Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA292997 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:DNA methylation study in breast cancer [methylation]
| PRJNA257041 | ENA
Project description:Genome-wide methylation analysis detects genes specific to breast cancer hormone receptor status and risk of recurrence
| PRJNA144967 | ENA
Project description:Current clinical guidelines suggest that breast cancers with low hormone receptor expression (LowHR) in 1% to 10% of tumor cells should be regarded as hormone receptor positive tumors. However, clinical data shows that patients with such tumors have a worse outcome compared to patients with hormone receptor expression above 10 %. Further, gene expression studies suggest that these tumors have a TNBC-like signature similar to triple negative breast cancers (TNBC). The goal of this study was to use DNA methylation-based classification to clarify the status for this infrequent but important patient subgroup. We performed whole genome DNA methylation profiling on 23 LowHR breast cancer specimens, including 13 samples with HER2 amplification and compared our results with a reference breast cancer cohort from The Cancer Genome Atlas. Unsupervised clustering and dimensionality reduction revealed that breast cancers with low hormone receptor expression that lacked HER2 amplification usually clustered with TNBC reference samples (8/10; “LowHR TNBC-like”). In contrast, most specimens with low hormone receptor expression and HER2 amplification grouped with hormone receptor positive cancers (11/13; “LowHR HRpos-like”). We observed highly similar DNA methylation patterns of LowHR TNBC-like samples and true TNBCs with almost no differential methylation. Furthermore, the Ki67 proliferation index of LowHR TNBC-like samples as well as clinical outcome parameters were more similar to TNBCs and differed from LowHR_HRpos-like cases. We here demonstrate that LowHR breast cancer comprises two molecularly distinct groups that can be separated by DNA methylation profiling. More clinical data is required for a definite classification of these tumors, but our data strongly suggests that LowHR TNBC-like samples are molecularly, histologically and clinically closely related to TNBC, while LowHR HRpos-like specimens are closely related to hormone receptor positive tumors.
2021-10-01 | GSE163521 | GEO
Project description:Preoperative short-term hormone therapy for hormone receptor positive breast cancer
| PRJNA317765 | ENA
Project description:DNA methylation profiling in the Carolina Breast Cancer Study
| PRJNA223415 | ENA
Project description:A microarray targeting promoters of cancer-related genes was used to evaluate DNA methylation at 935 CpG sites in 517 invasive breast tumors from the Carolina Breast Cancer Study (CBCS), a population-based study of invasive breast cancer. Concensus clustering using methylation (β) values for the 167 most variant CpG loci defined 4 clusters differing most distinctly in hormone receptor (HR) status, intrinsic subtype (luminal versus basal-like) and p53 mutation status. Supervised analyses for HR status, subtype, and p53 status identified differentially methylated CpG loci with considerable overlap (n=266). Concensus clustering also defined a hypermethylated luminal-enriched tumor cluster 3; gene ontology analysis of cluster 3 hypermethylated loci revealed enrichment for developmental genes, including homeobox domain genes (HOXB13, PAX6, IPF1, EYA4, DLK1, IHH, ISL1, TBX1, SOX1, SOX17). The hypermethylated luminal-enriched cluster 3 independently predicted poorer survival in multivariate Cox proportional hazard analysis, and this finding was confirmed in analysis of luminal A tumors. This study demonstrates epigenetic heterogeneity among breast tumors of a single intrinsic subtype, and shows that epigenetic patterns are strongly associated with HR status, subtype, and p53 mutation status. Among HR+ tumors, a gene signature characterized by hypermethylation of developmental genes may have prognostic value. Genes differentially methylated between clinically-important tumor subsets have roles in differentiation, development, and tumor growth and may be critical to inducing and maintaining tumor phenotypes and clinical outcomes. 517 breast tumors, 9 normal breast tissues
2015-04-07 | E-GEOD-51557 | biostudies-arrayexpress
Project description:Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning
| PRJNA140059 | ENA
Project description:DNA methylation profile of triple negative breast cancer specific genes (study)
| PRJNA313441 | ENA
Project description:Genome wide DNA methylation profiling of invasive breast cancer samples isolated from an ethnically diverse group of 80 patients in the Breast Cancer Care in Chicago (BCCC) study. DNA was extracted from formalin fixed, paraffin-embedded samples on 80 patients (21 White, 31 African-American, 23 Hispanic and 5 not reported) (training dataset) enrolled in the BCCC. Hormone receptor status was defined as negative if tumors were negative for both estrogen and progesterone (ER/PR) receptors (N=22/75). Formalin-fixed, paraffin-embedded (FFPE) tumor samples came from the Breast Cancer Care in Chicago (BCCC) study (Dookeran KA, Silva A, Warnecke RB, Rauscher GH: Race/ethnicity and disparities in mastectomy practice in the Breast Cancer Care in Chicago study. Ann Surg Oncol 2015, 22:66â74). Copies of pathology reports and the corresponding set of Hematoxylin and Eosin (H&E) stained slides were requested from the pathology department at each diagnosing institution, and a single pathologist selected tumor blocks representative of the tumor. Two recuts (at 4 µm each) were made from each selected block for H&E staining. The recuts were then examined in order to identify invasive components of the sample, and areas were marked according to tissue component. Cores of invasive tissue (2 mm in diameter) were obtained from the marked areas and DNA was extracted for the DNA methylation study. Bisulphite converted DNA from the 80 samples were hybridised to the Illumina GoldenGate Methylation Cancer Panel I
2016-02-12 | E-GEOD-72110 | biostudies-arrayexpress