Dataset Information

Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns.

ABSTRACT: Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis-subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal-like subtype into two different prognosis-subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes.

SUBMITTER: Zhang S

PROVIDER: S-EPMC6026876 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns.

Zhang Shumei S Wang Yihan Y Gu Yue Y Zhu Jiang J Ci Ce C Guo Zhongfu Z Chen Chuangeng C Wei Yanjun Y Lv Wenhua W Liu Hongbo H Zhang Dongwei D Zhang Yan Y

Molecular oncology 20180521 7

Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis-subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The spe ...[more]

PMID: 29675884

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Project description:Tumor subtype and menopausal status are strong predictors of breast cancer (BC) prognosis. We aimed to find and validate subtype- or menopausal-status-specific changes in tumor DNA methylation (DNAm) associated with all-cause mortality or BC progression. Associations between site-specific tumor DNAm and BC prognosis were estimated among The Cancer Genome Atlas participants (n = 692) with Illumina Infinium HumanMethylation450 BeadChip array data. All-cause mortality and BC progression were modeled using Cox proportional hazards models stratified by tumor subtypes, adjusting for age, race, stage, menopausal status, tumor purity, and cell type proportion. Effect measure modification by subtype and menopausal status were evaluated by incorporating a product term with DNAm. Site-specific inference was used to identify subtype- or menopausal-status-specific differentially methylated regions (DMRs) and functional pathways. The validation of the results was carried out on an independent dataset (GSE72308; n = 180). We identified a total of fifteen unique CpG probes that were significantly associated ( P≤1×10-7 with survival outcomes in subtype- or menopausal-status-specific manner. Seven probes were associated with overall survival (OS) or progression-free interval (PFI) for women with luminal A subtype, and four probes were associated with PFI for women with luminal B subtype. Five probes were associated with PFI for post-menopausal women. A majority of significant probes showed a lower risk of OS or BC progression with higher DNAm. We identified subtype- or menopausal-status-specific DMRs and functional pathways of which top associated pathways differed across subtypes or menopausal status. None of significant probes from site-specific analyses met genome-wide significant level in validation analyses while directions and magnitudes of coefficients showed consistent pattern. We have identified subtype- or menopausal-status-specific DNAm biomarkers, DMRs and functional pathways associated with all-cause mortality or BC progression, albeit with limited validation. Future studies with larger independent cohort of non-post-menopausal women with non-luminal A subtypes are warranted for identifying subtype- and menopausal-status-specific DNAm biomarkers for BC prognosis.

Project description:BackgroundAbnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to determine whether the distinction of the inflammatory breast cancer (IBC) phenotype from the non-IBC phenotype by transcriptomics could be sustained by methylomics.Methodology/principal findingsWe performed methylation profiling on a cohort of IBC (N = 19) and non-IBC (N = 43) samples using the Illumina Infinium Methylation Assay. These results were correlated with gene expression profiles. Methylation values allowed separation of breast tumor samples into high and low methylation groups. This separation was significantly related to DNMT3B mRNA levels. The high methylation group was enriched for breast tumor samples from patients with distant metastasis and poor prognosis, as predicted by the 70-gene prognostic signature. Furthermore, this tumor group tended to be enriched for IBC samples (54% vs. 24%) and samples with a high genomic grade index (67% vs. 38%). A set of 16 CpG loci (14 genes) correctly classified 97% of samples into the low or high methylation group. Differentially methylated genes appeared to be mainly related to focal adhesion, cytokine-cytokine receptor interactions, Wnt signaling pathway, chemokine signaling pathways and metabolic processes. Comparison of IBC with non-IBC led to the identification of only four differentially methylated genes (TJP3, MOGAT2, NTSR2 and AGT). A significant correlation between methylation values and gene expression was shown for 4,981 of 6,605 (75%) genes.Conclusions/significanceA subset of clinical samples of breast cancer was characterized by high methylation levels, which coincided with increased DNMT3B expression. Furthermore, an association was observed with molecular signatures indicative of poor patient prognosis. The results of the current study also suggest that aberrant DNA methylation is not the main force driving the molecular biology of IBC.

Project description:Mammography is used to detect breast cancer (BC), but its sensitivity is limited, especially for dense breasts. Circulating cell-free DNA (cfDNA) methylation tests is expected to compensate for the deficiency of mammography. We derived a specific panel of markers based on computational analysis of the DNA methylation profiles from The Cancer Genome Atlas (TCGA). Through training (n = 160) and validation set (n = 69), we developed a diagnostic prediction model with 26 markers, which yielded a sensitivity of 89.37% and a specificity of 100% for differentiating malignant disease from normal lesions [AUROC = 0.9816 (95% CI: 96.09-100%), and AUPRC = 0.9704 (95% CI: 94.54-99.46%)]. A simplified 4-marker model including cg23035715, cg16304215, cg20072171, and cg21501525 had a similar diagnostic power [AUROC = 0.9796 (95% CI: 95.56-100%), and AUPRC = 0.9220 (95% CI: 91.02-94.37%)]. We found that a single cfDNA methylation marker, cg23035715, has a high diagnostic power [AUROC = 0.9395 (95% CI: 89.72-99.27%), and AUPRC = 0.9111 (95% CI: 88.45-93.76%)], with a sensitivity of 84.90% and a specificity of 93.88%. In an independent testing dataset (n = 104), the obtained diagnostic prediction model discriminated BC patients from normal controls with high accuracy [AUROC = 0.9449 (95% CI: 90.07-98.91%), and AUPRC = 0.8640 (95% CI: 82.82-89.98%)]. We compared the diagnostic power of cfDNA methylation and mammography. Our model yielded a sensitivity of 94.79% (95% CI: 78.72-97.87%) and a specificity of 98.70% (95% CI: 86.36-100%) for differentiating malignant disease from normal lesions [AUROC = 0.9815 (95% CI: 96.75-99.55%), and AUPRC = 0.9800 (95% CI: 96.6-99.4%)], with better diagnostic power and had better diagnostic power than that of using mammography [AUROC = 0.9315 (95% CI: 89.95-96.34%), and AUPRC = 0.9490 (95% CI: 91.7-98.1%)]. In addition, hypermethylation profiling provided insights into lymph node metastasis stratifications (p < 0.05). In conclusion, we developed and tested a cfDNA methylation model for BC diagnosis with better performance than mammography.

Project description:BACKGROUND:DNA methylation (DNAm) age was found to be an indicator for all-cause mortality, cancer incidence, and longevity, but no study has involved in the associations of DNAm age with the prognosis of breast cancer. METHODS:We retrieved information of 1076 breast cancer patients from Genomic Data Commons (GDC) data portal on March 30, 2017, including breast cancer DNAm profiling, demographic features, clinicopathological parameters, recurrence, and all-cause fatality. Horvath's method was applied to calculate the DNAm age. Cox proportional hazards regression models were used to test the associations between DNAm age of the cancerous tissues and the prognosis (recurrence of breast cancer and all-cause fatality) with or without adjusting for chronological age and clinicopathological parameters. RESULTS:The DNAm age was markedly decelerated in the patients who were premenopausal, ER or PR negative, HER2-enriched or basal-like than their counterparts. In the first five-year follow-up dataset for survival, every ten-year increase in DNAm age was associated with a 15% decrease in fatality; subjects with DNAm age in the second (HR: 0.52; 95%CI: 0.29-0.92), the third (HR: 0.49; 95%CI: 0.27-0.87) and the fourth quartile (HR: 0.38; 95%CI: 0.20-0.72) had significant longer survival time than those in the first quartile. In the first five-year follow-up dataset for recurrence, every ten-year increase in DNAm age was associated with a 14% decrease of the recurrence; in the categorical analysis, a clear dose-response was shown (P for trend =0.02) and the fourth quartile was associated with a longer recurrence free survival (HR: 0.32; 95%CI: 0.14-0.74). In the full follow-up dataset, similar results were obtained. CONCLUSIONS:DNAm age of breast cancer tissue, which associated with menopausal status and pathological features, was a strong independent predictor of the prognosis. It was suggested that the prognosis of breast cancer was related to intrinsic biological changes and specific molecular targets for treatment of breast cancer may be implicit.

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Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns.

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Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns.

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