Computational deconvolution of drug sensitivity via single-cell-to-bulk transcriptome mapping.

CLIMB and CLIFF accurately deconvolve bulk RNA-seq data into cell-type and drug sensitivity profiles, outperforming existing methods in multiple cancer contexts.

• Why it matters: Understanding cell-specific signals in heterogeneous tissues is crucial for interpreting biological processes and improving drug development, but bulk measurements obscure these details.
• What they did: The authors developed CLIMB to infer cellular composition and CLIFF to estimate cell subtype- and single-cell-specific drug responses, validating both with in-vitro mixtures and large-scale pseudo-bulk analyses across five cancer types.
• The result: Applying their framework to the beatAML dataset, they generated a cell subtype-resolved drug sensitivity landscape for 121 compounds, identifying multiple agents potentially active against leukemic stem cells, advancing precision medicine.