Breast Cancer Sequencing Project
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR022654_1.fastq.gz | Fastqsanger.gz | |||
| ERR022654_2.fastq.gz | Fastqsanger.gz | |||
| ERR022655_1.fastq.gz | Fastqsanger.gz | |||
| ERR022655_2.fastq.gz | Fastqsanger.gz | |||
| ERR022656_1.fastq.gz | Fastqsanger.gz |
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2020-07-17 | PXD007088 | Pride
Project description:<p>Tumor relapse from treatment-resistant cells (minimal residual disease, MRD) underlies most breast cancer related deaths. Yet, the molecular characteristics defining their malignancy have largely remained elusive. Here we integrated multi-omics data from a tractable organoid system with a metabolic modelling approach to uncover the metabolic and regulatory idiosyncrasies of the MRD. We find that the resistant cells, despite their non-proliferative phenotype and the absence of oncogenic signaling, feature increased glycolysis and activity of certain urea cycle enzymes reminiscent of the tumor. This metabolic distinctiveness was also evident in a mouse model and in transcriptomic data from patients following neo-adjuvant therapy. We further identified a marked similarity in DNA methylation profiles between tumor and residual cells. Taken together, our data reveal a metabolic and epigenetic memory of the treatment-resistant cells. We further demonstrate that the memorized elevated glycolysis in MRD is crucial for their survival and can be targeted using a small molecule inhibitor without impacting normal cells. The metabolic aberrances of MRD thus offer new therapeutic opportunities for post-treatment care to prevent breast tumor recurrence.</p>
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Project description:differential miRNA expression between breast tumor tissue and normal tissue
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Project description:A bank of human breast tumor xenografts was established by serial passage of primary breast tumor fragments in the cleared mammary fat pads of immuno-compromised NOD-SCID-IL2Rgammac–/– mice. The 15 expression profiles from five different tumors were classified using the ClaNC method [1]. The training data set consisted of 94 samples from Herschkowitz et al. [2]; class labels were based on tumor subtype (Basal-like, Luminal A, Luminal B, Claudin-low, HER2+/ER- and Normal breast-like). [1] Dabney AR (2005) Classification of microarrays to nearest centroids. Bioinformatics 21:4148-4154. [2] Herschkowitz JI, et al. (2007) Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol 8:R76.
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Project description:Aberrant DNA methylation is frequently observed in breast cancer. However, the relationship between methylation patterns and the heterogeneity of breast cancer has not been comprehensively characterized. Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 188 human breast tumors. Unsupervised bootstrap consensus clustering was performed to identify DNA methylation epigenetic subgroups (epitypes). The Cancer Genome Atlas data, incluing methylation profiles of 669 human breast tumors, was utilized for validation. The identified epitypes were characterized by integration with publicly available genome-wide data, including gene expression levels, DNA copy numbers, whole-exome sequencing data, and chromatin states. We identified seven breast cancer epitypes. One epitype was distinctly associated with basal-like tumors and with BRCA1 mutations, one epitype contained a subset of ERBB2-amplified tumors characterized by multiple additional amplifications and the most complex genomes, and one epitype displayed a methylation profile similar to normal epithelial cells. Luminal tumors were stratified into the remaining four epitypes, with differences in promoter hypermethylation, global hypomethylation, proliferative rates and genomic instability. We observed two dominant patterns of aberrant methylation in breast cancer. One pattern, constitutively methylated in both basal-like and luminal breast cancer, was linked to genes with promoters in a Polycomb-repressed state in normal epithelial cells and displayed no correlation to gene expression levels. The second pattern correlated with gene expression levels and was associated with methylation in luminal tumors and genes with active promoters in normal epithelial cells. Our results suggest that hypermethylation patterns in basal-like breast cancer may have limited influence on tumor progression and instead reflects the repressed chromatin state of the tissue of origin. On the contrary, hypermethylation patterns specific to luminal breast cancer influence gene expression, may contribute to tumor progression, and may present an actionable epigenetic alteration in some luminal breast cancers. Genome-wide DNA methylation analysis of 188 breast cancers using Illumina Human Methylation 450K Beadchips.
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