Dataset Information

Sequencing of cancer cell subpopulations identifies micrometastases in a bladder cancer patient.

ABSTRACT:

Purpose

Pathologic staging of bladder cancer patients remains a challenge. Standard-of-care histology exhibits limited sensitivity in detection of micrometastases, which can increase risk of cancer progression and delay potential adjuvant therapies. Here, we sought to develop a proof of concept novel molecular approach to improve detection of cancer micrometastasis.

Experimental design

We combined fluorescence activated cell sorting and next-generation sequencing and performed whole-exome sequencing of total cancer cells and cancer cell subpopulations in multiple tumor specimens and regional lymph nodes in a single patient with muscle-invasive urothelial carcinoma of the bladder following radical cystectomy.

Results

Mean allele frequency analysis demonstrated a significant correlation between primary tumor cancer cells and cancer cells isolated from the lymph nodes, confirming lymph node disease despite negative pathologic staging. RNA-sequencing revealed intratumoral heterogeneity as well as enrichment for immune system and lipid metabolism gene sets in the micrometastatic cancer cell subpopulations.

Conclusions

Our analysis illustrates how next-generation sequencing of cancer cell subpopulations may be utilized to enrich for cancer cell markers and enhance detection of bladder cancer micrometastases to improve pathologic staging and provide insight into cancer cell biology.

SUBMITTER: Prado K

PROVIDER: S-EPMC5542213 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

Sequencing of cancer cell subpopulations identifies micrometastases in a bladder cancer patient.

Prado Kris K Zhang Kelvin X KX Pellegrini Matteo M Chin Arnold I AI

Oncotarget 20170701 28

<h4>Purpose</h4>Pathologic staging of bladder cancer patients remains a challenge. Standard-of-care histology exhibits limited sensitivity in detection of micrometastases, which can increase risk of cancer progression and delay potential adjuvant therapies. Here, we sought to develop a proof of concept novel molecular approach to improve detection of cancer micrometastasis.<h4>Experimental design</h4>We combined fluorescence activated cell sorting and next-generation sequencing and performed who ...[more]

PMID: 28487492

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Project description:Aims/hypothesisIslets have complex heterogeneity and subpopulations. Cell surface markers representing alpha, beta and delta cell subpopulations are urgently needed for investigations to explore the compositional changes of each subpopulation in obesity progress and diabetes onset, and the adaptation mechanism of islet metabolism induced by a high-fat diet (HFD).MethodsSingle-cell RNA sequencing (scRNA-seq) was applied to identify alpha, beta and delta cell subpopulation markers in an HFD-induced mouse model of glucose intolerance. Flow cytometry and immunostaining were used to sort and assess the proportion of each subpopulation. Single-cell proteomics was performed on sorted cells, and the functional status of each alpha, beta and delta cell subpopulation in glucose intolerance was deeply elucidated based on protein expression.ResultsA total of 33,999 cells were analysed by scRNA-seq and clustered into eight populations, including alpha, beta and delta cells. For alpha cells, scRNA-seq revealed that the Ace2low subpopulation had downregulated expression of genes related to alpha cell function and upregulated expression of genes associated with beta cell characteristics in comparison with the Ace2high subpopulation. The impaired function and increased fragility of ACE2low alpha cells exposure to HFD was further suggested by single-cell proteomics. As for beta cells, the CD81high subpopulation may indicate an immature signature of beta cells compared with the CD81low subpopulation, which had robust function. We also found differential expression of Slc2a2 in delta cells and a potentially stronger cellular function and metabolism in GLUT2low delta cells than GLUT2high delta cells. Moreover, an increased proportion of ACE2low alpha cells and CD81low beta cells, with a constant proportion of GLUT2low delta cells, were observed in HFD-induced glucose intolerance.Conclusions/interpretationWe identified ACE2, CD81 and GLUT2 as surface markers to distinguish, respectively, alpha, beta and delta cell subpopulations with heterogeneous maturation and function. The changes in the proportion and functional status of islet endocrine subpopulations reflect the metabolic adaptation of islets to high-fat stress, which weakened the function of alpha cells and enhanced the function of beta and delta cells to bring about glycaemic homeostasis. Our findings provide a fundamental resource for exploring the mechanisms maintaining each islet endocrine subpopulation's fate and function in health and disease.Data availabilityThe scRNA-seq analysis datasets from the current study are available in the Gene Expression Omnibus (GEO) repository under the accession number GSE203376.

Project description:ObjectiveOvarian cancer is the deadliest gynaecological cancer globally. In our study, we aimed to analyze specific cell subpopulations and marker genes among ovarian cancer cells by single-cell RNA sequencing (RNA-seq).MethodsSingle-cell RNA-seq data of 66 high-grade serous ovarian cancer cells were employed from the Gene Expression Omnibus (GEO). Using the Seurat package, we performed quality control to remove cells with low quality. After normalization, we detected highly variable genes across the single cells. Then, principal component analysis (PCA) and cell clustering were performed. The marker genes in different cell clusters were detected. A total of 568 ovarian cancer samples and 8 normal ovarian samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes were identified according to ∣log2fold change (FC) | >1 and adjusted p value <0.05. To explore potential biological processes and pathways, functional enrichment analyses were performed. Furthermore, survival analyses of differentially expressed marker genes were performed.ResultsAfter normalization, 6000 highly variable genes were identified across the single cells. The cells were divided into 3 cell populations, including G1, G2M, and S cell cycles. A total of 1,124 differentially expressed genes were identified in ovarian cancer samples. These differentially expressed genes were enriched in several pathways associated with cancer, such as metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway. Furthermore, marker genes, STAT1, ANP32E, GPRC5A, and EGFL6, were highly expressed in ovarian cancer, while PMP22, FBXO21, and CYB5R3 were lowly expressed in ovarian cancer. These marker genes were positively associated with prognosis of ovarian cancer.ConclusionOur findings revealed specific cell subpopulations and marker genes in ovarian cancer using single-cell RNA-seq, which provided a novel insight into the heterogeneity of ovarian cancer.

Dataset Information

Sequencing of cancer cell subpopulations identifies micrometastases in a bladder cancer patient.

Purpose

Experimental design

Results

Conclusions

Publications

Sequencing of cancer cell subpopulations identifies micrometastases in a bladder cancer patient.

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