DNA methylation marker to estimate the breast cancer cell fraction in DNA samples
- 21 Downloads
Estimation of the cancer cell fraction in breast cancer tissue is important for exclusion of samples unsuitable for multigene prognostic assays and a variety of molecular analyses for research. Here, we aimed to establish a breast cancer cell fraction marker based on DNA methylation. First, we screened genes unmethylated in non-cancerous mammary tissues and methylated in breast cancer tissues using microarray data from the TCGA database, and isolated 12 genes. Among them, four genes were selected as candidate marker genes without a high incidence of copy number alterations and with broad coverage across patients. Bisulfite pyrosequencing analysis of additional breast cancer biopsy specimens purified by laser capture microdissection (LCM) excluded two genes, and a combination of SIM1 and CCDC181 was finally selected as a fraction marker. In further additional specimens without LCM purification, the fraction marker was substantially methylated (≥ 20%) with high incidence (50/51). The cancer cell fraction estimated by the fraction marker was significantly correlated with that estimated by microscopic examination (p < 0.0001). Performance of a previously established marker, HSD17B4 methylation, which predicts therapeutic response of HER2-positive breast cancer to trastuzumab, was improved after the correction of cancer cell fraction by the fraction marker. In conclusion, we successfully established a breast cancer cell fraction marker based on DNA methylation.
KeywordsDNA methylation Cancer cell fraction Breast cancer Trastuzumab HER2 Cancer cell content HSD17B4
The authors are grateful to Drs. K. Ichimura, Y. Matsushita, and M. Kitahara of Division of Brain Tumor Translational Research in the National Cancer Center Research Institute for their technical assistance with the usage of the PSQ 96 Pyrosequencing System.
This research was supported by the Program for Promoting Platform of Genomics based Drug Discovery (Grant Number 18kk0305004h0003) from the Japan Agency for Medical Research and Development, AMED.
Compliance with ethical standards
Conflict of interest
The authors state no conflicts of interest regarding this work.
Written informed consent was obtained from all participants.
- 2.Gusnanto A, Wood HM, Pawitan Y, Rabbitts P, Berri S. Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data. Bioinformatics. 2012;28(1):40–7. https://doi.org/10.1093/bioinformatics/btr593.CrossRefGoogle Scholar
- 3.Roma C, Esposito C, Rachiglio AM, Pasquale R, Iannaccone A, Chicchinelli N, et al. Detection of EGFR mutations by TaqMan mutation detection assays powered by competitive allele-specific TaqMan PCR technology. BioMed Res Int. 2013;2013:385087. https://doi.org/10.1155/2013/385087.CrossRefPubMedPubMedCentralGoogle Scholar
- 4.Yau C, Mouradov D, Jorissen RN, Colella S, Mirza G, Steers G, et al. A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. Genome Biol. 2010;11(9):R92. https://doi.org/10.1186/gb-2010-11-9-r92.CrossRefPubMedPubMedCentralGoogle Scholar
- 9.Wu Y, Davison J, Qu X, Morrissey C, Storer B, Brown L, et al. Methylation profiling identified novel differentially methylated markers including OPCML and FLRT2 in prostate cancer. Epigenetics. 2016;11(4):247–58. https://doi.org/10.1080/15592294.2016.1148867.CrossRefPubMedPubMedCentralGoogle Scholar
- 15.Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, Floore A, et al. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat. 2009;116(2):295–302. https://doi.org/10.1007/s10549-008-0130-2.CrossRefPubMedPubMedCentralGoogle Scholar
- 16.Fujii S, Yamashita S, Yamaguchi T, Takahashi M, Hozumi Y, Ushijima T, et al. Pathological complete response of HER2-positive breast cancer to trastuzumab and chemotherapy can be predicted by HSD17B4 methylation. Oncotarget. 2017;8(12):19039–48. https://doi.org/10.18632/oncotarget.15118.CrossRefPubMedPubMedCentralGoogle Scholar
- 20.Takahashi T, Yamahsita S, Matsuda Y, Kishino T, Nakajima T, Kushima R, et al. ZNF695 methylation predicts a response of esophageal squamous cell carcinoma to definitive chemoradiotherapy. J Cancer Res Clin Oncol. 2015;141(3):453–63. https://doi.org/10.1007/s00432-014-1841-x.CrossRefPubMedPubMedCentralGoogle Scholar
- 21.Gyobu K, Yamashita S, Matsuda Y, Igaki H, Niwa T, Oka D, et al. Identification and validation of DNA methylation markers to predict lymph node metastasis of esophageal squamous cell carcinomas. Ann Surg Oncol. 2011;18(4):1185–94. https://doi.org/10.1245/s10434-010-1393-5.CrossRefPubMedPubMedCentralGoogle Scholar
- 22.Robinson MD, Stirzaker C, Statham AL, Coolen MW, Song JZ, Nair SS, et al. Evaluation of affinity-based genome-wide DNA methylation data: effects of CpG density, amplification bias, and copy number variation. Genome Res. 2010;20(12):1719–29. https://doi.org/10.1101/gr.110601.110.CrossRefPubMedPubMedCentralGoogle Scholar
- 30.Asada K, Ando T, Niwa T, Nanjo S, Watanabe N, Okochi-Takada E, et al. FHL1 on chromosome X is a single-hit gastrointestinal tumor-suppressor gene and contributes to the formation of an epigenetic field defect. Oncogene. 2013;32(17):2140–9. https://doi.org/10.1038/onc.2012.228.CrossRefPubMedPubMedCentralGoogle Scholar