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Changes in the three-dimensional (3D) structure of the genome are an emerging hallmark of cancer. Cancer-associated copy number variants and single nucleotide polymorphisms promote rewiring of chromatin loops, disruption of topologically associating domains (TADs), active/inactive chromatin state switching, leading to oncogene expression and silencing of tumor suppressors. However, little is known about 3D changes during cancer progression to a chemotherapy-resistant state. We integrated chromatin conformation capture (Hi-C), RNA-seq, and whole-genome sequencing obtained from triple-negative breast cancer patient-derived xenograft primary tumors (UCD52) and carboplatin-resistant samples and found increased short-range (< 2 Mb) interactions, chromatin looping, formation of TAD, chromatin state switching into a more active state, and amplification of ATP-binding cassette transporters. Transcriptome changes suggested the role of long-noncoding RNAs in carboplatin resistance. Rewiring of the 3D genome was associated with TP53, TP63, BATF, FOS-JUN family of transcription factors and led to activation of aggressiveness-, metastasis- and other cancer-related pathways. Integrative analysis highlighted increased ribosome biogenesis and oxidative phosphorylation, suggesting the role of mitochondrial energy metabolism. Our results suggest that 3D genome remodeling may be a key mechanism underlying carboplatin resistance. [ABSTRACT FROM AUTHOR]

The document is a correction notice for an article titled "Rewiring of the 3D genome during acquisition of carboplatin resistance in a triple-negative breast cancer patient-derived xenograft" published in Scientific Reports. The correction addresses an error in the Acknowledgements section, updating the funding information and adding a new grant. The correction was made by a group of authors led by Mikhail G. Dozmorov and J. Chuck Harrell. [Extracted from the article]

The goals of this study were to identify transcriptomic changes that arise in basal-like breast cancer cells during the development of resistance to epidermal growth factor receptor inhibitors (EGFRi) and to identify drugs that are cytotoxic once EGFRi resistance occurs. Human patient-derived xenografts (PDXs) were grown in immunodeficient mice and treated with a set of EGFRi; the EGFRi erlotinib was selected for more expansive in vivo studies. Single-cell RNA sequencing was performed on mammary tumors from the basal-like PDX WHIM2 that was treated with vehicle or erlotinib for 9 weeks. The PDX was then subjected to long-term erlotinib treatment in vivo. Through serial passaging, an erlotinib-resistant subline of WHIM2 was generated. Bulk RNA-sequencing was performed on parental and erlotinib-resistant tumors. In vitro high-throughput drug screening with > 500 clinically used compounds was performed on parental and erlotinib-resistant cells. Previously published bulk gene expression microarray data from MMTV-Wnt1 tumors were contrasted with the WHIM2 PDX data. Erlotinib effectively inhibited WHIM2 tumor growth for approximately 4 weeks. Compared to untreated cells, single-cell RNA sequencing revealed that a greater proportion of erlotinib-treated cells were in the G1 phase of the cell cycle. Comparison of WHIM2 and MMTV-Wnt1 gene expression data revealed a set of 38 overlapping genes that were differentially expressed in the erlotinib-resistant WHIM2 and MMTV-Wnt1 tumors. Comparison of all three data types revealed five genes that were upregulated across all erlotinib-resistant samples: IL19, KLK7, LCN2, SAA1, and SAA2. Of these five genes, LCN2 was most abundantly expressed in triple-negative breast cancers, and its knockdown restored erlotinib sensitivity in vitro. Despite transcriptomic differences, parental and erlotinib-resistant WHIM2 displayed similar responses to the majority of drugs assessed for cytotoxicity in vitro. This study identified transcriptomic changes arising in erlotinib-resistant basal-like breast cancer. These data could be used to identify a biomarker or develop a gene signature predictive of patient response to EGFRi. Future studies should explore the predictive capacity of these gene signatures as well as how LCN2 contributes to the development of EGFRi resistance. [ABSTRACT FROM AUTHOR]

The whole genome sequencing data generated in this study have been submitted to the NCBI SRA database (https://www.ncbi.nlm.nih.gov/bioproject/) under accession number PRJNA832117, reviewer's accession link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA832117. All original code has been deposited at Github (https://github.com/dozmorovlab/PDXHiC supplemental)." now reads: "All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/), under accession number GSE201435. The whole genome sequencing data generated in this study have been submitted to the NCBI SRA database (https://www.ncbi.nlm.nih.gov/bioproject/), under accession number PRJNA832117: https://dataview.ncbi.nlm.nih.gov/object/PRJNA832117. [Extracted from the article]