A multi-omic single-cell landscape of human gynecologic malignancies

Regner, Matthew J.; Wiśniewska, Kamila; García-Recio, Susana; Thennavan, Aatish; Méndez-Giráldez, Raúl; Malladi, Venkat S.; Hawkins, Gabrielle; Parker, Joel S.; Perou, Charles M.; Bae‐Jump, Victoria L.; Franco, Hector L.

doi:10.1016/j.molcel.2021.10.013

Cited by 50 publications

(71 citation statements)

References 111 publications

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“…From our pool of 12,339 constituent enhancers, we identified 126 super-enhancer regions using the Rank Ordering of Super-Enhancers (ROSE) algorithm (Figure 1G and H, Supplemental Data 1, 2, 3, and 4) 36, 37 . To determine if these BRD4-enriched super-enhancers are relevant to ovarian cancer patients, we leveraged single-cell assay for transposase-accessible chromatin sequencing (ATAC-seq) data generated from HGSOC patients to measure the activity of the super-enhancers within these tumors 38 . We detected the activity (defined by chromatin accessibility) of 121 out of 126 (96%) super-enhancers in the cancer cell fraction of HGSOC patients (Supplemental Figure 1A).…”

Section: Resultsmentioning

confidence: 99%

“…As a final validation experiment, we wanted to determine if SE60 and SE14 were specifically active within the cancer cell compartment of human HGSOC tumors and if their target genes are also active within the same cell type. To test this, we analyzed matched single-cell RNA-seq and single-cell ATAC-seq data from two HGSOC patients previously generated in our lab (Supplemental Figure 9) 38 . We annotated seven distinct cell types present in these tumors by both single-cell RNA-seq and single-cell ATAC-seq and identified the cancer cell population using the FDA approved biomarker CA125 (also known as MUC16 ) (Figure 7A and B) 55 .…”

Section: Resultsmentioning

confidence: 99%

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A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

Kelly

Wiśniewska

Regner

et al. 2022

Preprint

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The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we have utilized an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We have systematically probed the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we constructed chromatin interaction maps that enabled the annotation of direct target genes for these super-enhancers and later confirmed their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach has examined a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

Kelly

Wiśniewska

Regner

et al. 2022

Preprint

Self Cite

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“…A total of 41,515 cells passed our QC criteria for scATAC-seq 22 . For each individual sample, we integrated scATAC-seq and scRNA-seq, using label transfer 23-25 . When both data types were available for any given donor, we used the cluster information obtained from scRNA-seq as a reference and explicitly searched for the best-matched cluster for every single cell in scATAC-seq.…”

Section: Resultsmentioning

confidence: 99%

A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing

Lengyel

Weigert

et al. 2022

Preprint

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As part of the Human Cell Atlas initiative, we generated transcriptomic (scRNA-seq; 86,708 cells) and regulatory (scATAC-seq; 59,118 cells) profiles of the normal postmenopausal ovary and fallopian tube (FT) at single-cell resolution. In the FT, 22 cell clusters integrated into 11 cell types, including ciliated and secretory epithelial cells, while the ovary had 17 distinct cell clusters defining 6 major cell types. The dominant cell type in both the postmenopausal ovary and FT was stromal cells, which expressed several genes associated with aging. The fimbrial end of the FT had a significant number and variety of immune cells and active communication with the ovary; the ovary contained mostly stromal cells but few immune cells. The epithelial cells of the normal FT expressed multiple ovarian cancer risk-associated genes (CCDC170, RND3, TACC2, STK33, and ADGB). By integrating paired single-cell transcriptomics and chromatin accessibility data we found that the regulatory landscape of the fimbriae was markedly different from the isthmus and ampulla. Intriguingly, several cell types in the FT had comparable gene expression but different transcriptional regulations. Our single-cell transcriptional and regulatory maps allowed us to disentangle the complex cellular makeup of the postmenopausal FT and ovary and will contribute to a better understanding of gynecologic diseases in menopause.

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“…To explore whether the gene signature screened by WGCNA was consistent with the cell-specific genes of different cells in OvCa, the single-cell transcriptomes datasets of six ovarian tumor tissue from GSE173682 ( Regner et al, 2021 ) were downloaded. All additional analyses were performed using the Seurat (4.0.4, http://satijalab.org/seurat/ ) R toolkit ( Butler et al, 2018 ), including quality control and all subsequent analyses.…”

Section: Methodsmentioning

confidence: 99%

Identification of a Gene Signature of Cancer-Associated Fibroblasts to Predict Prognosis in Ovarian Cancer

Zeng

Wang

et al. 2022

Front. Genet.

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Ovarian cancer (OvCa) is one of the most widespread malignant tumors, which has the highest morbidity and unsatisfactory clinical outcomes among all gynecological malignancies in the world. Previous studies found that cancer-associated fibroblasts (CAFs) play significant roles in tumor growth, progression, and chemoresistance. In the current research, weighted gene co-expression network analysis (WGCNA), univariable COX regression, and the least absolute shrinkage and selection operator (LASSO) analysis were applied to recognize CAF-specific genes. After multiple bioinformatic analyses, four genes (AXL, GPR176, ITGBL1, and TIMP3) were identified as OvCa-specific CAF markers and used to construct the prognostic signature (CAFRS). Furthermore, the specificity of the four genes' expression was further validated at the single-cell level, which was high-selectively expressed in CAFs. In addition, our results showed that CAFRS is an independent significant risk factor affecting the clinical outcomes of OvCa patients. Meanwhile, patients with higher CAFRS were more likely to establish chemoresistance to platinum. Besides, the CAFRS were notably correlated with well-known signal pathways that were related to tumor progression. In summary, our study identifies four CAF-specific genes and constructs a novel prognostic signature, which may provide more insights into precise prognostic assessment in OvCa.

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A multi-omic single-cell landscape of human gynecologic malignancies

Cited by 50 publications

References 111 publications

A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing

Identification of a Gene Signature of Cancer-Associated Fibroblasts to Predict Prognosis in Ovarian Cancer

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