Single-cell transcriptomics identifies gene expression networks driving differentiation and tumorigenesis in the human fallopian tube

Dinh, Huy Q.; Lin, Xianzhi; Abbasi, Forough; Nameki, Robbin; Haro, Marcela; Olingy, Claire; Chang, Heidi; Hernández, Lourdes; Gayther, Simon A.; Wright, Kelly; Aspuria, Paul‐Joseph; Karlan, Beth Y.; Corona, Rosario I.; Li, Andrew; Rimel, B.J.; Siedhoff, Matthew T.; Medeiros, Fabíola; Lawrenson, Kate

doi:10.1016/j.celrep.2021.108978

Cited by 55 publications

(78 citation statements)

References 71 publications

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“…Lawrenson et al [ 97 ] recently investigated the transcriptomic profiles of ovarian surface epithelial cells and fallopian tube secretory epithelial cells and compared them to bulk RNA profiles of HGSTOC, confirming that most of the HGSTOC derived from the latter. Furthermore, Dinh et al [ 98 ] performed single-cell RNA sequencing on fallopian tube specimens of 8 healthy patients and found a population of early secretory cells of which transcriptomic profile was maintained in advanced HGSTOC tumours. Interestingly, a lot of the proposed genes describing this early secretory cell subcluster (e.g.…”

Section: Discussionmentioning

confidence: 99%

High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification

et al. 2021

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Background High-grade serous tubo-ovarian cancer (HGSTOC) is characterised by extensive inter- and intratumour heterogeneity, resulting in persistent therapeutic resistance and poor disease outcome. Molecular subtype classification based on bulk RNA sequencing facilitates a more accurate characterisation of this heterogeneity, but the lack of strong prognostic or predictive correlations with these subtypes currently hinders their clinical implementation. Stromal admixture profoundly affects the prognostic impact of the molecular subtypes, but the contribution of stromal cells to each subtype has poorly been characterised. Increasing the transcriptomic resolution of the molecular subtypes based on single-cell RNA sequencing (scRNA-seq) may provide insights in the prognostic and predictive relevance of these subtypes. Methods We performed scRNA-seq of 18,403 cells unbiasedly collected from 7 treatment-naive HGSTOC tumours. For each phenotypic cluster of tumour or stromal cells, we identified specific transcriptomic markers. We explored which phenotypic clusters correlated with overall survival based on expression of these transcriptomic markers in microarray data of 1467 tumours. By evaluating molecular subtype signatures in single cells, we assessed to what extent a phenotypic cluster of tumour or stromal cells contributes to each molecular subtype. Results We identified 11 cancer and 32 stromal cell phenotypes in HGSTOC tumours. Of these, the relative frequency of myofibroblasts, TGF-β-driven cancer-associated fibroblasts, mesothelial cells and lymphatic endothelial cells predicted poor outcome, while plasma cells correlated with more favourable outcome. Moreover, we identified a clear cell-like transcriptomic signature in cancer cells, which correlated with worse overall survival in HGSTOC patients. Stromal cell phenotypes differed substantially between molecular subtypes. For instance, the mesenchymal, immunoreactive and differentiated signatures were characterised by specific fibroblast, immune cell and myofibroblast/mesothelial cell phenotypes, respectively. Cell phenotypes correlating with poor outcome were enriched in molecular subtypes associated with poor outcome. Conclusions We used scRNA-seq to identify stromal cell phenotypes predicting overall survival in HGSTOC patients. These stromal features explain the association of the molecular subtypes with outcome but also the latter’s weakness of clinical implementation. Stratifying patients based on marker genes specific for these phenotypes represents a promising approach to predict prognosis or response to therapy.

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Section: Discussionmentioning

confidence: 99%

High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification

et al. 2021

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“…35,76 The emergence of singlecell transcriptomic data in cell types relevant to endometriosis and EOC offer further opportunities to explore potential celltype-specific effects on candidate genes. [42][43][44]77 ENOC and CCOC are believed to arise from ectopic (endometriosis-derived) or eutopic endometrial epithelium, while HGSOC is presumed to originate from fallopian tube secretory epithelial cells. 78 Despite the distinct cells of origin, we have previously shown that inherited genetic susceptibility to ENOC/CCOC and HGSOC is, to some extent, shared.…”

Section: Discussionmentioning

confidence: 99%

“…35,76 The emergence of single-cell transcriptomic data in cell types relevant to endometriosis and EOC offer further opportunities to explore potential cell-type-specific effects on candidate genes. 42 , 43 , 44 , 77 …”

Section: Discussionmentioning

confidence: 99%

A multi-level investigation of the genetic relationship between endometriosis and ovarian cancer histotypes

Mortlock

Fuente

Kho

et al. 2022

Cell Reports Medicine

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“…Gene expression normalization and cell clustering was done using the SCTransform pipeline (21) with percent mitochondrial reads regressed out and person specific batch effects corrected using Harmony (22). Identification of cell clusters was done using known marker genes (Additional File 2: Table S1) (23)(24)(25)(26)(27)(28)(29)(30) with differential gene expression calculated using a Wilcoxon rank sum test. Enrichment of cell clusters of specific phenotypes was done using MASC (mixed-effects modeling of associations of single cells) (https://github.com/immunogenomics/masc), which essentially uses a percentage of cells per cluster while also taking into account technical covariates; 10x library batch, preparation (whole ME or ME-tissue), nUMI per cell, percent mitochondrial reads and phase are accounted for.…”

Section: (Mean ± Standard Deviation [Sd]mentioning

confidence: 99%

Single cell analysis of menstrual endometrial tissues defines phenotypes associated with endometriosis

Shih

Adelson

Vashistha

et al. 2022

Preprint

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Background. Endometriosis is a common, complex disorder which is under-recognized and subject to prolong delays in diagnosis. It is accompanied by significant changes in the eutopic endometrial lining. Methods. We have undertaken the first single cell RNA-sequencing (scRNA-Seq) comparison of endometrial tissues in freshly collected menstrual effluent (ME) from 33 subjects, including confirmed endometriosis patients (cases) and controls as well as symptomatic subjects. Results. We identify a unique subcluster of proliferating uterine natural killer (uNK) cells in ME-tissues from controls that is almost absent from endometriosis cases, along with a striking reduction of total uNK cells in the ME of cases (p<10-16). In addition, IGFBP1+ decidualized subset of stromal cells are abundant in the shed endometrium of controls when compared to cases (p<10-16) confirming findings of compromised decidualization of cultured stromal cells from cases. By contrast, endometrial stromal cells from cases are enriched in cells expressing a pro-inflammatory phenotype. An enrichment of B cells in the cases (p=5.8 x 10-6) raises the possibility in some subjects of chronic endometritis, a disorder which predisposes to endometriosis. Conclusions. We propose that characterization of endometrial tissues in ME will provide an effective screening tool for identifying endometriosis in patients with chronic symptoms suggestive of this disorder. This constitutes a major advance, since delayed diagnosis for many years is a major clinical problem in the evaluation of these patients. Comprehensive analysis of ME is expected to lead to new diagnostic and therapeutic approaches to endometriosis and other associated reproductive disorders such as female infertility.

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Single-cell transcriptomics identifies gene expression networks driving differentiation and tumorigenesis in the human fallopian tube

Cited by 55 publications

References 71 publications

High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification

High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification

A multi-level investigation of the genetic relationship between endometriosis and ovarian cancer histotypes

Single cell analysis of menstrual endometrial tissues defines phenotypes associated with endometriosis

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