It is well known that cancer is an aggressive disease, often associated with relapse, in many cases due to drug resistance. Cancer stem cell and clonal evolution are frequently causes of innate or acquired drug resistance. Current RNA sequencing technologies do not distinguish gene expression of different cell lineages because they are based on bulk cell studies. Single-cell RNA sequencing technologies and related bioinformatics clustering and differential expression analysis represent a turning point in cancer research. They are emerging as essential tools for dissecting tumors at single-cell resolution and represent novel tools to understand carcinogenesis and drug response. In this review, we will outline the role of these new technologies in addressing cancer heterogeneity and cell lineage-dependent drug resistance.
Rosati, D., Giordano, A. (2022). Single-cell RNA sequencing and bioinformatics as tools to decipher cancer heterogenicity and mechanisms of drug resistance. BIOCHEMICAL PHARMACOLOGY, 195 [10.1016/j.bcp.2021.114811].
Single-cell RNA sequencing and bioinformatics as tools to decipher cancer heterogenicity and mechanisms of drug resistance
Rosati, Diletta;Giordano, Antonio
2022-01-01
Abstract
It is well known that cancer is an aggressive disease, often associated with relapse, in many cases due to drug resistance. Cancer stem cell and clonal evolution are frequently causes of innate or acquired drug resistance. Current RNA sequencing technologies do not distinguish gene expression of different cell lineages because they are based on bulk cell studies. Single-cell RNA sequencing technologies and related bioinformatics clustering and differential expression analysis represent a turning point in cancer research. They are emerging as essential tools for dissecting tumors at single-cell resolution and represent novel tools to understand carcinogenesis and drug response. In this review, we will outline the role of these new technologies in addressing cancer heterogeneity and cell lineage-dependent drug resistance.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1166436