DRMref: comprehensive reference map of drug resistance mechanisms in human cancer.
Xiaona LiuJiahao YiTina LiJianguo WenKexin HuangJiajia LiuGrant WangPora KimQianqian SongXiaobo ZhouPublished in: Nucleic acids research (2023)
Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.
Keyphrases
- single cell
- rna seq
- induced apoptosis
- combination therapy
- high throughput
- papillary thyroid
- cell cycle arrest
- epithelial mesenchymal transition
- adverse drug
- genome wide
- squamous cell
- endothelial cells
- signaling pathway
- drug induced
- transcription factor
- ejection fraction
- genome wide identification
- cell death
- endoplasmic reticulum stress
- systematic review
- newly diagnosed
- emergency department
- dna methylation
- squamous cell carcinoma
- electronic health record
- radiation therapy
- prognostic factors
- transforming growth factor
- stem cells
- cell therapy
- chronic kidney disease
- artificial intelligence
- mesenchymal stem cells
- locally advanced
- bone marrow
- deep learning