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Single-cell sequencing reveals the heterogeneity of pancreatic neuroendocrine tumors under the pattern of genomic instability and histological grading

ABSTRACT: Histological grading is the key factors affecting the prognosis of patients and instructive in guiding treatment and assessing recurrence in non-functional pancreatic neuroendocrine tumor (NF-Pan-NET). Approximately one-third of patients without copy number variation (CNV) alteration and the prognosis of these patients are better than that of patients with CNV alteration. Tumor classification based on CNV also showed significant value in evaluating prognosis. However, the difference between CNV and histological grading is unclear. Here, at single cell level, we analyzed the heterogeneity of tumor cells according to two classification criteria, genomic instability (including CNV alteration and tumor mutation burden) and histological grading. We found that the classification basis on genomic instability of the bulk tissues was better than histological grading in distinguishing tumor cells at scRNA-seq. We revealed that the activated core pathways of tumor cells were significantly different under different histological gradings and genomic instability patterns. In particular, patients with liver metastases had specific activation pathways. Deciphering the differences of the tumor microenvironment through single-cell sequencing, we found that tip cells, lymphatic endothelial cells, macrophages, CD1A+ dendritic cell, Treg, MAIT, ILC and CAFs might patriciate in the process of hepatic metastases, which will facilitate the understanding of the patterns to decode the malignant potential and of NF-Pan-NET.

ORGANISM(S): Homo sapiens

PROVIDER: GSE256136 | GEO | 2024/02/27

REPOSITORIES: GEO

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Project description:[original title] Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. In the present investigation we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1, in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data shows that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathological data it was possible to distinguish two molecular subtypes of Ta and T1 tumors, respectively. In addition, we define gene signatures validated in two independent data sets that classify urothelial carcinoma into low (G1/G2) and high grade (G3) tumors as well as non-muscle and muscle-invasive tumors with high precisions and sensitivities, suggesting molecular grading as a relevant complement to standard pathological grading. We also present a gene expression signature with independent prognostic impact on metastasis and disease specific survival. We conclude that the combination of molecular and histopathological classification systems may provide a strong improvement for bladder cancer classification and produce new insights into the development of this tumor type. 144 bladder cancer tumor samples and 12 normal samples were analyzed on 2-color cDNA or oligo microarrays using the Stratagene Universal Human Reference RNA as the common reference sample. 24 samples are hybridized to both the cDNA and oligo platform and these were used for merging of data from the two different gene expression platforms into a single expression matrix and for subsequent evaluation steps. The merged gene expression matrix used for analyses is supplied as a supplementary file (at the foot of this record). 103 of the samples were also analyzed on a BAC array containing ~32 000 BAC clones. Arrays were produced at the Swegene Centre for Integrative Biology at Lund University (SCIBLU).

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Single-cell sequencing reveals the heterogeneity of pancreatic neuroendocrine tumors under the pattern of genomic instability and histological grading

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