Application of single-cell sequencing technologies in pancreatic cancer

Mol Cell Biochem. 2021 Jun;476(6):2429-2437. doi: 10.1007/s11010-021-04095-4. Epub 2021 Feb 18.

Abstract

Pancreatic cancer (PC) is the third lethal disease for cancer-related mortalities globally. This is mainly because of the aggressive nature and heterogeneity of the disease that is diagnosed only in their advanced stages. Thus, it is challenging for researchers and clinicians to study the molecular mechanism involved in the development of this aggressive disease. The single-cell sequencing technology enables researchers to study each and every individual cell in a single tumor. It can be used to detect genome, transcriptome, and multi-omics of single cells. The current single-cell sequencing technology is now becoming an important tool for the biological analysis of cells, to find evolutionary relationship between multiple cells and unmask the heterogeneity present in the tumor cells. Moreover, its sensitivity nature is found progressive enabling to detect rare cancer cells, circulating tumor cells, metastatic cells, and analyze the intratumor heterogeneity. Furthermore, these single-cell sequencing technologies also promoted personalized treatment strategies and next-generation sequencing to predict the disease. In this review, we have focused on the applications of single-cell sequencing technology in identifying cancer-associated cells like cancer-associated fibroblast via detecting circulating tumor cells. We also included advanced technologies involved in single-cell sequencing and their advantages. The future research indeed brings the single-cell sequencing into the clinical arena and thus could be beneficial for diagnosis and therapy of PC patients.

Keywords: Circulating tumor cells; Intratumor heterogeneity; Metastasis; Pancreatic cancer; Single-cell sequencing; Transcriptome.

Publication types

  • Review

MeSH terms

  • Humans
  • Pancreatic Neoplasms / diagnosis
  • Pancreatic Neoplasms / genetics*
  • Pancreatic Neoplasms / metabolism
  • Sequence Analysis*
  • Single-Cell Analysis*
  • Transcriptome*