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A review of computational methods for predicting cancer drug response at the single-cell level through integration
Danielle Maeser1, Weijie Zhang1, Yingbo Huang2
1Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States; Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States.
Abstract:
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.
Insights
Tumor heterogeneity drives cancer therapy resistance. New methods using large-scale drug screens on single-cell RNA sequencing (scRNA-seq) data can identify drug sensitivities, paving the way for precision cancer medicine.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Tumor heterogeneity, the presence of diverse malignant cell clones, is a major cause of cancer treatment failure and therapy resistance.
- Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of this cellular diversity.
- Translating scRNA-seq findings into effective treatment strategies remains a significant challenge.
Purpose of the Study:
- To review methods that integrate large-scale drug screening data with scRNA-seq.
- To highlight the potential of these methods in addressing therapy resistance.
- To discuss their role in advancing precision medicine for cancer patients.
Main Methods:
- Surveying existing methodologies for large-scale drug screens.
- Analyzing how these methods can be applied to scRNA-seq data to determine cellular drug sensitivities.
- Focusing on techniques that enable efficient drug screening within complex tumor cell populations.
Main Results:
- Identification of methods that effectively link drug screening data with scRNA-seq profiles.
- Demonstration of these methods' capability to uncover drug sensitivities specific to distinct cancer cell groups.
- Establishment of a foundation for drug discovery tailored to tumor heterogeneity.
Conclusions:
- Methods combining drug screens and scRNA-seq are crucial for overcoming therapy resistance.
- These approaches are foundational for developing targeted therapies for specific cancer cell clones.
- The integration of these methods promises to revolutionize patient care in the era of heterogeneity-aware precision medicine.
