Fashion Footwear Association of New York (FFANY) Foundation, the Spanish Instituto de la Salud Carlos III (#CP17/0018), co-funded by ERDF “A way to make Europe,” the Asociación Española Contra el Cancer (AECC), and the UCLA Breast Cancer Epigenetics Research Program.

Author information

Author notes

  1. Javier I. J. Orozco and Julie Le have contributed equally to this work.

Authors and Affiliations

  1. Saint John’s Cancer Institute, Providence Saint John’s Health Center, Santa Monica, CA, USA

    Javier I. J. Orozco MD

  2. Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Julie Le MD & Jennifer L. Baker MD

  3. Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d’Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, Palma, Spain

    Miquel Ensenyat-Mendez MSc & Diego M. Marzese PhD

  4. Department of Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Joanne Weidhaas MD, PhD

  5. Department of Medicine Statistics Core, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Alexandra Klomhaus PhD

  6. Department of Surgery, Duke University School of Medicine, Durham, NC, USA

    Maggie L. DiNome MD

Authors
  1. Javier I. J. Orozco MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julie Le MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miquel Ensenyat-Mendez MSc
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jennifer L. Baker MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joanne Weidhaas MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alexandra Klomhaus PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Diego M. Marzese PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Maggie L. DiNome MD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Diego M. Marzese PhD or Maggie L. DiNome MD.

Ethics declarations

Disclosure

The authors have no conflict of interest disclosures to report.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Orozco, J.I.J., Le, J., Ensenyat-Mendez, M. et al. Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer. Ann Surg Oncol 29, 6407–6414 (2022). https://doi.org/10.1245/s10434-022-12143-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1245/s10434-022-12143-6

Search

Navigation