bioRxivJClub
Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures
bioRxiv · · Preprint · Open access
Osorio, Shahrouzi + 3 more
Abstract ↗AI summary
The abstract is read at the publisher; the summary is JClub's.
Retriever identifies QL-XII-47 plus GSK-690693 as the topmost promising drug combination for antagonizing triple-negative breast cancer (TNBC) cell line transcriptional signatures, after testing 11,325 combinations.
- Why it matters: Matching disease-specific transcriptional signatures to large drug-response datasets is difficult, and better computational links are needed to target TNBC-relevant tumor states and subpopulations.
- What they did: The tool extracts robust TNBC-specific drug response profiles from LINCS-L1000 cellular responses to hundreds of compounds, then combines these with a single-cell RNA-seq breast cancer atlas to predict antagonistic drug pairs.
- The result: Systematic evaluation of 151 profiles and 11,325 combinations yields QL-XII-47 and GSK-690693 as the leading candidate, enabling signature- and cell-type–targeted drug combination discovery for TNBC.
The findingWhy it mattersWhat they didThe result
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- Open access
- 3 cites