Abstract 158P
Background
Single-cell RNA sequencing (scRNA-seq) has the ability to unveil uncommon cell populations. However, due to the high demand for tissue quality and cell viability, currently most scRNA-seq for pancreatic cancer was performed by surgical specimen or biopsy from metastatic sites. This study aims to establish a practical experience to help scientists perform primary pancreatic tumor scRNA-seq using EUS-FNB samples in real-world practice.
Methods
Two punctures from the same lesion using the same needle, without applying suction (Non-suction group) and with a negative pressure of 5 ml (Suction group) were evaluated. Single cell RNA sequencing libraries were prepared with Chromium Single cell 5’ Reagent Kits v2 (10X Genomics, USA) following the manufacturer’s protocol.
Results
A total of 20 patients were enrolled. The median age was 65.1 years old (range 46.6-83.2). Suction group achieved a cell preparation success rate of 80% (16/20) which was significantly higher than the 10% (2/20) success rate in the non-suction group (p<0.001). After the establishment of cell preparation protocol, we generate single-cell RNA transcriptomes for four patients, including two early stage (9,632 cells) and two late stage (4,592 cells). After quality control, 11,950 single cells were feasible for downstream analysis. Overall, 66% of cells (7,842) belonged to early stage and 34% (4,108) belonged to late stage. 12 major cell subtypes were identified across early and late stage. The proportion of cancer cells cluster-4 was significantly higher in late stage. Differentially expressed genes analysis showed UBE2C is the most highly expressed gene in cancer cells cluster-4. As external validation, in TCGA PAAD dataset, we found UBE2C high expression pancreatic cancer had significantly poor survival.
Conclusions
EUS-FNB with a negative pressure of 5 ml is feasible for single-cell sequencing in daily practice. A UBE2C high-expression subclone exists in early-stage pancreatic cancer and correlates with poor prognosis, potentially becoming a new therapeutic target in future studies.
Clinical trial identification
Protocol number: NCT05767697 Release date: 02 March, 2023.
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
National Health Research Institutes.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
5P - Clinicopathologic features and genomic profiling of occult breast cancer
Presenter: Liansha Tang
Session: Poster Display
Resources:
Abstract
6P - Tumor cell-released autophagosomes (TRAPs) promote lung metastasis through inducing PD-L1 high expression of pulmonary vascular endothelial cells (PVECs) in breast cancer
Presenter: Xuru Wang
Session: Poster Display
Resources:
Abstract
7P - Tumor cell-released autophagosomes (TRAPs) promote breast cancer lung metastasis by modulating neutrophil extracellular traps formation
Presenter: Xiaohe Zhou
Session: Poster Display
Resources:
Abstract
9P - Clinicopathological features and prognosis of mucinous breast cancer: A retrospective analysis of 358 patients in Vietnam
Presenter: Hoai Hoang
Session: Poster Display
Resources:
Abstract
10P - Comparison of 28-gene and 70-gene panel in risk-prediction of Chinese women with early-stage HR-positive and HER2-negative breast cancer
Presenter: Lei Lei
Session: Poster Display
Resources:
Abstract
11P - Multimodal analysis of methylation and fragmentomic profiles in plasma cell-free DNA for differentiation of benign and malignant breast tumors
Presenter: Hanh Nguyen
Session: Poster Display
Resources:
Abstract
12P - Plasma cell-free mRNA profiles enable early detection of breast cancer
Presenter: Chi Nguyen
Session: Poster Display
Resources:
Abstract
13P - Relationship of distress and quality of life with gut microbiome composition in newly diagnosed breast cancer patients: A prospective, observational study
Presenter: Chi-Chan Lee
Session: Poster Display
Resources:
Abstract
14P - Classification of molecular subtypes of breast cancer in whole-slide histopathological images using a novel deep learning algorithm
Presenter: Hyung Suk Kim
Session: Poster Display
Resources:
Abstract
15P - The regulation of pregnenolone in breast cancer
Presenter: Hyeon-Gu Kang
Session: Poster Display
Resources:
Abstract