A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data

Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge...

Full description

Saved in:
Bibliographic Details
Published in:Current opinion in structural biology 2024-02, Vol.84, p.102745, Article 102745
Main Authors: Maeser, Danielle, Zhang, Weijie, Huang, Yingbo, Huang, R. Stephanie
Format: Article
Language:English
Subjects:
Antineoplastic Agents - pharmacology
Drug Discovery
Humans
Neoplasms - drug therapy
Precision Medicine
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.
ISSN:0959-440X
1879-033X
1879-033X
DOI:10.1016/j.sbi.2023.102745