Abstract
We present Beyondcell (https://gitlab.com/bu_cnio/beyondcell/), a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines therapeutic differences among cell populations, and generates a prioritised ranking of the differential sensitivity drugs between chosen conditions to guide drug selection. We performed Beyondcell analysis in four single-cell datasets to validate our score and to demonstrate that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
List of abbreviations
- TCs
- Therapeutic clusters
- TH
- Tumour heterogeneity
- scRNA-seq
- Single-cell RNA sequencing
- CCLE
- Cancer Cell Line Encyclopedia
- GDSC
- Genomics of Drugs Sensitivity in Cancer
- CTRP
- Cancer Therapeutic Response Portal
- LINCS
- Library of Integrated Network-based Cellular Signatures
- PSC
- Drug perturbation signature collection
- SSC
- Drug sensitivity signature collection
- MSigDB
- Molecular Signatures Database
- EMT
- Epithelial-mesenchymal transition
- SP
- Switch Point
- BCS
- Beyondcell score
- DSS
- Drug Specificity Score
- KNN
- k-Nearest Neighbours
- UMAP
- Uniform Manifold Approximation and Projection
- PCA
- Principal Components Analysis
- HSP
- Heat Shock Proteins
- RHPs
- Recurrently heterogeneous programs
- EpiSen
- Epithelial senescence-associated
- ICIs
- Immune Checkpoint Inhibitors
- CDKi
- CDK-inhibitor