Dynamics of the epigenetic index of stem cells as a prognostic factor for metastasis-free survival after neoadjuvant chemotherapy for breast cancer

Intratumoral heterogeneity, particularly the subpopulation of cancer stem cells (CSCs), is associated with therapy resistance and metastasis. The persistence of a small fraction of CSCs after neoadjuvant chemotherapy (NAC) underscores the need to evaluate the prognostic potential of the dynamic epigenetic stem cell index (ESCI) in the context of metastasis-free survival in patients with luminal B subtype breast cancer. Based on bisulfite sequencing data from public repositories, we constructed a reference epigenetic atlas comprising 88 samples of various cell and tissue types. Using Shannon entropy and the Wilcoxon test, 45 highly variable CpG pairs were selected and applied for epigenetic deconvolution. Statistical analysis and deconvolution were performed on 19 paired luminal B breast cancer samples collected before and after NAC. We found that a high baseline ESCI prior to treatment was associated with 10-year metastasis-free survival (p < 0.05). Following NAC, a significant reduction in ESCI was observed in the non-metastatic group, whereas the metastatic group retained baseline ESCI levels. Importantly, the dynamic reduction of ESCI in non-metastatic samples correlated significantly with improved metastasis-free survival (p < 0.05). Thus, ESCI dynamics determined by epigenetic deconvolution may serve as a promising prognostic and predictive biomarker for assessing the efficacy and outcomes of NAC in patients with luminal B breast cancer.

1. Bray F., Laversanne M., Sung H., et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024; 74(3): 229-263. doi:10.3322/caac.21834

2. Lüönd F., Tiede S., Christofori G. Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression. Br J Cancer. 2021;125:164–175. https://doi.org/10.1038/s41416-021-01328-7

3. Aliazis K., Christofides A., Shah R. et al. The tumor microenvironment’s role in the response to immune checkpoint blockade. Nat Cancer. 2025;6:924–937. https://doi.org/10.1038/s43018-025-00986-3

4. Romaniuk-Drapała A., Totoń E., Taube M. et al. Breast Cancer Stem Cells and Tumor Heterogeneity: Characteristics and Therapeutic Strategies. Cancers. 2024; 16(13):2481. https://doi.org/10.3390/cancers16132481

5. Guo Q., Zhou Y., Xie T., et al. Tumor microenvironment of cancer stem cells: Perspectives on cancer stem cell targeting. Genes & Diseases. 2024;11(3):101043. https://doi.org/10.1016/j.gendis.2023.05.024

6. Mazloumi Z., Farahzadi R., Rafat A., et al. Effect of aberrant DNA methylation on cancer stem cell properties. Experimental and Molecular Pathology. 2022;125:104757. https://doi.org/10.1016/j.yexmp.2022.104757

7. Pece S., Disalvatore D., Tosoni D., et al. Identification and clinical validation of a multigene assay that interrogates the biology of cancer stem cells and predicts metastasis in breast cancer: A retrospective consecutive study. EBioMedicine. 2019;42:352–362. https://doi.org/10.1016/j.ebiom.2019.02.036

8. Ferro dos Santos M.R., Giuili E., De Koker A., et al. Computational deconvolution of DNA methylation data from mixed DNA samples. Briefings in Bioinformatics. 2024;25(3): bbae234. https://doi.org/10.1093/bib/bbae234

9. Tanas A.S., Sigin V.O., Kalinkin A.I., et al. Genome-wide methylotyping resolves breast cancer epigenetic heterogeneity and suggests novel therapeutic perspectives. Epigenomics. 2019;11(6):605-617. https://doi.org/10.2217/epi-2018-0213

10. Teschendorff A.E., Breeze C.E., Zheng S.C., et al. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics. 2017;18(1). https://doi.org/10.1186/s12859-017-1511-5

11. Malta T.M., Sokolov A., Gentles A.J., et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell. 2018;173(2):338-354.e15. https://doi.org/10.1016/j.cell.2018.03.034

12. Loyfer N., Magenheim J., Peretz A., et al. A DNA methylation atlas of normal human cell types. Nature. 2023;613(7943):355-364. https://doi.org/10.1038/s41586-022-05580-6

13. Sun T., Yuan J., Zhu Y., et al. Systematic evaluation of methylationbased cell type deconvolution methods for plasma cell-free DNA. Genome biology. 2024;25(1). https://doi.org/10.1186/s13059-02403456-8

14. Salas L.A., Wiencke J.K., Koestler D.C, et al. Tracing human stem cell lineage during development using DNA methylation. 2018;28(9):1285-1295. https://doi.org/10.1101/gr.233213.117

15. El Helou R., Wicinski J., Guille A., et al. Brief Reports: A Distinct DNA Methylation Signature Defines Breast Cancer Stem Cells and Predicts Cancer Outcome. STEM CELLS. 2014;32(11):3031-3036. https://doi.org/10.1002/stem.1792

16. Litviakov N.V., Bychkov V.A., Stakheeva M.N., et al. Breast tumour cell subpopulations with expression of the MYC and OCT4 proteins. Journal of Molecular Histology. 2020;51(6):717-728. https://doi.org/10.1007/s10735-020-09917-1

17. Luo Y., Huang .J, Tang Y., et al. Regional methylome profiling reveals dynamic epigenetic heterogeneity and convergent hypomethylation of stem cell quiescence-associated genes in breast cancer following neoadjuvant chemotherapy. Cell & Bioscience. 2019;9(1). https://doi.org/10.1186/s13578-019-0278-y