Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures
Osorio, D.; Shahrouzi, P.; Tekpli, X.; Kristensen, V. N.; Kuijjer, M. L.
Show abstract
Developing novel cancer treatments is a challenging task that can benefit from computational techniques matching transcriptional signatures to large-scale drug response data. Here, we present retriever, a tool that extracts robust disease-specific transcrip-tional drug response profiles based on cellular response profiles to hundreds of compounds from the LINCS-L1000 project. We used retriever to extract transcriptional drug response signatures of triple-negative breast cancer (TNBC) cell lines and combined these with a single-cell RNA-seq breast cancer atlas to predict drug combinations that antagonize TNBC-specific disease signatures. After systematically testing 152 drug response profiles and 11,476 drug combinations, we identified the combination of kinase inhibitors QL-XII-47 and GSK-690693 as the topmost promising candidate for TNBC treatment. Our new computational approach allows the identification of drugs and drug combinations targeting specific tumor cell types and subpopulations in individual patients. It is, therefore, highly suitable for the development of new personalized cancer treatment strategies.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response 97%
- SLAYER: A Computational Framework for Identifying Synthetic Lethal Interactions through Integrated Analysis of Cancer Dependencies 95%
- Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer 95%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatments and receptor subtypes 94%
- Drug-tolerant idling melanoma cells exhibit theory-predicted metabolic low-low phenotype 94%
- Modulated TRPC1 expression predicts sensitivity of breast cancer to doxorubicin and magnetic field therapy: segue towards a precision medicine approach. 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.