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Comparative single-cell RNA sequencing (scRNA-seq) reveals liver metastasis-specific targets in a patient with small intestinal neuroendocrine cancer.

ABSTRACT: Genomic analysis of a patient's tumor is the cornerstone of precision oncology, but it does not address whether metastases should be treated differently. Here we tested whether comparative single-cell RNA sequencing (scRNA-seq) of a primary small intestinal neuroendocrine tumor to a matched liver metastasis could guide the treatment of a patient's metastatic disease. Following surgery, the patient was put on maintenance treatment with a somatostatin analog. However, the scRNA-seq analysis revealed that the neuroendocrine epithelial cells in the liver metastasis were less differentiated and expressed relatively little SSTR2, the predominant somatostatin receptor. There were also differences in the tumor microenvironments. RNA expression of vascular endothelial growth factors was higher in the primary tumor cells, reflected by an increased number of endothelial cells. Interestingly, vascular expression of the major VEGF receptors was considerably higher in the liver metastasis, indicating that the metastatic vasculature may be primed for expansion and susceptible to treatment with angiogenesis inhibitors. The patient eventually progressed on Sandostatin, and although consideration was given to adding an angiogenesis inhibitor to her regimen, her disease progression involved non-liver metastases that had not been characterized. Although in this specific case comparative scRNA-seq did not alter treatment, its potential to help guide therapy of metastatic disease was clearly demonstrated.

SUBMITTER: Rao M

PROVIDER: S-EPMC7133744 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Comparative single-cell RNA sequencing (scRNA-seq) reveals liver metastasis-specific targets in a patient with small intestinal neuroendocrine cancer.

Rao Manisha M Oh Ki K Moffitt Richard R Thompson Patricia P Li Jinyu J Liu Jingxuan J Sasson Aaron A Georgakis George G Kim Joseph J Choi Minsig M Powers Scott S

Cold Spring Harbor molecular case studies 20200401 2

Genomic analysis of a patient's tumor is the cornerstone of precision oncology, but it does not address whether metastases should be treated differently. Here we tested whether comparative single-cell RNA sequencing (scRNA-seq) of a primary small intestinal neuroendocrine tumor to a matched liver metastasis could guide the treatment of a patient's metastatic disease. Following surgery, the patient was put on maintenance treatment with a somatostatin analog. However, the scRNA-seq analysis reveal ...[more]

PMID: 32054662

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Project description:ObjectiveOvarian cancer has the highest mortality rate among gynecological malignant tumors, and it preferentially metastasizes to omental tissue, leading to intestinal obstruction and death. scRNA-seq is a powerful technique to reveal tumor heterogeneity. Analyzing omentum metastasis of ovarian cancer at the single-cell level may be more conducive to exploring and understanding omentum metastasis and prognosis of ovarian cancer at the cellular function and genetic levels.MethodsThe omentum metastasis site scRNA-seq data of GSE147082 were acquired from the GEO (Gene Expression Omnibus) database, and single cells were clustered by the Seruat package and annotated by the SingleR package. Cell differentiation trajectories were reconstructed through the monocle package. The ovarian cancer microarray data of GSE132342 were downloaded from GEO and were clustered by using the ConsensusClusterPlus package into omentum metastasis-associated clusters according to the marker genes gained from single-cell differentiation trajectory analysis. The tumor microenvironment (TME) and immune infiltration differences between clusters were analyzed by the estimate and CIBERSORT packages. The expression matrix of genes used to cluster GSE132342 patients was extracted from bulk RNA-seq data of TCGA-OV (The Cancer Genome Atlas ovarian cancer), and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to establish an omentum metastasis-associated gene (OMAG) signature. The signature was then tested by GSE132342 data. Finally, the clinicopathological characteristics of TCGA-OV were screened by univariate and multivariate Cox regression analysis to draw the nomogram.ResultsA total of 9885 cells from 6 patients were clustered into 18 cell clusters and annotated into 14 cell types. Reconstruction of differentiation trajectories divided the cells into 5 branches, and a total of 781 cell trajectory-related characteristic genes were obtained. A total of 3769 patients in GSE132342 were subtyped into 3 clusters by 74 cell trajectory-related characteristic genes. Kaplan-Meier (K-M) survival analysis showed that the prognosis of cluster 2 was the worst, P < 0.001. The TME analysis showed that the ESTIMATE score and stromal score in cluster 2 were significantly higher than those in the other two clusters, P < 0.001. The immune infiltration analysis showed differences in the fraction of 8 immune cells among the 3 clusters, P < 0.05. The expression data of 74 genes used for GEO clustering were extracted from 379 patients in TCGA-OV, and combined with survival information, 10 candidates for OMAGs were filtered by LASSO. By using multivariate Cox regression, the 6-OMAGs signature was established as RiskScore = 0.307*TIMP3 + 3.516*FBN1-0.109*IGKC + 0.209*RPL21 + 0.870*UCHL1 + 0.365*RARRES1. Taking TCGA-OV as the training set and GSE132342 as the test set, receiver operating characteristic (ROC) curves were drawn to verify the prognostic value of 6-OMAGs. Screened by univariate and multivariate Cox regression analysis, 3 (age, cancer status, primary therapy outcome) of 5 clinicopathological characteristics were used to construct the nomogram combined with risk score.ConclusionWe constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.

Dataset Information

Comparative single-cell RNA sequencing (scRNA-seq) reveals liver metastasis-specific targets in a patient with small intestinal neuroendocrine cancer.

Publications

Comparative single-cell RNA sequencing (scRNA-seq) reveals liver metastasis-specific targets in a patient with small intestinal neuroendocrine cancer.

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