Project description:This dataset contains ChIP-seq data profiling genomic binding of H3K27ac and H3K4me3 in single cell-derived control, as well as CRISPR/Cas9 induced tRNA gene deletion clones and intergenic region deletion clones in human cancer cell lines HAP1. In this study, we found a large genomic deletion of 10q23 in Cas9 modified clones and further investigate the effect of H3K27ac binding.
Project description:This study investigated mechanisms of acquired resistance to FGFR inhibitor therapy in RAS/RAF–wild-type colorectal cancer (CRC). RNA sequencing (RNA-seq) data from 47 RAS/RAF-wild-type colorectal cancer stem-cell (CRC-SC) spheroid lines previously deposited under GSE205787 were integrated with newly generated RNA-seq data from 31 additional RAS/RAF-wild-type CRC-SC spheroid lines and seven FGFR inhibitor-resistant CRC-SC derivatives. The resistant derivatives were established from parental CRC-SC spheroid lines by continuous exposure to erdafitinib or futibatinib. Comparative transcriptomic analysis between matched parental and resistant lines revealed consistent upregulation of EGFR and downregulation of PTPROt, a truncated isoform of PTPRO, in resistant derivatives. Gene set enrichment analysis further indicated activation of EGFR-related signaling pathways in FGFR inhibitor-resistant spheroids. In addition, the integrated RNA-seq dataset comprising 78 RAS/RAF-wild-type CRC-SC lines was used to validate the inverse correlation between EGFR and PTPRO mRNA expression, supporting the involvement of PTPROt downregulation and EGFR pathway activation in acquired resistance to FGFR inhibition.
Project description:Transcriptome analysis of 130 breast cancer samples (41 TNBCÂ ; 30 Her2Â ; 30 Luminal B and 29 Luminal A), 11 normal breast tissue samples and 14 TNBC cell lines. This dataset contains 178 arrays. 153 arrays were used to analyze 130 unique breast cancer samples from as many patients and 23 technical duplicates. In addition 11 âNormalâ samples from healthy breast tissue obtained from mammoplasty are included, as well as a collection of 14 breast cancer cell lines. Data production involved different array batches and hybridation series which were accounted for in the pre-processing of the data.
Project description:Transcriptome analysis of 130 breast cancer samples (41 TNBCM-BM- ; 30 Her2M-BM- ; 30 Luminal B and 29 Luminal A), 11 normal breast tissue samples and 14 TNBC cell lines. This dataset contains 178 arrays. 153 arrays were used to analyze 130 unique breast cancer samples from as many patients and 23 technical duplicates. In addition 11 M-bM-^@M-^\NormalM-bM-^@M-^] samples from healthy breast tissue obtained from mammoplasty are included, as well as a collection of 14 breast cancer cell lines. Data production involved different array batches and hybridation series which were accounted for in the pre-processing of the data.
Project description:To characterize genomic instability in breast cancer progression, we examined copy number loss and copy number gain in the MCF10A series of cell lines.
Project description:A variety of newly developed next-generation sequencing technologies are making their way rapidly into the research and clinical applications, for which accuracy and cross-lab reproducibility are critical, and reference standards are much needed. However, there is still a lack of well-characterized reference materials which include epigenomic and proteomic data. Our previous multicenter studies under the SEQC-2 umbrella using a breast cancer cell line with paired B-cell line have produced large amount different genomic data including whole genome sequencing (Illumina, PacBio, Nanopore), HiC, and scRNA-seq with detailed analyses on somatic mutations, single-nucleotide variations (SNVs), and structure variations (SVs). Here we further performed ATAC-seq, Methyl-seq, RNA-seq, and proteomic analyses and provided a comprehensive catalog of epigenomic landscape, which overlapped with the transcriptomes and proteomes for the two cell lines. We identified >7,700 peptide isoforms, where the majority (95%) of the genes had a single peptide isoform and found that the protein expression levels of the transcripts overlapping CGIs were much higher than the protein expression levels of the non-CGI transcripts in both cell lines. We observed that open chromatin regions had low methylation while closed chromatin regions had high methylation, which were largely regulated by CG density, where CG-rich regions had more accessible chromatin, low methylation, and higher gene and protein expressions. The CG-poor regions had higher repressive epigenetic regulations (less open chromatin and higher DNA methylation), resulting in a cell line specific methylation and gene expression patterns. Our studies provide well-defined reference materials consisting of two cell lines with genomic, epigenomic, transcriptomic, scRNA-seq and proteomic characterizations which can serve as standards for validating and benchmarking not only on various omics assays, but also on bioinformatics methods. It will be a valuable resource for both research and clinical communities.