Single-cell sequencing reveals the heterogeneity of pancreatic neuroendocrine tumors under the pattern of genomic instability and histological grading (A30)

Introduction: Non-functional pancreatic neuroendocrine tumor (NF-Pan-NET) is a kind of highly heterogeneous tumors. Histological grading is the key factors affecting the prognosis of patients and instructive in guiding treatment and assessing recurrence in NF-Pan-NET. 90% of NF-Pan-NET are grade G1 or G2, leading to an unpredictable clinical course that varies from indolent to highly malignant. Approximately one-third of patients without CNV alteration and the prognosis of these patients are better than that of patients with CNV alteration. Therefore, probing the heterogeneity of tumors based on histological grading and CNV variation is crucial.

Aim(s): We aimed to explore the heterogeneity in NF-Pan-NET with different histological grading and genomic instability patterns, in addition to identifying the characteristics of patients with liver metastases.

Materials and methods: We recruited 17 NF-Pan-NET and analyzed their single-cell RNA-sequence (scRNA-seq), whole exome sequencing (WES), bulk RNA-sequencing, proteomics and phospho-proteomics to decode the malignant potential of NF-Pan-NET.

Results: We found that inferCNV, which is widely used to identify malignant cells in other cancers, had significant deficiencies in identifying tumor cells in scRNA data of NF-Pan-NET. In additon, genome instability was better than histological grading in subtyping tumor cells. We illustrated the differences of tumor cells under different genome instability patterns, and the differences of tumor immune microenvironment between genome instability and histological grading classification.

Conclusion: We explored the heterogeneity in NF-Pan-NET of different histological grading and genome instability patterns as well as identified the characteristics of patients with liver metastases.

Keywords: pancreatic neuroendocrine tumor, single-cell sequencing, genomic instability, histological grading